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1
Cao, Ruyin, Yan Feng, Xilong Liu, Miaogen Shen, and Ji Zhou. "Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes." Remote Sensing 12, no. 1 (January 5, 2020): 190. http://dx.doi.org/10.3390/rs12010190.
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Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.
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Prasetyo, Sri Yulianto Joko, Wiwin Sulistyo, Prihanto Ngesti Basuki, Kristoko Dwi Hartomo, and Bistok Hasiholan. "Computer model of Tsunami vulnerability using machine learning and multispectral satellite imagery." Bulletin of Electrical Engineering and Informatics 11, no. 2 (April 1, 2022): 986–97. http://dx.doi.org/10.11591/eei.v11i2.3372.
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This research aims to develop a tsunami vulnerability assessment model on land use and land cover using information on NDVI, NDWI, MDWI, MSAVI, and NDBI extracted from sentinel 2 A and ASTER satellite images. The optimization model using algorithms LASSO and linear regression. The validation test is MSE, ME, RMSE and MAE which show that the linear regression has a higher accuracy than the LASSO. The NDWI interpolation values are 0.00 - (-0.35) and MNDWI interpolation values are 0.00 - (-0.40) which are interpreted as the presence of water surfaces along a coast. MSAVI are values (-0.20) - (-0.35) which are interpreted as the presence of no vegetation. The NDBI interpolation values are values 0.15-0.20 which are interpreted as the presence of built-up lands with social and economic activities. While the NDVI interpolation values are 0.20-0.30 which are interpreted as the presence of vegetation densities, biomass growths from the photosynthesis process, and moderate to low levels of vegetation health. The digital elevation model ASTER analysis shows that all areas with high socioeconomic activities, low NDVI, high NDWI/MDWI, high MSAVI and high NDBI are in areas with low elevation (10 meters) so they have a high vulnerability to tsunami waves.
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Xulu, Sifiso, Kabir Peerbhay, Michael Gebreslasie, and Riyad Ismail. "Drought Influence on Forest Plantations in Zululand, South Africa, Using MODIS Time Series and Climate Data." Forests 9, no. 9 (August 30, 2018): 528. http://dx.doi.org/10.3390/f9090528.
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South Africa has a long history of recurrent droughts that have adversely affected its economic performance. The recent 2015 drought has been declared the most serious in 26 years and impaired key agricultural sectors including the forestry sector. Research on the forests’ responses to drought is therefore essential for management planning and monitoring. The effects of the latest drought on the forests in South Africa have not been studied and are uncertain. The study reported here addresses this gap by using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) and precipitation data retrieved and processed using the JavaScript code editor in the Google Earth Engine (GEE) and the corresponding normalized difference infrared index (NDII), Palmer drought severity index (PDSI), and El Niño time series data for KwaMbonambi, northern Zululand, between 2002 and 2016. The NDVI and NDII time series were decomposed using the Breaks for Additive Seasonal and Trend (BFAST) method to establish the trend and seasonal variation. Multiple linear regression and Mann–Kendall tests were applied to determine the association of the NDVI and NDII with the climate variables. Plantation trees displayed high NDVI values (0.74–0.78) from 2002 to 2013; then, they decreased sharply to 0.64 in 2015. The Mann–Kendall trend test confirmed a negative significant (p = 0.000353) trend between 2014 and 2015. This pattern was associated with a precipitation deficit and low NDII values during a strong El Niño phase. The PDSI (−2.6) values indicated severe drought conditions. The greening decreased in 2015, with some forest remnants showing resistance, implying that the tree species had varying sensitivity to drought. We found that the plantation trees suffered drought stress during 2015, although it seems that the trees began to recover, as the NDVI signals rose in 2016. Overall, these results demonstrated the effective use of the NDVI- and NDII-derived MODIS data coupled with climatic variables to provide insights into the influence of drought on plantation trees in the study area.
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Das, Susanta, Samanpreet Kaur, and Antarpreet Jutla. "Earth Observations Based Assessment of Impact of COVID-19 Lockdown on Surface Water Quality of Buddha Nala, Punjab, India." Water 13, no. 10 (May 14, 2021): 1363. http://dx.doi.org/10.3390/w13101363.
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The novel coronavirus disease (COVID-19) halted almost all the industrial scale anthropogenic activities across the globe, resulting in improvements in water and air quality of megacities. Here, using Sentinel-2A data, we quantified impact of COVID-19 lockdown on the water quality parameters in one of the largest perennial creeks i.e., the Buddha Nala located in District Ludhiana in India. This creek has long been considered as a dumping ground for industrial wastes and has resulted in surface and ground water pollution in the entire lower Indus Basin. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Chlorophyll Index (NDCI), Nitrogen Content Index (NI), Normalized Difference Turbidity Index (NDTI), and Total Suspended Matter (TSM) were compared prior (2019) and during (2020) lockdown in the creek. There was a significant enhancement in NDVI, NDWI, NDCI, and NI values, and reduction in NDTI and TSM values during the lockdown period. When compared with prior year (2019), the values of indices suggested an improvement in water quality and an indicative change in aquatic ecology in the creek. The impact of the COVID-19 lockdown on the improvement in water quality of Buddha Nala was more evident in the upstream and downstream sections than the middle section. This is intriguing since the middle section of the creek was continually impacted by domestic household effluents. The earth observation inspired methodology employed and findings are testament to the discriminatory power to employ remote sensing data and to develop protocols to monitor water quality in regions where routine surveillance of water remains cost prohibitive.
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Mahesti, Triloka, Kristoko Dwi Hartomo, and Sri Yulianto Joko Prasetyo. "Penerapan Algoritma Random Forest dalam Menganalisa Perubahan Suhu Permukaan Wilayah Kota Salatiga." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 4 (October 25, 2022): 2074. http://dx.doi.org/10.30865/mib.v6i4.4603.
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The population increase in Salatiga city is growing rapidly from 2010 to 2020. This change affects the area with vegetation cover, increasing building density and increasing land surface temperatures. The rising of land surface temperature can affect climate change, air quality, human health quality and energy usage. The purpose of this research is to find out the effect of the area with built-up land and area with vegetation cover to land surface temperature by exploring the values of NDVI, NDBI, LST and Albedo. This research shows that the NDVI value has decreased while the NDBI, LST and Albedo values have increased from 2014 to 2021. The values of NDVI, NDBI and Albedo are the components used as validation of the value of the land surface temperature (LST) change in the study area. The results of the correlation between indices show that the highest correlation occurs between NDVI and NDBI with a value of -0.979 which has a negative correlation because vegetation density is always inversely proportional to the density of built up land. The classification results show that there are 7 villages in Salatiga City with high temperature increases, the villages name are Cebongan, Mangunsari, Ledok, Kutowinangun Kidul, Gendongan, Salatiga and Kalicacing. The results of the accuracy and kappa values in the Random Forest algorithm are quite accurate with an accuracy value of 90% and a kappa value of 73%. The usability test in this study was carried out by distributing questionnaires to city planning department in Salatiga City who had a recapitulation result of 3.62 with the criteria "quite useful". From these results, this research is in accordance with its objectives, the result can be used as one of the city government's recommendations for policy making, especially in Salatiga city planning department.
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Hartomo, Kristoko Dwi, Yessica Nataliani, and Zainal Arifin Hasibuan. "Vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values." PeerJ Computer Science 8 (March 28, 2022): e935. http://dx.doi.org/10.7717/peerj-cs.935.
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This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs atmospheric correction using DOS1 algorithm combined with k-NN algorithm to classify and predict tsunami-affected areas from vegetation indices data that have spatial and temporal resolutions. Meanwhile, the model uses the vegetation indices (i.e., NDWI, NDVI, SAVI), slope, and distance. The result of the experiment compared to other classification algorithms demonstrates good results for the proposed model. It has the smallest MSEs of 0.0002 for MNDWI, 0.0002 for SAVI, 0.0006 for NDVI, 0.0003 for NDWI, and 0.0003 for NDBI. The experiment also shows that the accuracy rate for the prediction model is about 93.62%.
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Puteh, Suhaimi, Nurul Fadhilah Mohamed Rodzali, Mohd Azraai Mohd Razman, Zelina Zaiton Ibrahim, and Muhammad Nur Aiman Shapiee. "Features Extraction of Capsicum Frutescens (C.F) NDVI Values using Image Processing." MEKATRONIKA 2, no. 1 (June 7, 2020): 38–46. http://dx.doi.org/10.15282/mekatronika.v2i1.6727.
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There is yet an application for monitoring plant condition using the Normalized Difference Vegetation Index (NDVI) method for Capsicum Frutescens (C.F) or chili. This study was carried out in three phases, where the first and second phases are to create NDVI images and recognize and extract features from NDVI images. The last stage is to assess the efficiency of Neural Network (N.N.), Naïve Bayes (N.B.), and Logistic Regression (L.R.) models on the classification of chili plant health. The images of the chili plant will be captured using two types of cameras, which can be differentiated by whether or not they have an infrared filter. The images were collected to create datasets, and the NDVI images' features were extracted. The 120 NDVI images of the chili plant were divided into training and test datasets, with 70.0% training and 30.0% test. The extracted data was used to test the classification accuracy of classifiers on datasets. Finally, the N.N. model was found to have the highest classification accuracy, with 96.4 % on the training dataset and 88.9 % on the test dataset. The state of the chili plant can be predicted based on feature extraction from NDVI images by the end of the study.
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Bai, Yongqing, Yaping Yang, and Hou Jiang. "Intercomparison of AVHRR GIMMS3g, Terra MODIS, and SPOT-VGT NDVI Products over the Mongolian Plateau." Remote Sensing 11, no. 17 (August 29, 2019): 2030. http://dx.doi.org/10.3390/rs11172030.
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The rapid development of remote sensing technology has promoted the generation of different vegetation index products, resulting in substantive accomplishment in comprehensive economic development and monitoring of natural environmental changes. The results of scientific experiments based on various vegetation index products are also different with the variation of time and space. In this work, the consistency characteristics among three global normalized difference vegetation index (NDVI) products, namely, GIMMS3g NDVI, MOD13A3 NDVI, and SPOT-VGT NDVI, are intercompared and validated based on Landsat 8 NDVI at biome and regional scale over the Mongolian Plateau (MP) from 2000 to 2014 by decomposing time series datasets. The agreement coefficient (AC) and statistical scores such as Pearson correlation coefficient, root mean square error (RMSE), mean bias error (MBE), and standard deviation (STD) are used to evaluate the consistency between three NDVI datasets. Intercomparison results reveal that GIMMS3g NDVI has the highest values basically over the MP, while SPOT-VGT NDVI has the lowest values. The spatial distribution of AC values between various NDVI products indicates that the three NDVI datasets are highly consistent with each other in the northern regions of the MP, and MOD13A3 NDVI and SPOT-VGT NDVI have better consistency in expressing vegetation cover and change trends due to the highest proportions of pixels with AC values greater than 0.6. However, the trend components of decomposed NDVI sequences show that SPOT-VGT NDVI values are about 0.02 lower than the other two datasets in the whole variation periods. The zonal characteristics show that GIMMS3g NDVI in January 2013 is significantly higher than those of the other two datasets. However, in July 2013, the three datasets are remarkably consistent because of the greater vegetation coverage. Consistency validation results show that values of SPOT-VGT NDVI agree more with Landsat 8 NDVI than GIMMS3g NDVI and MOD13A3 NDVI, and the consistencies in the northeast of the MP are higher than northwest regions.
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Zaitunah, Anita, Samsuri Samsuri, Angelia Frecella Silitonga, and Lailan Syaufina. "Urban Greening Effect on Land Surface Temperature." Sensors 22, no. 11 (May 30, 2022): 4168. http://dx.doi.org/10.3390/s22114168.
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Urbanization has accelerated the conversion of vegetated land to built-up regions. The purpose of this study was to evaluate the effects of urban park configuration on the Land Surface Temperature of the park and adjacent areas. In urban parks, the study analyzed the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and the Land Surface Temperature (LST). The NDVI categorization process resulted in the development of a vegetation density distribution. The majority of Medan’s urban areas were categorized as low density, as seen by their low NDVI values. The NDBI values were significantly higher in the majority of the area. This shows that the majority of places are experiencing a decline in vegetation cover. The density of vegetation varies according to the placement of park components such as trees, mixed plants, recreation, and sports areas. According to LST data, the temperature in the urban park was cooler than in the surrounding areas. Although the surrounding areas are densely populated, urban parks are dominated by trees. Additionally, there is a green space adjacent to the park, which is a green lane that runs alongside the main roadways.
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Poletaev, Arseniy. "Possibilities of GIS technologies for predictive detection of areas of solid flow discharge within water protection zones." InterCarto. InterGIS 28, no. 2 (2022): 583–96. http://dx.doi.org/10.35595/2414-9179-2022-2-28-583-596.
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The article considers the modern possibilities of GIS technologies for monitoring the state of the soil cover and water erosion processes. The possibilities of using the Normalized Diference Vegetation Index (NDVI) to assess various types of vegetation cover are shown. The substantiation of the choice of a key site, which includes both water protection zones and landscape positions associated with them in terms of material and energy flows, is presented. A method for obtaining a vector layer of NDVI values calculated from 9 Sentinel-2 satellite images for the period from March to November 2021 is presented. NDVI values are classified and the cells of the vector layer are combined into classes. Methods for obtaining rasters (with formula reduction) of the Topographical Wetness Index (TWI) and the Stream Power Index (SPI) on the territory of a key area are presented. The vector layer of NDVI values was compared with the TWI and SPI rasters, as well as with the average daily air temperature values. The dynamics of NDVI values for March–November 2021 is shown in the key area, a schematic map of the vector layer of NDVI values, ranked by class, is shown. The calculation of the ratio of areas of different classes in the key area was carried out. Topographical Wetness Index (TWI) and Stream Power Index (SPI) rasters are shown. Examples of queries to databases of layers obtained as a result of intersection of vector layers are given: TWI and NDVI, SPI and NDVI. Schematic maps have been obtained based on a combination of NDVI, TWI, SPI values, showing potentially erosion-hazardous areas. When comparing the average daily air temperature values with the average NDVI values, it was found that the correlation between them is 0.89. Possible measures aimed at reducing the environmental load on the water protection zone are proposed.
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Swanson, David K. "Start of the Green Season and Normalized Difference Vegetation Index in Alaska’s Arctic National Parks." Remote Sensing 13, no. 13 (June 30, 2021): 2554. http://dx.doi.org/10.3390/rs13132554.
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Daily Normalized Difference Vegetation Index (NDVI) values from the MODIS Aqua and Terra satellites were compared with on-the-ground camera observations at five locations in northern Alaska. Over half of the spring rise in NDVI was due to the transition from the snow-covered landscape to the snow-free surface prior to the deciduous leaf-out. In the fall after the green season, NDVI fluctuated between an intermediate level representing senesced vegetation and lower values representing clouds and intermittent snow, and then dropped to constant low levels after establishment of the permanent winter snow cover. The NDVI value of snow-free surfaces after fall leaf senescence was estimated from multi-year data using a 90th percentile smoothing spline curve fit to a plot of daily NDVI values vs. ordinal date. This curve typically showed a flat region of intermediate NDVI values in the fall that represent cloud- and snow-free days with senesced vegetation. This “fall plateau” was readily identified in a large systematic sample of MODIS NDVI values across the study area, in typical tundra, shrub, and boreal forest environments. The NDVI level of the fall plateau can be extrapolated to the spring rising leg of the annual NDVI curve to approximate the true start of green season.
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Akmal, Zakiy, Adi Wibowo, Revi Hernina, and Tjiong Giok Pin. "Spatial Study of Oil Palm Plant Health Using Sentinel 2 Imagery at PTPN VIII Cikasungka Plantation, Bogor, West Java." IOP Conference Series: Earth and Environmental Science 1111, no. 1 (December 1, 2022): 012008. http://dx.doi.org/10.1088/1755-1315/1111/1/012008.
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Abstract Oil palm (Elaeis guineensis Jacq.) is a plantation commodity that is the highest producer and exporter in Indonesia and has high economic value. Increasing oil palm productivity requires several factors, one of which is plant health. Therefore, detecting the health condition of oil palm plants requires research with spatial analysis and a remote sensing method that can monitor plantation data collection strategies efficiently and effectively. One of them is Sentinel-2 using the NDVI algorithm. The algorithm can estimate the relationship between vegetation and health conditions. This study resulted in very healthy plants having NDVI values ranging from 0.80-0.85, healthy plants having NDVI values ranging from 0.76-0.80, critical plants having NDVI values ranging from 0.72-0.76, and unhealthy plants having NDVI values ranging from 0.68-0.72. The distribution of the NDVI value at the PTPN VIII Cikasungka Plantation in January 2022 has a high NDVI value and is spread over every area of the district.
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Zhao, Haidi, Shiliang Liu, Shikui Dong, Xukun Su, Xuexia Wang, Xiaoyu Wu, Lei Wu, and Xiang Zhang. "Analysis of vegetation change associated with human disturbance using MODIS data on the rangelands of the Qinghai-Tibet Plateau." Rangeland Journal 37, no. 1 (2015): 77. http://dx.doi.org/10.1071/rj14061.
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This paper evaluated changes in vegetation from 2000 to 2012, based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI), and related them to changes in estimates of human disturbance on the rangelands of the Qinghai-Tibet Plateau. The main rangeland types studied were desert, steppe and meadow with the latter mainly found in the southern and eastern parts of the study area. The results indicated that human disturbance was distributed mainly in the southern and eastern parts of the study area and corresponded with high NDVI values. The NDVI values showed an upward trend over the study period, with 28.5% of the study area exhibiting a significant increase. The proportion of rangelands that experienced a downward trend in NDVI increased as the level of human disturbance increased. Of the different rangeland types, meadow had the highest NDVI values, the greatest human disturbance, and the highest proportion of rangelands that exhibited a significant decrease in NDVI. Compared with areas with no human disturbance, meadow and steppe rangelands that experienced an increase in human disturbance had lower rates of increase in their NDVI values but, conversely, desert rangelands showed the opposite trend. In addition, it was found that precipitation had the dominant influence on NDVI values and that higher precipitation and slighter lower temperatures over the period of the study were related to an increase in NDVI values.
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Aranguren, Marta, Ander Castellón, and Ana Aizpurua. "Wheat Yield Estimation with NDVI Values Using a Proximal Sensing Tool." Remote Sensing 12, no. 17 (August 25, 2020): 2749. http://dx.doi.org/10.3390/rs12172749.
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Nitrogen (N) splitting is critical to achieving high crop yields without having negative effects on the environment. Monitoring crop N status throughout the wheat growing season is key to finding the balance between crop N requirements and fertilizer needs. Three soft winter wheat fertilization trials under rainfed conditions in Mediterranean climate conditions were monitored with a RapidScan CS-45 (Holland Scientific, Lincoln, NE, USA) instrument to determine the normalized difference vegetation index (NDVI) values at the GS30, GS32, GS37, and GS65 growth stages. The threshold NDVI values in the Cezanne variety were 0.7–0.75 at the GS32, GS37, and GS65 growing stages. However, for the GS30 growing stage, a threshold value could not be established precisely. At this stage, N deficiency may not affect wheat yield, as long as the N status increases at GS32 stage and it is maintained thereafter. Following the NDVI dynamic throughout the growing season could help to predict the yields at harvest time. Therefore, the ΣNDVI from GS30 to GS65 explains about 80% of wheat yield variability. Therefore, a given yield could be achieved with different dynamics in wheat NDVI values throughout the growing cycle. The determined ranges of the NDVI values might be used for developing new fertilization strategies that are able to adjust N fertilization to wheat crop needs.
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Boitumelo, Mohlala, Ruzica Stričević, Enika Gregorić, and Ljubomir Zivotić. "Spatial and temporal changes in plant water supply obtained by NDVI in Tinja and Kozlica watersheds." Zemljiste i biljka 71, no. 2 (2022): 45–64. http://dx.doi.org/10.5937/zembilj2202120b.
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Normalized Difference Vegetation Index (NDVI) is an indicator of vegetation health and land cover changes, based on the reflectance of certain ranges in the electromagnetic spectrum. Land use, seasons and climate changes affect spatial variations in NDVI values. This study focuses on the basins of the rivers Tinja and Kozlica, located on the Eastern parts of the Maljen Mountain, and characterized by the dominant presence of grassy vegetation. Spatial and temporal changes in plant water supply are monitored using 10-meter Sentinel-2 imagery, and further processed on a monthly basis in QGIS for 2020-2021. For better elaboration of NDVI values basins of these two rivers were delineated into 305 sub-basins, on which further analysis was performed. NDVI data during both years range from < 0.1 - > 0.6. NDVI values change during different seasons, which is consistent with the increase and decrease of water stress during the studied period, which refers to changes in weather conditions during the growing season. In the summer months, the highest values exceed 0.6, and in some cases even 0.8. NDVI values in October and November decrease to 0.3 and 0.5, while in winter months NDVI values are <0.1. NDVI values are higher, and less variable, in sub-basins with woody, partially coniferous vegetation. This study contributes to increasing knowledge about the potential application of remote sensing as well as highresolution Sentinel-2 imagery for monitoring plant water supply because the assessment of drought impact on plant production requires the current monitoring of plant water regime. GIS tools enable the delineation of sub-catchments, which helps to better monitor the spatial variation of NDVI within natural landscape entities. NDVI and other indices are easy to calculate, and therefore, Sentinel-2 can play an important role in future drought early warning systemsand in determining conditions of the vegetation cover.
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Lastovicka, Josef, Pavel Svec, Daniel Paluba, Natalia Kobliuk, Jan Svoboda, Radovan Hladky, and Premysl Stych. "Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation." Remote Sensing 12, no. 12 (June 13, 2020): 1914. http://dx.doi.org/10.3390/rs12121914.
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In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q2–Q3: 0.63–0.71; NDMI Q2–Q3: 0.10–0.19) and the TCW index had negative values (Q2–Q3: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.
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Sinyutkina, Anna. "Spatial differentiation and temporal dynamics of drained raised bogs in Western Siberia." E3S Web of Conferences 333 (2021): 02015. http://dx.doi.org/10.1051/e3sconf/202133302015.
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This study analysed the influence of drainage on the vegetation cover of raised bogs in the taiga zone of Western Siberia. The study was based on a temporal analysis of Landsat satellite imagery data using the normalised difference vegetation index (NDVI) for the period from 1975–2020. We investigated four key sites within drained raised bogs. The analysis was not carried out using absolute NDVI values; rather, the ratio of the NDVI values of drained bogs to the NDVI values of a similar pristine bog was used. Four stages of the vegetation dynamics of drained bogs were determined. In the first stage, including the period before drainage, the NDVI values were close to those of the pristine site, which confirms that there are similar conditions before drainage. The second stage, from 1989–2001(2002), is characterised by a decrease in NDVI values relative to the pristine bog. This was probably due to the degradation of moss vegetation, which is a sensitive indicator of a decrease in the water table level in the absence of a significant growth of the tree layer. Furthermore, since the 2000s, there has been an increase in the NDVI values and they have stabilised at the level of a pristine bog.
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Nádudvari, Ádám. "The localization of urban heat island in the Katowice conurbation (Poland) using the combination of land surface temperature, Normalized Difference Vegetation Index and Normalized Difference Built-up Index." Geographia Polonica 94, no. 1 (2021): 111–29. http://dx.doi.org/10.7163/gpol.0196.
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The localization of Surface Urban Heat Island (SUHI) as a potential heat risk for the urban population was evaluated. The paper aimed to propose an approach to quantify and localize (SUHI) based on Landsat series TM, ETM+, OLI satellite imageries from the period 1996-2018 and recognize the Atmospheric Urban Heat Island (AUHI) effects from long term temperature measurements. Using the theoretical relation between the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI) and the LST (Land Surface Temperature), SUHIintensity and SUHIrisk maps were created from the combination of LST, NDVI, NDBI using threshold values to localize urban heat island in the Katowice conurbation. Negative values of SUHI intensity characterize areas where there is no vegetation, highly built-up areas, and areas with high surface temperatures. The urban grow – revealed from SUHI – and global climate change are acting together to strengthen the global AUHI effect in the region as the temperature measurements were indicated.
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Oliveira, Thálita Carrijo de, Elizabeth Ferreira, and Antônio Augusto Aguilar Dantas. "Temporal variation of normalized difference vegetation index (NDVI) and calculation of the crop coefficient (Kc ) from NDVI in areas cultivated with irrigated soybean." Ciência Rural 46, no. 9 (June 16, 2016): 1683–88. http://dx.doi.org/10.1590/0103-8478cr20150318.
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ABSTRACT: Vegetation indices obtained by remote sensing products have various applications in agriculture. An important application of the Normalized Difference Vegetation Index (NDVI) is obtaining the crop coefficient (Kc). The aims of this study were to analyze NDVI temporal profiles and to obtain Kc from the NDVI vegetation index product MOD13Q1. The analysis is based on the phenological stages of irrigated soybean crops in the municipality of Planura/MG during the 2010/2011 growing season. Areas planted with irrigated soybean were identified through fieldwork. Temporal series of the MOD13Q1 products were used to analyze NDVI, allowing the extraction of NDVI values for all points in the period studied. The NDVI temporal profiles showed a similar pattern to each other and corresponded to the crop cycle. The KcNDVI values for the MOD13Q1 products were well correlated to the FAO Kc values (r2=0.72). Thus, NDVI can be used as an alternative for obtaining crop coefficient (Kc).
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Terekhin, E. A. "RECOGNITION OF ABANDONED AGRICULTURAL LANDS USING SEASONAL NDVI VALUES." Computer Optics 41, no. 5 (January 1, 2017): 719–25. http://dx.doi.org/10.18287/2412-6179-2017-41-5-719-725.
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Prudente, Victor Hugo Rohden, Erivelto Mercante, Jerry Adriani Johann, Carlos Henrique Wachholz de Souza, Lucas Volochen Oldoni, Luiz Almeida, Willyan Ronaldo Becker, and Bruno Bonemberger Da Silva. "Comparison Between Vegetation Index Obtained by Active and Passive Proximal Sensors." Journal of Agricultural Studies 9, no. 2 (January 26, 2022): 391. http://dx.doi.org/10.5296/jas.v9i2.18462.
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Proximal sensors have been used to characterize the crop biophysical properties by reflectance values and/or using Vegetation Indices (IV). Our goal with this work is to compare NDVI (Normalized Difference Vegetation Index) spectra-temporal profiles obtained by active (GreenSeeker 505 Handheld) and passive (FieldSpec4 model Standard-Res) proximal sensors to monitor soybeans and beans. It was monitored agricultural fields with soybeans Nidera 5909RG variety and beans Imperador variety, located in the municipality of Cascavel, Parana state, Brazil. The proximal sensors were used to monitor the crop's conditions on different Days After Sowing (DAS). NDVI from FieldSpec4 (NDVI FS) showed a higher correlation with GreenSeeker NDVI (NDVI GS) in the wavelengths of 649 nm and 771 nm for soybeans (rs = 0.9105) and 646 nm and 792 nm for beans (rs = 0.9382). The inter-calibration of NDVI GS values in function of NDVI FS, considering the entire phenological cycle, resulted in RMSE = 0.0520 and dr = 0.8630 for soybeans and RMSE = 0.0636 and dr = 0.8890 for beans. NDVI values showed saturation during the major vegetative development of the crops, interfering in the inter-calibration process. In general, the NDVI GS and NDVI FS were similar in terms of their spectral-temporal pattern. According to our results, the active sensor could be used to crop monitoring, resulting in a lower cost and less climatic interference.
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Zsigmond, Tibor, Péter Braun, János Mészáros, István Waltner, and Ágota Horel. "Investigating Plant Response to Soil Characteristics and Slope Positions in a Small Catchment." Land 11, no. 6 (May 25, 2022): 774. http://dx.doi.org/10.3390/land11060774.
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Methods enabling stakeholders to receive information on plant stress in agricultural settings in a timely manner can help mitigate a possible decrease in plant productivity. The present work aims to study the soil–plant interaction using field measurements of plant reflectance, soil water content, and selected soil physical and chemical parameters. Particular emphasis was placed on sloping transects. We further compared ground- and Sentinel-2 satellite-based Normalized Vegetation Index (NDVI) time series data in different land use types. The Photochemical Reflectance Index (PRI) and NDVI were measured concurrently with calculating the fraction of absorbed photochemically active radiation (fAPAR) and leaf area index (LAI) values of three vegetation types (a grassland, three vineyard sites, and a cropland with maize). Each land use site had an upper and a lower study point of a given slope. The NDVI, fAPAR, and LAI averaged values were the lowest for the grassland (0.293, 0.197, and 0.51, respectively), which showed the highest signs of water stress. Maize had the highest NDVI values (0.653) among vegetation types. Slope position affected NDVI, PRI, and fAPAR values significantly for the grassland and cropland (p < 0.05), while the soil water content (SWC) was different for all three vineyard sites (p < 0.05). The strongest connections were observed between soil physical and chemical parameters and NDVI values for the vineyard samples and the selected soil parameters and PRI for the grassland. Measured and satellite-retrieved NDVI values of the different land use types were compared, and strong correlations (r = 0.761) between the methods were found. For the maize, the satellite-based NDVI values were higher, while for the grassland they were slightly lower compared to the field-based measurements. Our study indicated that incorporating Sentinel-derived NDVI can greatly improve the value of field monitoring and provides an opportunity to extend field research in more depth. The present study further highlights the close relations in the soil–plant–water system, and continuous monitoring can greatly help in developing site-specific climate change mitigating methods.
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Qiu, Chunrong, Guiping Liao, Hongyuan Tang, Fan Liu, Xiaoyi Liao, Rui Zhang, and Zanzhong Zhao. "Derivative Parameters of Hyperspectral NDVI and Its Application in the Inversion of Rapeseed Leaf Area Index." Applied Sciences 8, no. 8 (August 4, 2018): 1300. http://dx.doi.org/10.3390/app8081300.
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AVNDVI (Accumulative Visible Normalized Difference Vegetation Index), a new type of derivative parameters of NDVI, was set up by improving the computational formulas and importing the spectral information of visible bands after analyzing the construction idea of NDVI and its derivative parameters. Then, the characteristic values of VNDVI (Visible NDVI) were calculated by applying a combinational method of sensitive bands of visible bands. The study carried out the fitting analysis between NDVI, VNDVI, AVNDVI, and LAI (Leaf Area Index). Several conclusions are obtained according to data analysis. Firstly, all of the determination coefficients between NDVI, VNDVI, AVNDVI, and LAI of rapeseed can reach or exceed 0.83. The distribution of their RMSE values ranges from 0.4 to 0.5 and absolute values of RE vary from 0.9% to 2.1%. Secondly, the inversion sensitivity SV of VNDVI and LAI ranges from 0.7 to 1.9 relative to NDVI, and the inversion sensitivity SA of AVNDVI decreases in varying degrees with the promotion of capacity of resisting disturbance accordingly. Its value varies from 0.1 to 0.9. Thirdly, the values of SA remain stable between 0.1 and 0.3 with the increase of NDVI. Applying the inversion model of AVNDVI will be a considerable scheme when faced with a complex environment and many interfering factors.
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Loginov, Vladimir F., and Maxim A. Khitrykau. "Estimation of changes in bioclimatic potential values on the territory of Belarus using normalised difference vegetation index (NDVI)." Journal of the Belarusian State University. Geography and Geology, no. 1 (June 8, 2021): 3–12. http://dx.doi.org/10.33581/2521-6740-2021-1-3-12.
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Relations between bioclimatic potential changes and changes in state of crops have been analysed. NDVI (normalised difference vegetation index) and biological productivity parameter by D. I. Shashko (Bk) were used for this purpose. Average values of both parameters have been increasing over the territory of Belarus: since the beginning of 21st century, Bk values increased by 10–15 points and NDVI values – by 0.02–0.03 points. Relations between them depend on the type of vegetation. Current climate changes appeared to be favorable for forests, but average NDVI values on the croplands have been decreasing despite Bk growth. The main reason for this is high correlation between state of vegetation and water resources available (correlation coefficient r between NDVI and precipitation is 0.65–0.80), which, according to TWSA (terrestrial water storage anomaly) measurements, have begun to decrease during the last decade.
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Jiang, Rui, Pei Wang, Yan Xu, Zhiyan Zhou, Xiwen Luo, Yubin Lan, Genping Zhao, Arturo Sanchez-Azofeifa, and Kati Laakso. "Assessing the Operation Parameters of a Low-altitude UAV for the Collection of NDVI Values Over a Paddy Rice Field." Remote Sensing 12, no. 11 (June 8, 2020): 1850. http://dx.doi.org/10.3390/rs12111850.
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Unmanned aerial vehicle (UAV) remote sensing platforms allow for normalized difference vegetation index (NDVI) values to be mapped with a relatively high resolution, therefore enabling an unforeseeable ability to evaluate the influence of the operation parameters on the quality of the thus acquired data. In order to better understand the effects of these parameters, we made a comprehensive evaluation on the effects of the solar zenith angle (SZA), the time of day (TOD), the flight altitude (FA) and the growth level of paddy rice at a pixel-scale on UAV-acquired NDVI values. Our results show that: (1) there was an inverse relationship between the FA (≤100 m) and the mean NDVI values, (2) TOD and SZA had a greater impact on UAV–NDVIs than the FA and the growth level; (3) Better growth levels of rice—measured using the NDVI—could reduce the effects of the FA, TOD and SZA. We expect that our results could be used to better plan flight campaigns that aim to collect NDVI values over paddy rice fields.
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Bariani, Cassiane J. M. Victoria, Nelson M. Victoria Bariani, M. T. Petry, R. Carlesso, D. Silveira Kersten, L. Basso, and J. R. Henkes. "Using NDVI Time-series Profiles for Monitoring Corn Plant Phenology of Irrigated Areas in Southern Brazil." Agrociencia 19, no. 3 (December 2015): 79. http://dx.doi.org/10.31285/agro.19.291.
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Among the factors that contribute the most for increasing maize cultivated area and grain yield in Southern Brazil are the crop genetic selection, soil, crop and water management and recent advances in crop remote monitoring techniques. The Normalized Difference Vegetation Index (NDVI) obtained with remote sensing techniques may be used to provide historical and real-time evaluation characteristics of a particular crop, such as density and vigor without neither field visits, nor interfering directly or indirectly in crop growth and development. This procedure may substantially reduce monitoring or control costs. In this paper, a temporal profile series of NDVI was generated during the maize crop growth period with the objective of evaluating the crop phenology of seven irrigated areas under center pivots. Eight images from LANDSAT5/TM satellite, of the 222/80 and 223/80 path/row were used. The quantitative intervals of NDVI mean values were evaluated from the temporal profile series based on the crop sowing and harvest dates. The NDVI values varied from: 0.18-0.53for initial crop stage; 0.54-0.80for rapid crop growth; 0.20-0.74 for mid-season; and 0.28-0.41for late season. The use of NDVI allows a good differentiation among the maize crop stages of irrigated areas. There has been a drop in NDVI values in the R1 stage, at 54 days after sowing (DAS), due to detasseling. Maximum NDVI value (0.80) was observed at 63DAS, with maize phenology between R2-R5 stages. NDVI values decreased from R6 growth stage till harvest (134 DAS) due to crop maturity and senescence. During this period the average NDVI value was 0.40.
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Prajesh, P. J., Balaji Kannan, S. Pazhanivelan, and K. P. Ragunath. "Monitoring and mapping of seasonal vegetation trend in Tamil Nadu using NDVI and NDWI imagery." Journal of Applied and Natural Science 11, no. 1 (February 20, 2019): 54–61. http://dx.doi.org/10.31018/jans.v11i1.1964.
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In order to monitor vegetation growth and development over the districts and land covers of Tamil Nadu, India during the crop growing season viz., Khairf and Rabi of 2017, Moderate Resolution Imaging Spectroradiometer (MODIS) derived surface reflectance product (MOD09A1) which is available at 500 m resolution and 8-day temporal period was used to derive a time series based Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for monitoring and mapping terrestrial vegetation trend analysis which showed areas in Tamil Nadu having vegetation greening and vegetation browning. The regression slope values derived from the trend analysis was utilized and the NDVI and NDWI seasonal trend showed majority of area in Tamil Nadu falling under positive trend during the Kharif season (86.52 per cent for NDVI and 90.29 per cent for NDWI). While irrespective of land cover classes, NDVI and NDWI during Kharif season showed a greater positive trend (greening) with least negative trend (browning) for vegetation growth over the land covers whereas during Rabi season it was observed to have a mix of positive trend and negative trend over the land covers. This study was carried out to show that a systematic study can be done for understanding changes over the landscape through the use of high spatial resolution satellite dataset such as MODIS, which provides detailed spatial and temporal description at regional scale. While a trend analysis using regression slope values can be considered for demonstrating the spatial and temporal consistency on land and vegetation dynamics.
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Zamani-Noor, Nazanin, and Dominik Feistkorn. "Monitoring Growth Status of Winter Oilseed Rape by NDVI and NDYI Derived from UAV-Based Red–Green–Blue Imagery." Agronomy 12, no. 9 (September 16, 2022): 2212. http://dx.doi.org/10.3390/agronomy12092212.
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The current study aimed to evaluate the potential of the normalized difference vegetation index (NDVI), and the normalized difference yellowness index (NDYI) derived from red–green–blue (RGB) imaging to monitor the growth status of winter oilseed rape from seeding to the ripening stage. Subsequently, collected values were used to evaluate their correlations with the yield of oilseed rape. Field trials with three seed densities and three nitrogen rates were conducted for two years in Salzdahlum, Germany. The images were rapidly taken by an unmanned aerial vehicle carrying a Micasense Altum multi-spectral camera at 25 m altitudes. The NDVI and NDYI values for each plot were calculated from the reflectance at RGB and near-infrared (NIR) bands’ wavelengths pictured in a reconstructed and segmented ortho-mosaic. The findings support the potential of phenotyping data derived from NDVI and NDYI time series for precise oilseed rape phenological monitoring with all growth stages, such as the seedling stage and crop growth before winter, the formation of side shoots and stem elongation after winter, the flowering stage, maturity, ripening, and senescence stages according to the crop calendar. However, in comparing the correlation results between NDVI and NDYI with the final yield, the NDVI values turn out to be more reliable than the NDYI for the real-time remote sensing monitoring of winter oilseed rape growth in the whole season in the study area. In contrast, the correlation between NDYI and the yield revealed that the NDYI value is more suitable for monitoring oilseed rape genotypes during flowering stages.
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Guan, Senlin, Koichiro Fukami, Hitoshi Matsunaka, Midori Okami, Ryo Tanaka, Hiroshi Nakano, Tetsufumi Sakai, Keiko Nakano, Hideki Ohdan, and Kimiyasu Takahashi. "Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops using Small UAVs." Remote Sensing 11, no. 2 (January 9, 2019): 112. http://dx.doi.org/10.3390/rs11020112.
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The aim of this study was to use small unmanned aerial vehicles (UAVs) for determining high-resolution normalized difference vegetation index (NDVI) values. Subsequently, these results were used to assess their correlations with fertilizer application levels and the yields of rice and wheat crops. For multispectral sensing, we flew two types of small UAVs (DJI Phantom 4 and DJI Phantom 4 Pro)—each equipped with a compact multispectral sensor (Parrot Sequoia). The information collected was composed of numerous RGB orthomosaic images as well as reflectance maps with spatial resolution greater than a ground sampling distance of 10.5 cm. From 223 UAV flight campaigns over 120 fields with a total area coverage of 77.48 ha, we determined that the highest efficiency for the UAV-based remote sensing measurement was approximately 19.8 ha per 10 min while flying 100 m above ground level. During image processing, we developed and used a batch image alignment algorithm—a program written in Python language–to calculate the NDVI values in experimental plots or fields in a batch of NDVI index maps. The color NDVI distribution maps of wide rice fields identified differences in stages of ripening and lodging-injury areas, which accorded with practical crop growth status from aboveground observation. For direct-seeded rice, variation in the grain yield was most closely related to that in the NDVI at the early reproductive and late ripening stages. For wheat, the NDVI values were highly correlated with the yield ( R 2 = 0.601–0.809) from the middle reproductive to the early ripening stages. Furthermore, using the NDVI values, it was possible to differentiate the levels of fertilizer application for both rice and wheat. These results indicate that the small UAV-derived NDVI values are effective for predicting yield and detecting fertilizer application levels during rice and wheat production.
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Jamei, Yashar, Mehdi Seyedmahmoudian, Elmira Jamei, Ben Horan, Saad Mekhilef, and Alex Stojcevski. "Investigating the Relationship between Land Use/Land Cover Change and Land Surface Temperature Using Google Earth Engine; Case Study: Melbourne, Australia." Sustainability 14, no. 22 (November 10, 2022): 14868. http://dx.doi.org/10.3390/su142214868.
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The rapid alteration to land cover, combined with climate change, results in the variation of the land surface temperature (LST). This LST variation is mainly affected by the spatiotemporal changes of land cover classes, their geospatial characteristics, and spectral indices. Melbourne has been the subject of previous studies of land cover change but often over short time periods without considering the trade-offs between land use/land cover (LULC) and mean daytimes summer season LST over a more extended period. To fill this gap, this research aims to investigate the role of LULC change on mean annual daytime LST in the hot summers of 2001 and 2018 in Melbourne. To achieve the study’s aim, LULC and LST maps were generated based on the cost-effective cloud-based geospatial analysis platform Google Earth Engine (GEE). Furthermore, the geospatial and geo-statistical relationship between LULC, LST, and spectral indices of LULC, including the Normalised Difference Built-up Index (NDBI) and the Normalised Difference Vegetation Index (NDVI), were identified. The findings showed that the mean daytime LST increased by 5.1 °C from 2001 to 2018. The minimum and maximum LST values were recorded for the vegetation and the built-up area classes for 2001 and 2018. Additionally, the mean daytime LST for vegetation and the built-up area classes increased by 5.5 °C and 5.9 °C from 2001 to 2018, respectively. Furthermore, both elevation and NDVI were revealed as the most influencing factors in the LULC classification process. Considering the R2 values between LULC and LST and their NDVI values in 2018, grass (0.48), forest (0.27), and shrubs (0.21) had the highest values. In addition, urban areas (0.64), bare land (0.62), and cropland (0.61) LULC types showed the highest R2 values between LST regarding their NDBI values. This study highlights why urban planners and policymakers must understand the impacts of LULC change on LST. Appropriate policy measures can be proposed based on the findings to control Melbourne’s future development.
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Yang, J. S., F. P. Campomanes, C. L. Patiño, and M. J. L. Flores. "MONITORING VEGETATION COVER CHANGE USING VEGETATION INDICES IN TANGBO RIVER, BARANGAY TANGBO, SAMBOAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 493–98. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-493-2019.
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Abstract. Tangbo River is an important resource in Cebu’s southern town of Samboan for being the site of Aguinid Falls, a known tourist destination. Monitoring the changes in the river’s riparian vegetation is important since it has impacts on its ecological role of helping maintain biodiversity and river water quality. This study aims to detect vegetation index changes along the Tangbo River corridor using three vegetation indices: NDVI, EVI, NDMI, and Tasseled Cap indices, specifically for the years 1998, 2004, 2009, 2016, and 2019. It also aims to monitor the changes in NDVI and EVI values alongside tourism arrivals in Aguinid in 2018.Cloudless Landsat 5 (1998, 2004, 2009, and 2016) and Landsat 8 (2019) imagery were selected. Thirty reference points were plotted along the river with a 30-m distance between each point. Vegetation Indices (VI) and Tasseled Cap values were generated using data from these points and were compared for each selected year. NDVI and EVI values from the same reference points used in Landsat were generated from selected cloudless months of 2018 Planetscope imagery. Inbound tourist records were acquired from the tourism office of Samboan and the tourism arrivals for the year 2018 was then graphed with the Planetscope VI values for better visualization.Landsat imagery showed that there was a general upward trend in the vegetation indices from 1998 to 2019. Tasseled Cap Greenness and Wetness showed an increase in values from 1998–2019 while Tasseled Cap Brightness showed the opposite. Results from Planetscope data for the year 2018 showed that there was an inverse pattern between NDVI and tourism arrivals. Tourism arrivals peaked during the months of April and May based on annual records, while VI values dropped. On the other hand, both VI values peaked towards the last quarter of the year while tourist numbers dropped. This suggests that the pattern of VI values and tourism arrivals seemed to be influenced by seasonal changes rather than with each other. Findings from the study shows that further data collection is required to be able to establish a relationship between tourism and vegetation index values.
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Avcı, İ., and E. Farzaliyev. "CLASSIFICATION OF LANDS OF REMOTE SENSITIVE DATA BY NDVI METHOD IN SMART AGRICULTURE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 73–77. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-73-2021.
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Abstract. A large share of the earth's surface is observed with remote sensing technology. Thanks to the data obtained from this process, information about the observed lands is obtained. In this study, NDVI (normalized difference), which is developed by applying mathematical operations on the reflection values of plants at different wavelengths from remote sensing technology and different application areas of this technology, electromagnetic rays, and spectral reflection values, and which is used as a method that provides a value expressing vegetation density. Vegetation index) method, NDVI value, and plant groups analyzed according to this value, sample MATLAB applications related to the NDVI method are mentioned. -Green-Blue) image of visible red and infrared regions, histogram graph showing the relationships between the intensities of values in NIR (near-infrared) and Red (visible Red) bands, NDVI image, and threshold function at the end. The NDVI image was obtained by using the direction (to detect areas that may have vegetation) is shown.
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Krizek, João Pedro Ocanha, and Luciana Cavalcanti Maia Santos. "Protocolo Metodológico para Obtenção dos Valores de Reflectância e de NDVI de Imagens Landsat 8/OLI Utilizando LEGAL." Revista Brasileira de Geografia Física 14, no. 2 (April 14, 2021): 869. http://dx.doi.org/10.26848/rbgf.v14.2.p869-880.
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A obtenção dos valores de reflectância se mostra imprescindível para se calcular índices de vegetação, como o NDVI (Normalized Difference Vegetation Index). Este índice é utilizado para classificar a distribuição global da vegetação e para inferir variáveis ecológicas e ambientais, como a produção de fitomassa. Apesar disso, não é incomum encontrar trabalhos que utilizam os números digitais (ND) para a obtenção direta dos índices de vegetação; entretanto, tais números digitais não representam valores físicos reais e, portanto, não podem ser utilizados diretamente para o cálculo do NDVI. Assim, o objetivo deste artigo é demonstrar um protocolo metodológico para a conversão dos ND das imagens Landsat 8/OLI em valores de reflectância e a subsequente obtenção do NDVI, através da linguagem LEGAL (Linguagem Espacial para Geoprocessamento Algébrico), e, dessa forma, possibilitar a replicação e execução de outras pesquisas que visem obter esse índice de vegetação no software SPRING. Além disso, objetivou-se também demonstrar a importância da conversão dos ND em reflectância, a partir da comparação de uma imagem NDVI gerada através da reflectância com a mesma imagem NDVI gerada por meio dos dados brutos. Os resultados apontaram que a obtenção do NDVI através dos valores brutos de imagens de sensoriamento remoto, sem a necessária conversão dos números digitais em valores reais de reflectância, leva a resultados incorretos na estimativa de dados ecológicos da vegetação, subestimando a fitomassa. Dessa forma, esse trabalho ressalta a importância de se seguir um protocolo metodológico para a estimativa correta da fitomassa, produtividade e outros parâmetros da vegetação. Methodological protocol for obtaining reflectance and NDVI values from Landsat 8/OLI images using LEGALA B S T R A C TObtaining reflectance values is essential for calculating vegetation indices, such as the NDVI (Normalized Difference Vegetation Index). This index is used to classify the global distribution of vegetation and to infer the ecological and environmental parameters such as phytomass production. Nevertheless, it is common to find works that use digital numbers (DN) to directly obtain vegetation indices; however, such digital numbers do not represent actual physical values and therefore cannot be used directly for NDVI calculation. Thus, this paper aims to demonstrate a methodological protocol for DN conversion of Landsat 8/OLI images into reflectance values and then for obtaining NDVI through the LEGAL (Spatial Language for Algebraic Geoprocessing). Therefore, this protocol enables the replication and execution of other studies aimed to obtain this vegetation index using SPRING. In addition, the objective was also to demonstrate the importance of converting DN to reflectance by comparing an NDVI image generated from reflectance with the same NDVI image generated through the raw data. The results showed that obtaining the NDVI through the raw values of remote sensing images, without the conversion of digital numbers to real reflectance values, leads to incorrect results in the estimation of ecological vegetation data, underestimating phytomass, thus emphasizing the importance of following a methodological protocol for the correct estimation of biomass, productivity and other phytological parameters.Keywords: protocol, NDVI, reflectance, Landsat 8, SPRING
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Kutchartt, E., J. Hernández, P. Corvalán, Á. Promis, and F. Pirotti. "DETECTING AND EVALUATING DISTURBANCE IN TEMPERATE RAINFOREST WITH SENTINEL-2, MACHINE LEARNING AND FOREST PARAMETERS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 913–20. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-913-2022.
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Abstract. Earth observation via remote sensing imagery provides a fast way to define alteration levels. In this work 12 stands of Araucaria-Nothofagus forests were selected in southern Chile, which represented four alteration levels: (i) None (ii) Low (iii) Medium and (iv) High. The stands were surveyed measuring 379 field plots and Google Earth Engine was used to collect a composite of Sentinel-2 images over a one-year range, from June 2019 to June 2020. The following approaches were tested: (i) aggregating the normalized difference vegetation index (NDVI) of each image and selecting the 95th and 99th percentile values of NDVI for each pixel; (ii) creating a composite imagery with best pixels over one year timeline using NDVI as weighting factor and NDVI value band itself (NDVI) – this is similar to the 99th percentile in the previous point, but with maximum values of NDVI; (iii) aggregating the composite as in the previous approach, but using the full spectral information of Sentinel-2 and then random forest machine learning for classification over alteration areas with k-fold validation with k=5. Results show that the 95th and 99th percentile of NDVI values from approach (i) do not discriminate the four classes correctly. The maximum NDVI from approach can distinguish all four classes. It must be noted through that statistical significance does not necessarily imply a strong practical significance; medium and high alterations have very similar NDVI distributions. Random forest results provided an F-score for each class higher than 80% except for the “medium alteration” class.
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Ramachandran, J., R. Lalitha, and S. Vallal Kannan. "Estimation of Site-specific Crop Coefficients for Major Crops of Lalgudi Block in Tamil Nadu using Remote Sensing based Algorithms." Journal of Agricultural Engineering 58, no. 1 (March 31, 2021): 62–72. http://dx.doi.org/10.52151/jae2021581.1735.
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Crop coefficient (Kc ) is an important parameter in estimating the crop water requirements during different crop growth stages. The Kc values for a particular crop are highly site and region-specific and need to be precisely determined for each agro-climatic region for better irrigation scheduling and improved water and crop productivity. The site-specific crop coefficients for paddy, sugarcane, and banana cultivated in Lalgudi block, Tiruchirapalli District, Tamil Nadu, India, were estimated using two remote sensing-based methods viz. NDVI-Kc linear regression technique and SEBAL actual evapotranspiration to reference evapotranspiration ratio approach (SEBAL-Kc ). The Kc values obtained by remote sensing methods were compared with FAO-56 Adjusted Kc (ClimAdj-Kc ) for local climatic conditions and FAO-56 tabulated reference Kc values (FAOTab-Kc ). Regression analysis revealed a good agreement between NDVI-Kc and ClimAdj-Kc for paddy (R2 =0.95), banana (R2 =0.93), and sugarcane (R2 =0.79). Compared to FAO56-Kc, the derived Kc values using NDVI-Kc were higher, while the SEBAL-Kc values were lower for all growth stages of paddy. For sugarcane crops, the FAO-56 Kc, NDVI-Kc, and ClimAdj-Kc for local climate were almost similar in all stages. In the case of bananas, NDVI-Kc and SEBAL-Kc were higher as compared to the FAO-56-Kc and ClimAdj-Kc. SEBAL approach performs well as it accounted for local climatic conditions and crop canopy changes, whereas NDVI considered only crop canopy. However, the SEBAL method is computationally intensive as compared to the NDVI-Kc method. The Kc values estimated in this study can be important in quantifying the crop evapotranspiration at regional and field scales, leading to better decision-making in irrigation scheduling.
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Bertici, Radu, Daniel Dicu, Mihai Herbei, and Florin Sala. "The Potential of Pig Sludge Fertilizer for Some Pasture Agricultural Lands’ Improvement: Case Study in Timiș County, Romania." Agronomy 12, no. 3 (March 14, 2022): 701. http://dx.doi.org/10.3390/agronomy12030701.
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In the context of the current energy crisis, pig sludge may be a more accessible fertilizer resource for different categories of farmers and agro-ecosystems, in order to support soil fertility and agricultural production. The present study presents results regarding the influence of pig sludge on soil quality and the spatial and temporal variability of a pasture agro-ecosystem, in the area of Ciacova locality, Timiș County, Romania. The pig sludge was fermented for a period of 6 months in fermentation tanks and was applied at a rate of 80 m3 ha−1 y−1 between 2013 and 2019, on two pasture plots (P808, P816). In the study period (2013–2019), the agrochemical indices studied presented the values: pH = 5.90 ± 0.09 (P816-6-13) and pH = 6.90 ± 0.06 (P808-7-18); P = 10.20 ± 2.26 ppm (P808-4-13) and P = 69.10 ± 3.04 ppm (P808-5-19); K = 176.00 ± 7.44 ppm (P816-4-13) and K = 429.00 ± 7.33 ppm (P816-3-19); NI = 2.45% ± 0.08% (P816-6-13) and NI = 3.87% ± 0.06% (P816-6-19). The variability of the land, i.e., the pasture category, evaluated based on the NDVI index (seven NDVI classes were generated, C1 to C7) decreased under the influence of pig sludge, the values of the variation coefficients being CVNDVI = 17.5098 in 2019 compared to CVNDVI = 41.5402 in 2013 for P808 and CVNDVI = 32.0685 in 2019, compared to CVNDVI = 52.2031 in 2013 for P816. It was found that the land area decreased (2019 compared to 2013) from classes C1 to C4 NDVI (low NDVI values, NDVI < 0.5) and the area increased within classes C6 and C7 NDVI (high NDVI values, NDVI > 0.5).
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Rodimtsev, S. A., N. E. Pavlovskaya, S. V. Vershinin, I. V. Gorkova, and I. N. Gagarina. "The use of the vegetative index NDVI to predict grain crop yields." Bulletin of NSAU (Novosibirsk State Agrarian University), no. 4 (January 12, 2023): 56–67. http://dx.doi.org/10.31677/2072-6724-2022-65-4-56-67.
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The need for unified approaches to determining the phenological phase of a reliable indicator of the vegetative index is one of the critical problems of early forecasting of crop yields using satellite monitoring. Several works of domestic and foreign researchers formulate different estimates of the correlation relationship between NDVI and yield. This study aimed to obtain predictive models for the product of winter wheat and spring barley using indicators that are adequate for formalizing the tasks of predicting the trend section of the vegetative index NDVI of crops within the experimental farm of the Oryol State Agrarian University. Based on the analysis of the dynamics of the vegetation index NDVI, based on multi-year studies, the maximum mean annual values of the vegetation index, 0.72 for winter wheat and 0.56 for spring barley, were determined.The maximum NDVI values of the 2021 season for these crops are 0.78 and 0.58. It was found that the peaks of NDVI values correspond to the earing phase of crops with possible variation from 1 to 13 days. The correlation coefficients between the maximum values of NDVI and productivity of crops were 0.79 and 0.75 for winter wheat and spring barley, respectively, which suggests the possibility of reliable prediction of crop yield based on the data of their peak NDVI values. The authors obtained predictive crop yield models based on polynomial (second-degree) functions. A reliable yield forecast expands the scope of reasonable estimates and the implementation of plans aimed at the progressive development of the individual farm. Furthermore, it contributes to the food security of Russia as a whole.
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NITESH AWASTHI, JAYANTNATH TRIPATHI, KAILASH K. DAKHORE, DILEEP KUMAR GUPTA, and Y. E. KADAM. "Linkage between the vegetation indices and climate factors over Haryana." Journal of Agrometeorology 24, no. 4 (December 2, 2022): 380–83. http://dx.doi.org/10.54386/jam.v24i4.1834.
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Present study was an attempt to study the relationship of Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) with climatic parameters (maximum temperature, minimum temperature, relative humidity, rainfall, wind speed and aerosol optical depth) over the Indian state of Haryana using MODIS derived vegetation indices on monthly and yearly values for the time period from 2010 to 2020. The values of correlations coefficients of NDVI and LAI with climatic variables varied with the months, the nature of their variation was similar for two indices. During summer season the correlation values were maximum while these were minimum during rainy season. The overall correlation analysis revealed that the rainfall and relative humidity were positively correlated with NDVI and LAI, while the remaining climate variables had negative impact on the NDVI and LAI.
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Devitt, D. A., B. Bird, L. Fenstermaker, and M. D. Petrie. "Differing Fine-Scale Responses of Vegetation and Bare Soil to Moisture Variation in a Pinyon-Juniper Woodland Underlie Landscape-Scale Responses Observed from Remote Sensing." Environment and Natural Resources Research 11, no. 1 (June 30, 2021): 1. http://dx.doi.org/10.5539/enrr.v11n1p1.
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Pinyon juniper woodlands in the American southwest face an uncertain ecological future with regard to climate altered precipitation. Although satellite remote sensing will be relied upon to assess the overall health of these plant communities more fine scaled information is needed to elucidate the mechanisms shaping the broader scaled regional assessments. We conducted a study to assess the NDVI response at the plant canopy level (insitu sensors placed over the canopies) of three tree and one shrub species to changes in precipitation, reference evapotranspiration and soil volumetric water content. Landsat data was used to compare stand integrated and satellite NDVI values. We also provided supplemental water in the amount of 10.85 cm over the study period to additional trees and shrubs which also had insitu NDVI sensors placed over their canopies. NDVI at the canopy level separated statistically by species and when contrasted with bare soil (p<0.001). Spring early summer dry down events were inversely related to increasing ETref-precipitation with a steeper dry down slope in the first year associated with no rainfall occurring in May and June. All three-tree species did not show any significant difference in canopy NDVI based on supplemental water, however the shrub species did reveal a significant response to water (p<0.001). Although all of the three-tree species revealed a one-month period in which they responded to precipitation in July of the first year after 11.2 cm of precipitation, no immediate (day of or next day) response was observed to precipitation or supplemental water events. Snowberry was unique in its NDVI response during the spring green up period in the second year revealing a highly linear shift over a 40-day period with a clear separation between treatments (p<0.001) with those plants receiving supplemental water having a higher more positive slope. Landsat NDVI values revealed an inverse sinusoidal relationship with ETref-precipitation (R2=0.59 p=0.012). Landsat values (0.19+/- 0.01) were found to have no significant difference with bare soil NDVI (0.17+/- 0.01) but were significantly different from all four tree and shrub species. Integrated NDVI based on sensor weighted % cover estimates (0.37+/-0.03) were nearly double Landsat values (0.19+/-0.01). Both NDVI values of pinyon pine and Utah juniper were found to be linear correlated with Landsat NDVI in the second Year (R2>0.75, p<0.001). Multiple regression analysis revealed that 95% of the variation in Landsat NDVI in the second year could be accounted for based on bare soil NDVI and pinyon pine NDVI (p<0.001). et al., NDVI interspace (bare soil) of pinyon juniper woodlands dominated the nature of the Landsat curve. Our results demonstrate the value of ground sensors to help fill the gap between what can be inferred at the forest canopy level and what is occurring at the plant level.
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Naif, Salwa S., Dalia A. Mahmood, and Monim H. Al-Jiboori. "Seasonal normalized difference vegetation index responses to air temperature and precipitation in Baghdad." Open Agriculture 5, no. 1 (October 20, 2020): 631–37. http://dx.doi.org/10.1515/opag-2020-0065.
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AbstractThe spatial distribution of urban vegetation cover is strongly related to climatological conditions, which play a vital role in urban cooling via shading and reducing ground surface temperature and effective strategy in mitigation urban heat island. Based on the Landsat satellite images, the quantitative normalized difference vegetation index (NDVI) was spatially mapped at two times for each year during 2008, 2013, 2019 in Baghdad. The NDVI values ranged from −1 to +1 with considering values larger than 0.2 indicate the dense healthy vegetation. In this study, the fractional areas of NDVI >0.2 were computed with their percentage. The responses of the NDVI during the growing seasons to two climate indices (i.e., air temperature and precipitation) were investigated. These climatic data obtained from the Iraqi Meteorological Organization and Seismology for the aforementioned years were used to explore the potential correlations between seasonal NDVI and above climate variables. The result shows that NDVI-derived vegetation growth patterns were highly correlated with their recording during the current growth seasons.
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Montgomery, Sarah M., Brandi B. Karisch, and Joby Czarnecki. "188 Comparison of Nutritive Value Parameters to Normalized Difference Vegetation Index by Means of Unmanned Aerial Vehicle." Journal of Animal Science 100, Supplement_3 (September 21, 2022): 85. http://dx.doi.org/10.1093/jas/skac247.166.
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Abstract Remote sensing has opened doors for grazing management by allowing producers to monitor forage in a more efficient way. The objective of this study was to observe the relationship between the normalized vegetation index (NDVI) from images taken by an unmanned aerial vehicle (UAV) to nutritive value parameters. This project took place at the H. H. Leveck Animal Research Center located in Starkville, MS where 9, 2-hectare pastures planted in Marshall annual ryegrass (Lolium multiflorum) were utilized. Each 2-hectare pasture was subdivided into 0.20-hectare sections where forage samples were collected from 3, 1/4 of a meter quadrat every 14-days. Images were taken with UAV over a 76-day grazing period every 14-days. Images from the UAV were captured at 121.9 m above ground level using a DJI inspire 2 rotor wing fitted with a MicaSense RedEdge camera (MicaSense; Seattle, WA). Images were processed using Pix4D (Pix4D SA; Prilly, Switzerland) and analyzed in QGIS (QGIS; OSGeo; Finland). Raster calculator in QGIS was utilized to calculate NDVI values. Forage samples were analyzed using NIR for crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), relative forage quality (RFQ), and invitro dry matter digestibility (IVDMD) and correlated to NDVI values. Regression analysis was conducted in R (RStudio, PBC) using the package easynls (easynls-package) procedure within package ggplot2 (ggplot2-package). Regression models were obtained through a backward-elimination technique. Neutral detergent fiber (r2 = 0.27) had the best correlated relationship to NDVI compared with CP (r2 = 0.21), ADF (r2 = 0.25), RFQ (r2 = 0.18), and IVDMD (r2 = 0.22). As NDVI values increased, CP, RFQ, and IVDMD increased while ADF and NDF decreased. There was a weak correlation between all nutritive value parameters and NDVI values. In conclusion, weak relationships indicate that NDVI values may not be the best predictor of nutritive value.
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Shippert, Margaret M., Donald A. Walker, Nancy A. Auerbach, and Brad E. Lewis. "Biomass and leaf-area index maps derived from SPOT images for Toolik Lake and Imnavait Creek areas, Alaska." Polar Record 31, no. 177 (April 1995): 147–54. http://dx.doi.org/10.1017/s0032247400013644.
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AbstractA new emphasis on understanding natural systems at large spatial scales has led to an interest in deriving ecological variables from satellite reflectance images. The normalized difference vegetation index (NDVI) is a measure of canopy greenness that can be derived from reflectances at near-infrared and red wavelengths. For this study we investigated the relationships between NDVI and leaf-area index (LAI), intercepted photosynthetically active radiation (IPAR), and biomass in an Arctic tundra ecosystem. Reflectance spectra from a portable field spectrometer, LAI, IPAR, and biomass data were collected for 180 vegetation samples near Toolik Lake and Imnavait Creek, Alaska, during July and August 1993. NDVI values were calculated from red and near-infrared reflectances of the field spectrometer spectra. Strong linear relationships are seen between mean NDVI for major vegetation categories and mean LAI and biomass. The relationship between mean NDVI and mean IPAR for these categories is not significant. Average NDVI values for major vegetation categories calculated from a SPOT image of the study area were found to be highly linearly correlated to average field NDVI measurements for the same categories. This indicates that in this case it is appropriate to apply equations derived for field-based NDVI measurements to NDVI images. Using the regression equations for those relationships, biomass and LAI images were calculated from the SPOT NDVI image. The resulting images show expected trends in LAI and biomass across the landscape.
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Towers, Pedro C., and Carlos Poblete-Echeverría. "Effect of the Illumination Angle on NDVI Data Composed of Mixed Surface Values Obtained over Vertical-Shoot-Positioned Vineyards." Remote Sensing 13, no. 5 (February 25, 2021): 855. http://dx.doi.org/10.3390/rs13050855.
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Accurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite, or aerial imagery with moderate-resolution capture information at nadir of pixels whose values are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI values for each fraction are considerably different. On a VSP vineyard, the illumination direction for each specific row orientation depends on the relative position of sun and earth. Respective proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth, but canopy fraction is independent of these variations. The focus of this study is the interaction of illumination direction with canopy orientation, and the corresponding effect on integrated NDVI. The results confirm that factors that intervene in determining the direction of illumination on a VSP will alter the integrated NDVI value. Shading induced considerable changes in the NDVI proportions affecting the final integrated NDVI value. However, the effect of shading decreases as the row orientation approaches the solar path. Therefore, models of biophysical parameters using moderate-resolution imagery should consider corrections for variations caused by factors affecting the angle of illumination to provide more general solutions that may enable canopy data to be obtained from mixed, integrated vine NDVI.
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Kizilgeci, Ferhat, Mehmet Yildirim, Mohammad Sohidul Islam, Disna Ratnasekera, Muhammad Aamir Iqbal, and Ayman EL Sabagh. "Normalized Difference Vegetation Index and Chlorophyll Content for Precision Nitrogen Management in Durum Wheat Cultivars under Semi-Arid Conditions." Sustainability 13, no. 7 (March 26, 2021): 3725. http://dx.doi.org/10.3390/su13073725.
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To impart sustainability to modern intensive farming systems, environmental pollution caused by nitrogenous fertilizers in needs to be reduced by optimizing their doses. To estimate the grain yield and nutrtional quallity of wheat, the normalized difference vegetation index (NDVI) and chlorophyll content (SPAD) are potential screening tools to identify the N deficiency and screen out the promising cultivars. The two-year field study was comprised with five levels of nitrogen (N) (control, 50, 100, 150 and 200 kg N ha−1) and two durum wheat genotypes (Sena and Svevo). The experimental design was split-plot, in which N levels were placed in the main plots, while wheat genotypes were arranged in sub-plots. To predict the yield and quality traits, the NDVI and SPAD values recorded at heading, anthesis and milky growth stages were taken as response variables. The results revealed that N fertilization significantly influenced the SPAD and NDVI attributed traits of durum wheat, except NDVI at milky stage (NDVI-M) during the first year. The maximum value of NDVI was recorded by 150 kg N ha−1, while control treatment gave the minimum value. The grain yield was increased with the increasing dose of the N up to 100 kg N ha−1 (4121 kg ha−1), and thereafter, it was declined with further increased of N levels. However, the variation between the genotypes was not significant, except NDVI and SPAD values at the milky stage. The genotype Svevo had the highest NDVI values at all growth stages, while the genotype Sena recorded the maximum SPAD values during both years. Similarly, the N levels significantly influenced the quality traits (protein, wet gluten, starch test weight and Zeleny sedimentation) of both genotypes. The highly significant relationship of SPAD and NDVI with the grain yield and yield attributes showed their reliability as indicators for determining the N deficiency and selection of superior wheat genotypes for ensuring food security under climate change scenario.
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Tian, Haifeng, Ni Huang, Zheng Niu, Yuchu Qin, Jie Pei, and Jian Wang. "Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm." Remote Sensing 11, no. 7 (April 5, 2019): 820. http://dx.doi.org/10.3390/rs11070820.
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Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.
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Wang, Yiting, Yuanyuan Chen, Pengfei Li, Yinggang Zhan, Rui Zou, Bo Yuan, and Xiaode Zhou. "Effect of Snow Cover on Detecting Spring Phenology from Satellite-Derived Vegetation Indices in Alpine Grasslands." Remote Sensing 14, no. 22 (November 12, 2022): 5725. http://dx.doi.org/10.3390/rs14225725.
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The accurate estimation of phenological metrics from satellite data, especially the start of season (SOS), is of great significance to enhance our understanding of trends in vegetation phenology under climate change at regional or global scales. However, for regions with winter snow cover, such as the alpine grasslands on the Tibetan Plateau, the presence of snow inevitably contaminates satellite signals and introduces bias into the detection of the SOS. Despite recent progress in eliminating the effect of snow cover on SOS detection, the mechanism of how snow cover affects the satellite-derived vegetation index (VI) and the detected SOS remains unclear. This study investigated the effect of snow cover on both VI and SOS detection by combining simulation experiments and real satellite data. Five different VIs were used and compared in this study, including four structure-based (i.e., NDVI, EVI2, NDPI, NDGI) VIs and one physiological-based (i.e., NIRv) VI. Both simulation experiments and satellite data analysis revealed that the presence of snow can significantly reduce the VI values and increase the local gradient of the growth curve, allowing the SOS to be detected. The bias in the detected SOS caused by snow cover depends on the end of the snow season (ESS), snow duration parameters, and the snow-free SOS. An earlier ESS results in an earlier estimate of the SOS, a later ESS results in a later estimate of the SOS, and an ESS close to the snow-free SOS results in small bias in the detected SOS. The sensitivity of the five VIs to snow cover in SOS detection is NDPI/NDGI < NIRv < EVI2 < NDVI, which has been verified in both simulation experiments and satellite data analysis. These findings will significantly advance our research on the feedback mechanisms between vegetation, snow, and climate change for alpine ecosystems.
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Arslan, İbrahim, Mehmet Topakcı, and Nusret Demir. "Monitoring Maize Growth and Calculating Plant Heights with Synthetic Aperture Radar (SAR) and Optical Satellite Images." Agriculture 12, no. 6 (June 1, 2022): 800. http://dx.doi.org/10.3390/agriculture12060800.
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The decrease in water resources due to climate change is expected to have a significant impact on agriculture. On the other hand, as the world population increases so does the demand for food. It is necessary to better manage environmental resources and maintain an adequate level of crop production in a world where the population is constantly increasing. Therefore, agricultural activities must be closely monitored, especially in maize fields since maize is of great importance to both humans and animals. Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical satellite images were used to monitor maize growth in this study. Backscatter and interferometric coherence values derived from Sentinel-1 images, as well as Normalized Difference Vegetation Index (NDVI) and values related to biophysical variables (such as Leaf Area Index (LAI), Fraction of Vegetation Cover (fCover or FVC), and Canopy Water Content (CW)) derived from Sentinel-2 images were investigated. Sentinel-1 images were also used to calculate plant heights. The Interferometric SAR (InSAR) technique was applied to calculate interferometric coherence values and plant heights. For the plant height calculation, two image pairs with the largest possible perpendicular baseline were selected. Backscatter, NDVI, LAI, fCover, and CW values were low before planting, while the interferometric coherence values were generally high. Backscatter, NDVI, LAI, fCover, and CW values increased as the maize grew, while the interferometric coherence values decreased. Among all Sentinel-derived values, fCover had the best correlation with maize height until maize height exceeded 260 cm (R2 = 0.97). After harvest, a decrease in backscatter, NDVI, LAI, fCover, and CW values and an increase in interferometric coherence values were observed. NDVI, LAI, fCover, and CW values remained insensitive to tillage practices, whereas backscatter and interferometric coherence values were found to be sensitive to planting operations. In addition, backscatter values were also sensitive to irrigation operations, even when the average maize height was about 235 cm. Cloud cover and/or fog near the study area were found to affect NDVI, LAI, fCover, and CW values, while precipitation events had a significant impact on backscatter and interferometric coherence values. Furthermore, using Sentinel-1 images, the average plant height was calculated with an error of about 50 cm.
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Wang, Yun-wen, Bruce L. Dunn, Daryl B. Arnall, and Pei-sheng Mao. "Use of an Active Canopy Sensor and SPAD Chlorophyll Meter to Quantify Geranium Nitrogen Status." HortScience 47, no. 1 (January 2012): 45–50. http://dx.doi.org/10.21273/hortsci.47.1.45.
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This research was conducted to investigate the potentials of normalized difference vegetation index (NDVI), a Soil-Plant Analyses Development (SPAD) chlorophyll meter, and leaf nitrogen (N) concentration [% dry matter (DM)] for rapid determination of N status in potted geraniums (Pelargonium ×hortorum). Two F1 cultivars were chosen to represent a dark-green leaf cultivar, Horizon Deep Red, and a light-green leaf cultivar, Horizon Tangerine, and were grown in a soilless culture system. All standard 6-inch (15.24-cm) pots filled with a medium received an initial top-dress application of 5 g controlled-release fertilizer (15N–9P–12K), then plants were supplemented with additional N in the form of urea at 0, 50, 100, or 200 mg·L−1 N every few days to produce plants ranging from N-deficient to N-sufficient. The NDVI readings of individual plants from a NDVI pocket sensor developed by Oklahoma State University were collected weekly until the flowering stage. Data on flower traits, including number of pedicels (NOP), number of full umbels per pot (NOFU), total number of flowers per pot (TNF), number of flowers per pedicel (NOF), and inflorescences diameter (IFD), were collected 3 months after initial fertilizer treatment. After measuring flower traits, pedicels were removed from each pot, and SPAD value, NDVI, and leaf N concentration (g·kg−1 DM) were measured simultaneously. Cultivar and N rate significantly affected all but two flower and one N status parameters studied. The coefficient of determination R2 showed that NOP, NOFU, and TNF traits were more related to the N rates and the status parameters studied for ‘Horizon Tangerine’ than for ‘Horizon Deep Red’. For the latter cultivar, NOP and TNF traits were highly related to NDVI and SPAD values than N rates and leaf N content parameters. Correlation analysis indicated that the NDVI readings (R2 = 0.848 and 0.917) and SPAD values (R2 = 0.861 and 0.950) were significantly related to leaf N content (g·kg−1 DM) between cultivars. However, sensitivity of the NDVI and chlorophyll values to N application rate in geranium was slightly less than leaf N content. Strong correlations (R2 = 0.974 and 0.979, respectively) between NDVI and SPAD values were found within cultivars. The results demonstrated NDVI and SPAD values can be used to estimate N status in geranium. Because the pocket NDVI sensor will be cheaper than the SPAD unit, it has an advantage in determining N content in potted ornamentals.
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Yao, Yonghui, and Lulu Cui. "Vegetation Dynamics in the Qinling-Daba Mountains through Climate Warming with Land-Use Policy." Forests 13, no. 9 (August 26, 2022): 1361. http://dx.doi.org/10.3390/f13091361.
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The Qinling-Daba Mountains in central China (also known as the north–south transitional zone) comprise an ideal area to study land cover change, climate change, and human activities. The normalized difference vegetation index (NDVI) change and associated driving factors are highly sensitive to vegetation cover change. To discover the long-term vegetation trends in the transition zone and determine the driving factors of NDVI change in recent decades, this study analyzed the NDVI variation trend and its spatial variation with elevation, slope, and land-use type based on annual growing season NDVI data from 1990–2019 (Landsat 30 m; Google Earth Engine). The results show that NDVI values in the Qinling-Daba Mountains significantly increased and experienced a dynamic change process, involving an initial decrease and subsequent increase over this time period. The period of 2000–2005 showed a remarkable increasing stage of the NDVI in the transition zone. Such NDVI changes are sensitive to elevation and slope. For example, areas at elevations < 1500 m or with slopes of 5°–25° exhibited a stronger rate of NDVI increase than in other places. The NDVI change was also found to be positively affected by human land use and climate warming, both of which had a stronger impact than precipitation. The area with rapid NDVI growth was also the region with the greatest impact of human cropland and host to the Grain-for-Green project. This demonstrates that human land use has had a positive impact on the NDVI change in recent decades, although urbanization had led to a decrease in the NDVI in surrounding areas. Land-use policies have contributed to the large increase in NDVI values, especially those for forest conservation and expansion programs such as the Grain-for-Green project.
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Zhou, Huailin, Guangsheng Zhou, Xingyang Song, and Qijin He. "Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices." Remote Sensing 14, no. 3 (January 26, 2022): 584. http://dx.doi.org/10.3390/rs14030584.
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A variety of spectral vegetation indices (SVIs) have been constructed to monitor crop water stress. However, their abilities to reflect dynamic canopy water content (CWC) and vegetation water content (VWC) during the growing season have not been concurrently examined, and the underlying mechanisms remain unclear, especially in relation to soil drying. In this study, a field experiment was conducted and designed with various irrigation regimes applied during two consecutive growing seasons of maize. The results showed that CWC, VWC, and the SVIs exhibited obvious trends of first increasing and then decreasing within a growing season. In addition, VWC was allometrically related to CWC across the two growing seasons. A linear relationship between the five SVIs and CWC occurred within a certain CWC range (0.01–0.41 kg m−2), while the relationship between these SVIs and VWC was nonlinear. Furthermore, the five SVIs indicated critical values for VWC, and these values were 1.12 and 1.15 kg m−2 for the water index (WI) and normalized difference water index (NDWI), respectively; however, the normalized difference infrared index (NDII), normalized difference vegetation index (NDVI), and optimal soil-adjusted vegetation index (OSAVI) had the same critical value of 0.55 kg m−2. Therefore, in comparison to the NDII, NDVI, and OSAVI, the WI and NDWI better reflected the crop water content based on their sensitives to CWC and VWC. Moreover, CWC was the most important direct biotic driver of the dynamics of SVIs, while leaf area index (LAI) was the most important indirect biotic driver. VWC was a critical indirect regulator of WI, NDWI, NDII, and OSAVI dynamics, whereas vegetation dry mass (VDM) was the critical indirect regulator of NDVI dynamics. These findings may provide additional information for estimating agricultural drought and insights on the impact mechanism of soil water deficits on SVIs.