Academic literature on the topic 'NDVI values'
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Journal articles on the topic "NDVI values"
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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.
Dissertations / Theses on the topic "NDVI values"
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Näsström, Rickard. "Reaching the 2014 UN New York Declaration on Forests Goals, using satellites to monitor global value chains." Thesis, Stockholms universitet, Kulturgeografiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-128585.
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This master thesis in geography investigates how remote sens- ing can be used in Transnational Corporations (TNC) global Corporate Social Responsibility (CSR) initiatives. The study aims to delineate an accurate method in remote sensing to be used to monitor deforestation in global value chains. Research questions asked are 1) What are the current monitoring practises used by TNCs to monitor global value chains? 2) Which is the most user-friendly and accurate remote sensing technique to map deforestation? 3) How can remote sensing successfully be implemented in TNCs CSR-initiatives? The study is approached from two perspectives, building on theories of value chains, and qualitative methods to answer the first research question. While the second question is a method study, investigating how well a spectral approach versus a contextual approach can map deforest- ation in Landsat scenes. The results are compared with Global Forest Watch (GFW), and the highest accuracy were acquired from the WICS (Window Indipendent Context Segmentation) technique. Conclusions includes that remote sensing can be used in CSR initiatives, to establish a baseline level or as a fifth dimen- sion in a score sheet approach. However, inconclusive mapping of value chains are a big hinder today.
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Bradford, Jessica. "Examining Culex tarsalis (Diptera: Culicidae) population changes with satellite vegetation index data." Kansas State University, 2014. http://hdl.handle.net/2097/17139.
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Master of Public HealthDepartment of Diagnostic Medicine/Pathobiology
Michael W. Sanderson
A zoonotic disease is any disease or infection that is naturally transmissible from vertebrate animals to humans. Over 200 zoonoses have been described (Zoonoses and the Human-Animal-Ecosystems Interface, 2013). Many zoonotic viruses are arboviruses, viruses transmitted by an infected, blood-sucking, arthropod vector (Hunt, 2010). There are several endemic arboviruses in the United States; some foreign arboviruses, such as Rift Valley fever (RVF) virus, are potential bioterrorism agents (Dar, 2013). Arboviruses, both endemic and foreign, threaten public health (Gubler, 2002) and therefore disease surveillance, vector control and public education are all vital steps in minimizing arboviral disease impact in the United States. Mosquito-borne disease threats, such as West Nile virus and Rift Valley fever, are constant concerns in the United States and globally. Current strategies to prevent and control mosquito-borne diseases utilize vector distribution, seasonal and daylight timing, and variation in population numbers. Climate factors, such as availability of still water for development of immature mosquitoes, shade, and rainfall, are known to influence population dynamics of mosquitoes. Using 1995-2011 mosquito population surveillance data from Fort Riley, Kansas, we compared population numbers of Culex tarsalis (Diptera: Culicidae), a vector of several arboviruses including West Nile virus and potentially Rift Valley fever, to a satellite-derived index of climate, the Normalized Difference Vegetation Index (NDVI) anomaly. No correlation between the population numbers and NDVI anomaly was observed, which contrasts with results from similar analyses in other locations. These findings suggest a need for continued investigation into mosquito population dynamics in additional ecological regions of the United States to better describe the heterogeneity of environment-population relationships within and among mosquito species.
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Llale, Semakaleng. "Climate variability: Human management response to environmental changes in Touws River valley and Makolokwe." University of the Western Cape, 2020. http://hdl.handle.net/11394/7296.
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Magister Artium - MAClimate has been changing significantly around the globe; hence climate variability is of great interest to researchers. The changes in climate have caused variances in rainfall and temperature, both elements of paramount importance in farming, whether commercial or communal farming. As these fluctuations in temperature and rainfall occur, they cause direct impacts on different livelihoods, fauna and flora. The aim of this thesis is to investigate the human management responses of farmers in two different contexts of communal farming (Makolokwe) and commercial farming (Touws River valley), with a focus investigation on the adaptation and coping strategies of the farmers, as well as spatial analysis of the vegetation and rainfall variability. Farmers were asked to discuss climate and adaptation based on the rainfall data available as well as far as they could remember the occurrence of changes. Rainfall data was available between 1988 and 2017 for Touws River, while the data utilised for Makolokwe was available between 1928 and 2016. The link between the local knowledge of the farmers and scientific knowledge is an important aspect of this research. The Normalised Difference Vegetation Index (NDVI) was used to analyse the vegetation changes on a temporal and spatial scale in the context of Makolokwe and Touws River valley respectively. The differing variations in climate variability and change experienced by the two farming communities are placed alongside an exploration of the adaptation and coping measures which are put in place by farmers as a response to the changes evident in climate, as it allows for better and thorough understanding of the occurring changes in the two communities. The study found that perceptions about climate variability vary in the two communities although there are some common factors. Farmers’ perceptions about climate variability are drawn from their own observations at a local level as well as knowledge from the media regarding terms such as El Niño and drought. Farmers in both communities indicated that they experienced insufficient rain in the winter months which had an impact on the grazing areas and the management of the livestock. These months also threatened livelihoods, especially for farmers who depend on their livestock for their livelihood, in particular communal farmers. Perceptions of factors such as decreasing grazing and vegetation in their environments have led to the adoption of adaptation and coping strategies on the part of farmers. Commercial farmers have more choices in this regard than communal farmers, such as converting to game farming. Common coping strategies include: (1) farmers have had to subsidise and use alternative food sources for the livestock, (2) livestock numbers have been reduced in order to adapt to climate variability, with an impact on livelihoods (3) farmers have had to rely on their hope and faith that things will get better. Planning for climate variability is challenging for land managers. Knowledge and access to resources is therefore essential in ensuring that farmers are kept on track with the changing environment.
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Daly, Fiona Frances Margaret. "The effect of diet on the nutrition and production of merino ewes in the arid shrublands of Western Australia." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/570.
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For the Arid Shrublands of Western Australia (WA) knowledge is limited on what sheep eat and how nutritious their diets are. A study was undertaken on two stations near Yalgoo (28º18’S 116º38’E) in WA, from November 2005 to December 2007. Station 1 (28º39’S 116º18’E) used a flexible rotational grazing management system (RGS), moving 3000-4000 Merino sheep every 3 – 6 weeks through a choice of 20 paddocks. Station 2 (28º18’S 116º42’E) used a flexible continuous grazing management system where small mobs (500 sheep) stayed in paddocks all year, until shearing. Two paddocks on Station 2 were chosen to represent paddocks with high (CGS-G) and low (CGS-P) feed value.A total of 300 Merino hogget (18 months old) ewes were randomly selected from the stations. One hundred and fifty sheep from each station were selected and separated into three mobs of 50 sheep by stratifying live weights. The selected sheep were allocated to either of the two paddocks on Station 2 or the single rotating mob on Station 1. Therefore there were a total of 100 sheep, 50 from each station, on each of the two paddocks on Station 2 and the one rotating mob on Station 1.Throughout the study period sheep live weights, body condition scores (BCS) and wool production were measured and related to plant photosynthetic activity (derived from Normalised Difference Vegetation Index - NDVI), and dietary energy, protein and digestibility (determined from faecal NIRS calibrations). A DNA reference data bank of some common native plant species was established and then used as a library to identify plant species in sheep faeces and thus provide information on variations in diet composition over the study period. Plant nutritional content was also measured and compared to climatic changes and sheep nutrition.Over the study period Merino ewe live weights, wool production, faecal samples and native plant leaf material were collected and analysed from each of the three management treatments (RGS, CGS-G, CGS-P). Wool production measurements included wool length, strength and fibre diameter, including position of breaks, minimum and maximum diameter along the staple of midside samples. Oven dried plant and faecal samples were ground and subsequently analysed for proximate composition. Plant samples were further analysed for mineral contents and 24 h in vitro gas production (GP) using the rumen buffer gas fermentation technique. Organic matter digestibility (OMD) and metabolisable energy (ME) content of the plants were determined using 24 h net gas production. Faecal near infrared reflectance spectroscopy (fNIRS) calibrations, developed by Curtin University of Technology and ChemCentre WA, were used to predict the nutritional attributes of sheep diets.Sheep production was found to be affected by rainfall, seasons, management and differing blood lines. In 2006, live weights, BCS and wool fibre diameter increased in response to high summer rainfall. Lower rainfall in 2007 resulted in variable, but generally less animal production with lower live weights, BCS and wool fibre diameter. Management decisions to avoid mating in 2006 on CGS; and agistment for sheep on RGS at the end of 2006 resulted in better sheep production results. Sheep originally sourced from Station 2 generally had higher live weights than sheep sourced from Station 1, suggesting a difference in bloodlines.Faecal DNA provided useful information regarding diet selection and diversity of sheep grazing on the Arid Shrublands of WA. Of the species that were DNA profiled, the sheep ate Acacia saligna, Aristida contorta, Atriplex spp., Enchylaena tomentosa, Frankenia sp., Ptilotus obovatus, Rhagodia eremaea and Scaevola spinescens in 2006 whilst in 2007; the sheep consumed A. saligna, A. contorta, Atriplex spp., Eremophila forrestii, Enneapogon caerulescens, Frankenia spp., Maireana spp., Ptilotus obovatus, Rhagodia eremaea, Solanum lasiophyllum and Stipa elegantissima. However, there were 28 amplified bands in 2006 and 51 in 2007 that did not conclusively match any of the reference plant species. This indicates that the sheep were consuming diets that contained more species than what was analysed in this study. Faecal DNA results indicated a decrease in the diversity of the diets selected by the sheep during summer, which coincided with a decrease in animal production.Native plants were found to be low in OMD and ME, but high in crude protein (CP), and variable in mineral content. Sheep were able to select diets adequate in OMD, ME and CP for maintenance requirements, and low in tannins and phenolics, although continuous drought conditions resulted in reduced production, indicating that the sheep were not getting adequate nutrition to meet their growth requirements. The use of fNIRS provided more useful information about the quality of the diet of the sheep than nutritionally profiling individual plants. NDVI was found to be related to dietary OMD and wool fibre diameter changes along the staple.Overall, the effects of management seemed to be secondary to the effects of climate on sheep production and nutrition. The statistical accuracy of results was low; however, the use of advanced technologies to explore relationships between climate, plant nutritional profiles and animal production and nutrition has provided an expansion of knowledge of sheep nutrition in the region. This extra knowledge may help land owners in the region to make more sustainable management decisions concerning livestock management and grazing pressures on native pastures.
Book chapters on the topic "NDVI values"
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Puteh, Suhaimi, Nurul Fadhilah Mohamed Rodzali, Anwar P. P. Abdul Majeed, Ismail Mohd Khairuddin, Zelina Zaiton Ibrahim, and Mohd Azraai Mohd Razman. "Classification of Capsicum Frutescens Health Condition Through Features Extraction from NDVI Values Using Image Processing." In Lecture Notes in Mechanical Engineering, 414–23. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4803-8_41.
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Alam, Asraful, Arijit Ghosh, and Lakshminarayan Satpati. "Investigation of Urban Environment Using Tasseled Cap Transformation." In Advances in Environmental Engineering and Green Technologies, 88–96. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-5027-4.ch005.
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Urban settlements have more complex environments, in unremitting fruition, where most of the world population lives. Most of the cities in developing countries have been developed without a rationale, and the life conditions are repeatedly insufferable. For this research work, NDVI is particularly used to assess the status of vegetation cover. Tasseled cap is another index that creates three band images for this study. Brightness, greenness, wetness are the three bands that represent the area under consideration. The present study aims particularly at comparing high NDVI area and greenness values given by tasseled cap and low NDVI values and high brightness values and status of urban environment. Based on the overlapping of tasseled cap image, an NDVI image is observed in which most of the area of healthy vegetation is located in the north west and south east part of Kharagpur city, which extended from south west to north east and north to south respectively.
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Santos, Arthur, Fernando Santil, and Claudionor Silva. "The Use of NDVI and NDBI to Provide Subsidies to Public Manager’s Decision Making on Maintaining the Thermal Comfort in Urban Areas." In Vegetation Index and Dynamics. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.97350.
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The use of physical indexes such as NDVI (Normalized Difference Vegetacion Index) and NDBI (Normalized Difference Built-up Index), related to the variation of Surface Temperature (LST), have been widely used as support for mapping and monitoring land use and occupation, mainly in urban centers, due to, among other factors, changes in the energy balance and, consequently, increase heat of cities. Thus, this study approaches the urban space of the municipality of Paracatu, Minas Gerais (MG) and aims to verify urban growth, through the variation of NDVI, NDBI and LST, between the years 1990 and 2019 by using images of the LANDSAT-5 and LANDSAT-8 satellites. As a final result, an urbanization map of the municipality was obtained, and it was possible to verify that these indexes were adequate to size the environmental impact caused by disordered urbanization, since the degradation of vegetation caused in the area was responsible for reducing and/or increasing the values recorded by the indexes. In addition, the results made it possible to identify areas with higher and lower temperature variations, causing the agility of decision-making and the development of projects that meet the peculiarities of each sector of the city.
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Martins Moreira, Rodrigo, and Maria Paula Cardoso Yoshii. "Integrating Google Earth Engine and Decametric Sentinel 2 Images for Analysis of Vegetation Pre and Post the Disaster at Brumadinho, Brazil." In Natural Hazards - New Insights [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108286.
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This paper presents the application of the normalized difference vegetation index to assess the vegetation dynamics for the period between years 2017 and 2021 at Brumadinho, MG, Brazil. The normalized difference vegetation index was calculated using a Google Earth Engine script applying Sentinel 2 data with a spatial resolution of 10 meters, to quantify the extent of the affected area and assess the vegetation dynamic after the disaster. The Dwass-Steel-Crichlow-Fligner test for nonparametric data was used for a pairwise comparison between years and the confidence interval was calculated using bootstrap with 9999 repetitions. The total area affected by the dam brake was 2662 ha. The NDVI values presented a statistically significant decrease from 2017 to 2019, with little increase until 2021. Mean NDVI values were 0.314003 [0.31028; 0.317564], 0.339887 [0.336591; 0.343231], 0.145814 [0.144004; 0.1476], 0.1495 [0.147676; 0.15128], and 0.15572 [0.153727; 0.15774] for 2017–2021, respectively. According to the results, we conclude that the vegetation in the affected area did not fully recover.
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Alexander Correa-Muñoz, Nixon, and Carol Andrea Murillo-Feo. "The Potential of Remote Sensing to Assess Conditioning Factors for Landslide Detection at a Regional Scale: The Case in Southeastern Colombia." In Slope Engineering [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94251.
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This landslide detection research applied remote sensing techniques. Morphometry to derive both DEM terrain parameters and land use variables. SAR interferometry (InSAR) for showing that InSAR coherence and InSAR displacement obtained with SRTM DEM 30 m resolution were strongly related to landslides. InSAR coherence values from 0.43 to 0.66 had a high association with landslides. PS-InSAR allowed to estimate terrain velocities in the satellite line-of-sight (LOS) in the range − 10 to 10 mm/year concerning extremely slow landslide displacement rates. SAR polarimetry (PolSAR) was used over L-band UAVSAR quad-pol data, obtaining the scattering mechanism of volume and surface retrodispersion more associated with landslides. The optical remote sensing with a multitemporal approach for change detection by multi-year Landsat (5, 7 and 8)-NDVI, showed that NDVI related to landslides had values between 0.42 and 0.72. All the information was combined into a multidimensional grid product and crossed with training data containing a Colombian Geologic Service (CGS) landslide inventory. A detection model was implemented using the Random Forest supervised method relating the training sample of landslides with multidimensional explanatory variables. A test sample with a proportion of 70:30 allowed to find the accuracy of detection of about 70.8% for slides type.
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Begum, Masuma, Niloy Pramanick, Anirban Mukhopadhyay, and Sayani Datta Majumdar. "Scenarios of the Tropical Dry Forest of Purulia District West Bengal." In Practice, Progress, and Proficiency in Sustainability, 254–67. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0014-9.ch013.
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In this chapter, satellite images of the years 1995, 2005, and 2015 of LANDSAT have been used. After pre-processing (geometric correction and atmospheric correction using FLAASH, LULC change dynamics have been assessed to estimate the changes in total forest cover in Purulia district through an unsupervised K-means classification scheme. To evaluate the health status, vegetation indices, namely NDVI, SAVI, and CVI, have been used. The increase in NDVI, SAVI, and CVI values was inferred as no significant degradation of Purulia forest cover. Moreover, future scenarios have been predicted by implementing a CA-MARKOV model. Using the land cover map of 1995 as the base map, and from 1995 to 2005 as training data, a land cover map of 2015 has been generated which in turn validated by the actual land cover of 2015. After validation, prediction of land cover was possible for the years 2035 and 2050. The prediction suggested that forest area will increase by approximately 4% from 2015 to 2035 and by 3% from 2035 to 2050.
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Pasko, Olga, Natalia Staurskaya, Olga Sergeevna Tokareva, Pedro Cabral, Nadezhda Anatolyevna Lebedeva, and Saif Mohanad Majid. "Analysis of the Vegetation State of the Territory of Central Iraq Using Landsat Data." In Handbook of Research on Agricultural Policy, Rural Development, and Entrepreneurship in Contemporary Economies, 484–503. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9837-4.ch024.
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In agricultural production, there is a change in the properties of soils and the problem of land degradation is rising. It is especially important for Iraq, whose economic well-being is in oil and agriculture. The objects of authors' study are the territories of Central Iraq; the subject of authors' research is the temporary-territorial variability of vegetation. This chapter analyzes the vegetation dynamics of the five provinces' territory of Central Iraq by determining the values of the Normalized Difference Vegetation Index (NDVI) from remote sensing data in the period from 2003 to 2017. Regional features are reflected in the variability and change rate of these processes and in the valley plots occupied by vegetation of different state classes, from the total area of the province. The differences in the state classes of vegetation on the territories of Central Iraq are conditioned not by natural, but by other reasons, in particular, by the state of meliorative systems.
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Ragán, Péter, László Duzs, Zsuzsanna Dorogi, Ágnes Törő, and Tamás Rátonyi. "The effects of the soil tillage and the fertilization on the NDVI values of the maize plant." In Abstract book of the 18th Alps-Adria Scientific Workshop, 132–33. Szent István Egyetemi Kiadó Nonprofit Kft., 2019. http://dx.doi.org/10.34116/nti.2019.aa.57.
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Alemaw, Berhanu F., Thebeyame Ronald Chaoka, and Brigton Munyai. "Remote Sensing-Based Evapotranspiration Modelling for Agricultural Water Management in the Limpopo Basin." In Environmental Information Systems, 249–86. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7033-2.ch013.
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The study was motivated by the need to determine the spatial variation of ET and to test the applicability of RS based methods in arid to semi-arid climates with limited ground-based measurements. In this paper we present results of an effort of determining spatial actual evapotranspiration in the Limpopo basin, the Notwane subcatchment in the south-eastern part of Botswana, using remote sensing data from MODIS and Landsat Data sets. The Simplified Surface Energy Balance Index (S-SEBI) was applied to determine actual evapotranspiration using the seven bands of Landsat and MODIS surface reflectance and temperature channels. Three different dates were used to estimate ET from both Landsat and MODIS scenes. The estimated ET values from the two sensors show approximately equally comparable results. An assessment was also conducted to determine the factors influencing evapotranspiration. No strong correlation was identified for ET against the five factors investigated: Net radiation, NDVI, Surface Temperature, emissivity and surface albedo.
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Alemaw, Berhanu F., Thebeyame Ronald Chaoka, and Brigton Munyai. "Remote Sensing-Based Evapotranspiration Modelling for Agricultural Water Management in the Limpopo Basin." In Advances in Geospatial Technologies, 50–85. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3440-2.ch004.
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The study was motivated by the need to determine the spatial variation of ET and to test the applicability of RS based methods in arid to semi-arid climates with limited ground-based measurements. In this paper we present results of an effort of determining spatial actual evapotranspiration in the Limpopo basin, the Notwane subcatchment in the south-eastern part of Botswana, using remote sensing data from MODIS and Landsat Data sets. The Simplified Surface Energy Balance Index (S-SEBI) was applied to determine actual evapotranspiration using the seven bands of Landsat and MODIS surface reflectance and temperature channels. Three different dates were used to estimate ET from both Landsat and MODIS scenes. The estimated ET values from the two sensors show approximately equally comparable results. An assessment was also conducted to determine the factors influencing evapotranspiration. No strong correlation was identified for ET against the five factors investigated: Net radiation, NDVI, Surface Temperature, emissivity and surface albedo.
Conference papers on the topic "NDVI values"
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Amar, Haddad, Beldjazia Amina, Kadi Zahia, Redjaimia Lilia, and Rached-Kanouni Malika. "THE NORMALIZED DIFFERENCE VEGETATION INDEX AS AN INDICATOR OF DYNAMICS." In GEOLINKS Conference Proceedings. Saima Consult Ltd, 2021. http://dx.doi.org/10.32008/geolinks2021/b2/v3/27.
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Mediterranean ecosystems are considered particularly sensitive to climate change. Any change in climatic factors affects the structure and functioning of these ecosystems and has an influence on plant productivity. The main objective of this work is to characterize one of the Mediterranean ecosystems; the Chettaba forest massif (located in the North-East of Algeria) from a vegetation point of view and their link with monthly variations using Landsat 8 satellite images from five different dates (June 25, 2017, July 27, 2017, August 28, 2017, October 15, 2017). The comparison of NDVI values in Aleppo pine trees was performed using analysis of variance and the use of Friedman's non-parametric test. The Mann-Kendall statistical method was applied to the monthly distribution of NDVI values to detect any trends in the data over the study period. The statistical results of NDVI of Aleppo pine trees indicate that the maximum value is recorded in the month of June, while the lowest values are observed in the month of August where the species studied is exposed to periods of thermal stress.
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Golovastova, E. S., and Ie A. Dunaieva. "Convergence of NDVI values by remote sensing data and field measurements." In CURRENT STATE, PROBLEMS AND PROSPECTS OF THE DEVELOPMENT OF AGRARIAN SCIENCE. Federal State Budget Scientific Institution “Research Institute of Agriculture of Crimea”, 2019. http://dx.doi.org/10.33952/09.09.2019.160.
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Gulyanov, Yu A. "Correlation of the vegetation index (NDVI) and phytometric parameters at different stages of field crops development." In CURRENT STATE, PROBLEMS AND PROSPECTS OF THE DEVELOPMENT OF AGRARIAN SCIENCE. Federal State Budget Scientific Institution “Research Institute of Agriculture of Crimea”, 2020. http://dx.doi.org/10.33952/2542-0720-2020-5-9-10-123.
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The main goal of our research was to identify the relationship between the normalized difference vegetation index (NDVI) and the area of assimilation surface (AS) of spring wheat crops during the growing season, as well as to develop practical application of the findings. Throughout the growing season, the area of assimilation surface of T. aestivum increases much faster than the vegetation index NDVI. The smallest AS (282.7 m2/ha), which corresponded to 0.01 units of the NDVI (calculated factor) was observed during the tillering stage. It reaches its maximum values – 331.7–406.1–383.7 m2/ha (1.20–1.47–1.39 times higher) from stem elongation to the end of flowering. During the grain filling and maturation, these values decrease to 336.2 m2/ha but still are 1.19 times higher than the initial ones.
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Simon, Mihai. "NDVI VALUES IN DETERMINATION OF MEADOWS BIOMASS FOR QUANTITATIVE EVALUATION AND CARTOGRAPHY." In 18th International Multidisciplinary Scientific GeoConference SGEM2018. STEF92 Technology, 2018. http://dx.doi.org/10.5593/sgem2018v/6.4/s08.029.
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Tummala, Pragathi, M. Sobhana, and Sruthi Kakumani. "Predicting crop yield with NDVI and Backscatter values using Deep Neural Networks." In 2022 International Mobile and Embedded Technology Conference (MECON). IEEE, 2022. http://dx.doi.org/10.1109/mecon53876.2022.9751969.
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Rodionova, N. V. "Satellite monitoring of the environment in the area of the Iskitim coal mines in 2013–2020." In Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.39.39.042.
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The paper considers the use of multispectral data from the Landsat-8, Sentinel-2, Aqua and Terra satellites for monitoring pollution in the areas of open-pit coal mines in the Iskitim district of the Novosibirsk region for the period 2013–2020. The change in the values of the reflection coefficient (RC) from the surface and water bodies, the snow index NDSI during the snowmelt period, the change of NDVI in the summer, in the area of Kolyvansky and Vostochny coal mines and in the area of the Linevo village are considered. The dynamics of the aerosol optical thickness (AOT) changes, CO and CH4 concentrations in the atmosphere of the Iskitim district using the Giovanni data analysis and visualization system are shown.
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JOVANOVSKA, Gordana, Uğur AVDAN, and Nalan DEMİRCİOĞLU YILDIZ. "SPATIAL CHANGE ANALISYS BASED ON NDVI VALUES USING LANDSAT DATA: CASE STUDY IN TETOVO, MACEDONIA." In International Scientific Conference GEOBALCANICA. Geobalcanica Society, 2016. http://dx.doi.org/10.18509/gbp.2016.11.
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GHERASIM, Paul Marian, Mihai DIMA, and Ioana AGAPIE (MEREUȚĂ). ""Studing LST and NDVI Values for Suhi Non-Suhi Occupied by Constructions and Buildings: a Case Study of Iasi. "." In Air and Water – Components of the Environment 2022 Conference Proceedings. Casa Cărţii de Ştiinţă, 2022. http://dx.doi.org/10.24193/awc2022_11.
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In this paper we tried to study the values of radiant temperatures (Land Surface Temperature) and NDVI (Normalized Difference Vegetation Index) for areas occupied by buildings and green spaces. The area affected by the Urban Heat Island (UHI) was also determined. Study Area, Iasi, the largest city in eastern Romania, is geographically situated on latitude 47°12'N to 47°06'N and longitude 27°32'E to 27°40'E. LST is an estimate of ground temperature and is important to identify change in environment. An important parameter in global climate change is rapid urbanization which leads to an increase in Land Surface Temperature (LST). The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural zones due to urbanization. It also been found that night UHI is more powerful than day. At night the LST values for SUHI varies between 24.5°C-25.9°C, and during the day between 35°C-38.7°C. With the development of remote sensing technology, it has become an important approach to urban heat island research. MODIS and Landsat data were used to estimate the LST and NDVI. From the analysis of the images it can be seen that the temperatures in SUHI are lower where there are green spaces around the buildings, and temperatures are higher in the non-UHI area, where inside or around the green spaces there are surfaces built or covered with concrete. Statistical data show very average temperatures for areas affected by UHI, 37.8°C for daytime and 24.6°C for night.
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Bachoo, Asheer, and Sally Archibald. "Influence of Using Date-Specific Values when Extracting Phenological Metrics from 8-day Composite NDVI Data." In 2007 International Workshop on the Analysis of Multi-temporal Remote Sensing Images. IEEE, 2007. http://dx.doi.org/10.1109/multitemp.2007.4293044.
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Al-Shehhi, Maryam R., Rasha Saffarini, Ali Farhat, Nada K. Al-Meqbali, and Hosni Ghedira. "Evaluating the effect of soil moisture, surface temperature, and humidity variations on MODIS-derived NDVI values." In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6049889.
Full textReports on the topic "NDVI values"
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Manninen, Terhikki, and Pauline Stenberg. Influence of forest floor vegetation on the total forest reflectance and its implications for LAI estimation using vegetation indices. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361379.
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Recently a simple analytic canopy bidirectional reflectance factor (BRF) model based on the spectral invariants theory was presented. The model takes into account that the recollision probability in the forest canopy is different for the first scattering than the later ones. Here this model is extended to include the forest floor contribution to the total forest BRF. The effect of the understory vegetation on the total forest BRF as well as on the simple ratio (SR) and the normalized difference (NDVI) vegetation indices is demonstrated for typical cases of boreal forest. The relative contribution of the forest floor to the total BRF was up to 69 % in the red wavelength range and up to 54 % in the NIR wavelength range. Values of SR and NDVI for the forest and the canopy differed within 10 % and 30 % in red and within 1 % and 10 % in the NIR wavelength range. The relative variation of the BRF with the azimuth and view zenith angles was not very sensitive to the forest floor vegetation. Hence, linear correlation of the modelled total BRF and the Ross-thick kernel was strong for dense forests (R2 > 0.9). The agreement between modelled BRF and satellite-based reflectance values was good when measured LAI, clumping index and leaf single scattering albedo values for a boreal forest were used as input to the model.
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Broussard, Whitney, Glenn Suir, and Jenneke Visser. Unmanned Aircraft Systems (UAS) and satellite imagery collections in a coastal intermediate marsh to determine the land-water interface, vegetation types, and Normalized Difference Vegetation Index (NDVI) values. Engineer Research and Development Center (U.S.), October 2018. http://dx.doi.org/10.21079/11681/29517.
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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.
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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.
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Wang, Chih-Hao, and Na Chen. Investigating the Resilience of Accessibility to Emergency and Lifesaving Facilities under Natural Hazards. Mineta Transportation Institute, May 2022. http://dx.doi.org/10.31979/mti.2022.2126.
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Studying accessibility, including the resilience of city transportation networks, is critical to understand how these networks influence individuals’ mobility and lives. This study developed an analytical research framework to examine the resilience of accessibility to emergency and lifesaving facilities under the threats of natural hazards such as earthquakes and wildfires. With a cumulative-opportunity approach, the authors measured accessibility by counting emergency and lifesaving facilities (including parks, schools, hospitals, roads, and fire stations) that can be reached by driving at the census tract level in San Fernando Valley, CA. With the calculated accessibility, the authors run simulations to collect data showing what would happen if an area were affected by a selected disaster. They then used statistical analysis to identify those areas where accessibility is significantly reduced compared to the original status. A normalized difference accessibility index (NDAI) was further created to suggest plans and strategies to help those vulnerable areas through adding facilities/services or improving transportation infrastructure.