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Artículos de revistas sobre el tema "Vegetation monitoring"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 5 de marzo de 2023

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1

Khan, Asim, Warda Asim, Anwaar Ulhaq y Randall W. Robinson. "A deep semantic vegetation health monitoring platform for citizen science imaging data". PLOS ONE 17, n.º 7 (27 de julio de 2022): e0270625. http://dx.doi.org/10.1371/journal.pone.0270625.

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Automated monitoring of vegetation health in a landscape is often attributed to calculating values of various vegetation indexes over a period of time. However, such approaches suffer from an inaccurate estimation of vegetational change due to the over-reliance of index values on vegetation’s colour attributes and the availability of multi-spectral bands. One common observation is the sensitivity of colour attributes to seasonal variations and imaging devices, thus leading to false and inaccurate change detection and monitoring. In addition, these are very strong assumptions in a citizen science project. In this article, we build upon our previous work on developing a Semantic Vegetation Index (SVI) and expand it to introduce a semantic vegetation health monitoring platform to monitor vegetation health in a large landscape. However, unlike our previous work, we use RGB images of the Australian landscape for a quarterly series of images over six years (2015–2020). This Semantic Vegetation Index (SVI) is based on deep semantic segmentation to integrate it with a citizen science project (Fluker Post) for automated environmental monitoring. It has collected thousands of vegetation images shared by various visitors from around 168 different points located in Australian regions over six years. This paper first uses a deep learning-based semantic segmentation model to classify vegetation in repeated photographs. A semantic vegetation index is then calculated and plotted in a time series to reflect seasonal variations and environmental impacts. The results show variational trends of vegetation cover for each year, and the semantic segmentation model performed well in calculating vegetation cover based on semantic pixels (overall accuracy = 97.7%). This work has solved a number of problems related to changes in viewpoint, scale, zoom, and seasonal changes in order to normalise RGB image data collected from different image devices.

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2

Maxwald, Melanie, Markus Immitzer, Hans Peter Rauch y Federico Preti. "Analyzing Fire Severity and Post-Fire Vegetation Recovery in the Temperate Andes Using Earth Observation Data". Fire 5, n.º 6 (8 de diciembre de 2022): 211. http://dx.doi.org/10.3390/fire5060211.

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In wildfire areas, earth observation data is used for the development of fire-severity maps or vegetation recovery to select post-fire measures for erosion control and revegetation. Appropriate vegetation indices for post-fire monitoring vary with vegetation type and climate zone. This study aimed to select the best vegetation indices for post-fire vegetation monitoring using remote sensing and classification methods for the temperate zone in southern Ecuador, as well as to analyze the vegetation’s development in different fire severity classes after a wildfire in September 2019. Random forest classification models were calculated using the fire severity classes (from the Relativized Burn Ratio—RBR) as a dependent variable and 23 multitemporal vegetation indices from 10 Sentinel-2 scenes as descriptive variables. The best vegetation indices to monitor post-fire vegetation recovery in the temperate Andes were found to be the Leaf Chlorophyll Content Index (LCCI) and the Normalized Difference Red-Edge and SWIR2 (NDRESWIR). In the first post-fire year, the vegetation had already recovered to a great extent due to vegetation types with a short life cycle (seasonal grass-species). Increasing index values correlated strongly with increasing fire severity class (fire severity class vs. median LCCI: 0.9997; fire severity class vs. median NDRESWIR: 0.9874). After one year, the vegetations’ vitality in low severity and moderate high severity appeared to be at pre-fire level.

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3

HONDA, Yoshiaki, Shunji MURAI y Kikuuo KATOOU. "Global Monitoring of Vegetation." Journal of the Japan society of photogrammetry and remote sensing 31, n.º 1 (1992): 4–14. http://dx.doi.org/10.4287/jsprs.31.4.

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4

Økland, T. "Vegetational and ecological monitoring of boreal forests in Norway. I. Rausjømarka in Akershus county, SE Norway." Sommerfeltia 10, n.º 1 (1 de junio de 1990): 1–56. http://dx.doi.org/10.2478/som-1990-0001.

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Abstract Vegetational and ecological monitoring of boreal forests in Norway was initiated in 1988, as a part of the programme “Countrywide monitoring of forest health” at Norwegian Institute of Land Invetory (NIJOS). Ten reference areas for monitoring will be established and analysed within five years; two new areas each year. Each of the monitoring areas is planned to be reanalysed every fifth year. In each monitoring area 10 macro sample plots, 50 m2 each, are selected. Within each macro sample plot 5 meso sample plots, 1 m2 each, are randomly placed and the vegetation is analysed by using frequency in subplots as measure of species abundance. Within each meso sample plot one micro sample plot (two in the first established monitoring area), 0.0625 m2 each, is analysed by the same method. In connection with each meso sample plot several environmental variables are recorded. In each ma cro sample plot several tree variables and variables describing the terrain are recorded. The variables are used for environmental interpretation as well as for monitoring, since known relations between vegetation and environmental gradients form the basis of vegetational and ecological monitoring. Any future changes in vegetation, soil and the health of trees have to be interpreted in relation to the analysis of vegetation-environment relationships in order to identify changes due to air pollution or climatic changes. The data from the first established monitoring area, Rausj0marka in Akershus county, are subjected to analysis in this paper. The most important vegetational and environmental gradients in the area are discussed, as well as the field methodology and the methods for data analysis to be used in integrated monitoring. The advantages of integrated monitoring of vegetation, soil and trees on the same sample plots are emphasized, including advantages for surveying and monitoring of species (bioindicators).

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5

Shukla, Sonali. "Vegetation Monitoring System-A Review". International Journal for Research in Applied Science and Engineering Technology 6, n.º 2 (28 de febrero de 2018): 258–63. http://dx.doi.org/10.22214/ijraset.2018.2040.

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6

Mohler, Robert R. J., Gordon L. Wells, Cecil R. Hallurn y Michael H. Trenchard. "Monitoring vegetation of drought environments". BioScience 36, n.º 7 (julio de 1986): 478–83. http://dx.doi.org/10.2307/1310346.

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7

Gobron, N., A. Belward, B. Pinty y W. Knorr. "Monitoring biosphere vegetation 1998-2009". Geophysical Research Letters 37, n.º 15 (agosto de 2010): n/a. http://dx.doi.org/10.1029/2010gl043870.

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8

Retalis, A. "Modern Approaches in Vegetation Monitoring." Photogrammetric Record 21, n.º 114 (junio de 2006): 182. http://dx.doi.org/10.1111/j.1477-9730.2006.00375_3.x.

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9

Zhang, Xiaoyang, Mark A. Friedl, Crystal B. Schaaf, Alan H. Strahler, John C. F. Hodges, Feng Gao, Bradley C. Reed y Alfredo Huete. "Monitoring vegetation phenology using MODIS". Remote Sensing of Environment 84, n.º 3 (marzo de 2003): 471–75. http://dx.doi.org/10.1016/s0034-4257(02)00135-9.

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10

Wildi, O., E. Feldmeyer-Christe, S. Ghosh y N. E. Zimmermann. "Comments on vegetation monitoring approaches". Community Ecology 5, n.º 1 (junio de 2004): 1–5. http://dx.doi.org/10.1556/comec.5.2004.1.1.

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11

Pádua, Luís, Pedro Marques, Jonáš Hruška, Telmo Adão, Emanuel Peres, Raul Morais y Joaquim Sousa. "Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery". Remote Sensing 10, n.º 12 (29 de noviembre de 2018): 1907. http://dx.doi.org/10.3390/rs10121907.

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This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.

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12

Eastwood, J. A., M. G. Yates, A. G. Thomson y R. M. Fuller. "The reliability of vegetation indices for monitoring saltmarsh vegetation cover". International Journal of Remote Sensing 18, n.º 18 (diciembre de 1997): 3901–7. http://dx.doi.org/10.1080/014311697216739.

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13

Durpaire, J. P., T. Gentet, T. Phulpin y M. Arnaud. "Spot-4 vegetation instrument: Vegetation monitoring on a global scale". Acta Astronautica 35, n.º 7 (abril de 1995): 453–59. http://dx.doi.org/10.1016/0094-5765(94)00279-u.

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14

Pei, Fengsong, Changjiang Wu, Xiaoping Liu, Xia Li, Kuiqi Yang, Yi Zhou, Kun Wang, Li Xu y Gengrui Xia. "Monitoring the vegetation activity in China using vegetation health indices". Agricultural and Forest Meteorology 248 (enero de 2018): 215–27. http://dx.doi.org/10.1016/j.agrformet.2017.10.001.

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15

Lin, Wen-Pin. "Monitoring and Protection of Forest Ecological Tourism Resources by Dynamic Monitoring System". Ecological Chemistry and Engineering S 26, n.º 1 (1 de marzo de 2019): 189–97. http://dx.doi.org/10.1515/eces-2019-0018.

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Abstract Forest can adjust climate and provide resources for the development of the society and tourism as well as promote the progress of human civilization, which is of great significance to the survival and development of human beings. With the industrial development and the improvement of people’s living standard, the development strength on forest resources is becoming higher than ever before. As forest resources are important resources which can maintain the ecological balance of the earth, its monitoring and protection is necessary. Hence, remote sensing technology has been developed for monitoring the changes of forest resources, which has the quickness characteristics and real-time spatial information acquisition and analysis capacities. This paper firstly introduced the geographical location, geomorphology, climate status, soil and vegetation types of Zhangjiakou City, Hebei Province and the basic theory of remote sensing monitoring such as vegetation spectral reflectance and normalized differential vegetation index (NDVI). Then, the NDVI was used to analyse the vegetation coverage and area ratio of Zhangjiakou City in 2006, 2010 and 2016. It was found that the vegetation coverage during the ten years from 2006 to 2016 showed an overall trend of growth. Conclusions: It is concluded that dynamic monitoring can effectively monitor and protect forest vegetation, which provided ideas for the follow-up forestry planning and ecological tourism development in Zhangjiakou.

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16

Zribi, Mehrez, Erwan Motte, Pascal Fanise y Walid Zouaoui. "Low-Cost GPS Receivers for the Monitoring of Sunflower Cover Dynamics". Journal of Sensors 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6941739.

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The aim of this research is to analyze the potential use of Global Navigation Satellite System (GNSS) signals for the monitoring of in situ vegetation characteristics. An instrument, based on the use of a pair of low-cost receivers and antennas, providing continuous measurements of all the available Global Positioning System (GPS) satellite signals is proposed for the determination of signal attenuation caused by a sunflower cover. Experimental campaigns with this instrument, combined with ground truth measurements of the vegetation, were performed over a nonirrigated sunflower test field for a period of more than two months, corresponding to a significant portion of the vegetation cycle. A method is proposed for the analysis of the signal attenuation data as a function of elevation and azimuth angles. A high correlation is observed between the vegetation’s water content and the GPS signals attenuation, and an empirical modeling is tested for the retrieval of signal behavior as a function of vegetation water content (VWC). The VWC was estimated from GNSS signals on a daily basis, over the full length of the study period.

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17

Smith, Tyler, Jeremy Lundholm y Len Simser. "Wetland Vegetation Monitoring in Cootes Paradise". Ecological Restoration 19, n.º 3 (2001): 145–54. http://dx.doi.org/10.3368/er.19.3.145.

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18

Alvino, Francisco C. G., Catariny C. Aleman, Roberto Filgueiras, Daniel Althoff y Fernando F. da Cunha. "VEGETATION INDICES FOR IRRIGATED CORN MONITORING". Engenharia Agrícola 40, n.º 3 (junio de 2020): 322–33. http://dx.doi.org/10.1590/1809-4430-eng.agric.v40n3p322-333/2020.

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19

MIDGLEY, GUY. "Monitoring vegetation: a science in flux?" Journal of Biogeography 29, n.º 7 (julio de 2002): 971–72. http://dx.doi.org/10.1046/j.1365-2699.2002.00689.x.

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20

HOLMES, M. G. "Monitoring vegetation in the future: radar". Botanical Journal of the Linnean Society 108, n.º 2 (febrero de 1992): 93–109. http://dx.doi.org/10.1111/j.1095-8339.1992.tb01634.x.

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21

Svanberg, S. "Fluorescence lidar monitoring of vegetation status". Physica Scripta T58 (1 de enero de 1995): 79–85. http://dx.doi.org/10.1088/0031-8949/1995/t58/009.

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22

Gouveia, C., R. M. Trigo y C. C. DaCamara. "Drought and vegetation stress monitoring in Portugal using satellite data". Natural Hazards and Earth System Sciences 9, n.º 1 (18 de febrero de 2009): 185–95. http://dx.doi.org/10.5194/nhess-9-185-2009.

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Abstract. Remote sensed information on vegetation and soil moisture, namely the Normalised Difference Vegetation Index (NDVI) and the Soil Water Index (SWI), is employed to monitor the spatial extent, severity and persistence of drought episodes over Continental Portugal, from 1999 to 2006. The severity of a given drought episode is assessed by evaluating the cumulative impact over time of drought conditions on vegetation. Special attention is given to the drought episodes that have occurred in the last decade, i.e., 1999, 2002 and particularly the major event of 2005. During both the 1999 and 2005 drought episodes negative anomalies of NDVI are observed over large sectors of Southern Portugal for up to nine months (out of eleven) of the vegetative cycle. On the contrary, the 2002 event was characterized by negative anomalies in the northern half of Portugal and for a shorter period (eight out of eleven months). The impact of soil moisture on vegetation dynamics is evaluated by analyzing monthly anomalies of SWI and by studying the annual cycle of SWI vs. NDVI. While in the case of the drought episode of 1999 the scarcity of water in the soil persisted until spring, in the recent episode of 2005 the deficit in greenness was already apparent at the end of summer. The impact of dry periods on vegetation is clearly observed in both arable land and forest, and it is found that arable land presents a higher sensitivity. From an operational point of view, obtained results reveal the possibility of using the developed methodology to monitor, in quasi real-time, vegetation stress and droughts in Mediterranean ecosystems.

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23

Nuijten, Rik J. G., Nicholas C. Coops, Catherine Watson y Dustin Theberge. "Monitoring the Structure of Regenerating Vegetation Using Drone-Based Digital Aerial Photogrammetry". Remote Sensing 13, n.º 10 (16 de mayo de 2021): 1942. http://dx.doi.org/10.3390/rs13101942.

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Measures of vegetation structure are often key within ecological restoration monitoring programs because a change in structure is rapidly identifiable, measurements are straightforward, and structure is often a good surrogate for species composition. This paper investigates the use of drone-based digital aerial photogrammetry (DAP) for the characterization of the structure of regenerating vegetation as well as the ability to inform restoration programs through spatial arrangement assessment. We used cluster analysis on five DAP-derived metrics to classify vegetation structure into seven classes across three sites of ongoing restoration since linear disturbances in 2005, 2009, and 2014 in temperate and boreal coniferous forests in Alberta, Canada. The spatial arrangement of structure classes was assessed using land cover maps, mean patch size, and measures of local spatial association. We observed DAP heights of short-stature vegetation were consistently underestimated, but strong correlations (rs > 0.75) with field height were found for juvenile trees, shrubs, and perennials. Metrics of height and canopy complexity allowed for the extraction of relatively tall and complex vegetation structures, whereas canopy cover and height variability metrics enabled the classification of the shortest vegetation structures. We found that the boreal site disturbed in 2009 had the highest cover of classes associated with complex vegetation structures. This included early regenerative (22%) and taller (13.2%) wood-like structures as well as structures representative of tall graminoid and perennial vegetation (15.3%), which also showed the highest patchiness. The developed tools provide large-scale maps of the structure, enabling the identification and assessment of vegetational patterns, which is challenging based on traditional field sampling that requires pre-defined location-based hypotheses. The approach can serve as a basis for the evaluation of specialized restoration objectives as well as objectives tailored towards processes of ecological succession, and support prioritization of future inspections and mitigation measures.

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24

Zhang, Lifu, Na Qiao, Muhammad Hasan Ali Baig, Changping Huang, Xin Lv, Xuejian Sun y Ze Zhang. "Monitoring vegetation dynamics using the universal normalized vegetation index (UNVI): An optimized vegetation index-VIUPD". Remote Sensing Letters 10, n.º 7 (27 de marzo de 2019): 629–38. http://dx.doi.org/10.1080/2150704x.2019.1597298.

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25

Kurbanov, R. K. y N. I. Zakharova. "Application of Vegetation Indexes to Assess the Condition of Crops". Agricultural Machinery and Technologies 14, n.º 4 (18 de diciembre de 2020): 4–11. http://dx.doi.org/10.22314/2073-7599-2020-14-4-4-11.

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Monitoring of the state of agricultural crops and forecasting the crops development begin with aerial photography using a unmanned aerial vehicles and a multispectral camera. Vegetation indexes are selected empirically and calculated as a result of operations with values of diff erent spectral wavelengths. When assessing the state of crops, especially in breeding, it is necessary to determine the limiting factors for the use of vegetation indexes.(Research purpose) To analyze, evaluate and select vegetation indexes for conducting operational, high-quality and comprehensive monitoring of the state of crops and the formation of optimal management decisions.(Materials and Methods) The authors studied the results of scientifi c research in the fi eld of remote sensing technology using unmanned aerial vehicles and multispectral cameras, as well as the experience of using vegetation indexes to assess the condition of crops in the precision farming system. The limiting factors for the vegetation indexes research were determined: a limited number of monochrome cameras in popular multispectral cameras; key indicators for monitoring crops required by agronomists. After processing aerial photographs from an unmanned aerial vehicle, a high-precision orthophotomap, a digital fi eld model, and maps of vegetation indexes were created.(Results and discussion) More than 150 vegetation indexes were found. Not all of them were created through observation and experimentation. The authors considered broadband vegetation indexes to assess the status of crops in the fi elds. They analyzed the vegetation indexes of soybean and winter wheat crops in the main phases of vegetation.(Conclusions) The authors found that each vegetative index had its own specifi c scope, limiting factors and was used both separately and in combination with other indexes. When calculating the vegetation indexes for practical use, it was recommended to be guided by the technical characteristics of multispectral cameras and took into account the index use eff ectiveness at various vegetation stages.

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26

Xu, Nianxu, Jia Tian, Qingjiu Tian, Kaijian Xu y Shaofei Tang. "Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index". Remote Sensing 11, n.º 10 (19 de mayo de 2019): 1192. http://dx.doi.org/10.3390/rs11101192.

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Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle’s hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R2s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge’s reflectance and the NDVI. Positive correlations (R2 = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R2 = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images.

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27

Li, Huaimin, Weipan Lin, Fangrong Pang, Xiaoping Jiang, Weixing Cao, Yan Zhu y Jun Ni. "Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis". Sensors 20, n.º 10 (20 de mayo de 2020): 2894. http://dx.doi.org/10.3390/s20102894.

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An instrument developed to monitor and diagnose crop growth can quickly and non-destructively obtain crop growth information, which is helpful for crop field production and management. Focusing on the problems with existing two-band instruments used for crop growth monitoring and diagnosis, such as insufficient information available on crop growth and low accuracy of some growth indices retrieval, our research team developed a portable three-band instrument for crop-growth monitoring and diagnosis (CGMD) that obtains a larger amount of information. Based on CGMD, this paper carried out studies on monitoring wheat growth indices. According to the acquired three-band reflectance spectra, the combined indices were constructed by combining different bands, two-band vegetation indices (NDVI, RVI, and DVI), and three-band vegetation indices (TVI-1 and TVI-2). The fitting results of the vegetation indices obtained by CGMD and the commercial instrument FieldSpec HandHeld2 was high and the new instrument could be used for monitoring the canopy vegetation indices. By fitting each vegetation index to the growth index, the results showed that the optimal vegetation indices corresponding to leaf area index (LAI), leaf dry weight (LDW), leaf nitrogen content (LNC), and leaf nitrogen accumulation (LNA) were TVI-2, TVI-1, NDVI (R730, R815), and NDVI (R730, R815), respectively. R2 values corresponding to LAI, LDW, LNC and LNA were 0.64, 0.84, 0.60, and 0.82, respectively, and their relative root mean square error (RRMSE) values were 0.29, 0.26, 0.17, and 0.30, respectively. The addition of the red spectral band to CGMD effectively improved the monitoring results of wheat LAI and LDW. Focusing the problem of vegetation index saturation, this paper proposed a method to construct the wheat-growth-index spectral monitoring models that were defined according to the growth periods. It improved the prediction accuracy of LAI, LDW, and LNA, with R2 values of 0.79, 0.85, and 0.85, respectively, and the RRMSE values of these growth indices were 0.22, 0.23, and 0.28, respectively. The method proposed here could be used for the guidance of wheat field cultivation.

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28

Atanasov, Asparuh, Radko Mihaylov, Svilen Stoyanov, Desislava Mihaylova y Peter Benov. "Drone-based Monitoring of Sunflower Crops". ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA 6, n.º 1 (18 de mayo de 2022): 1–9. http://dx.doi.org/10.29114/ajtuv.vol6.iss1.258.

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Remote monitoring and utilization of digital technologies is essential for the application of the precision farming approach, which contributes significantly to the improved quality of agricultural products. The paper compares the data for six vegetation indices when observing the sunflower vegetation in South Dobrudzha in 2021. Images with RGB and digital NIR camera were obtained via a remotely piloted quadcopter. The flight plan specifies speed 8 m/s, altitude 100 m and shooting overlapping images of 80%. Six vegetation indices: NDVI, EVI2, SAVI, CVI, MGVRI and MPRI were calculated from the images obtained during the flight. The calculation of the indices takes into account the intensity of solar radiation and the parameters of the meteorological situation at the time of shooting. The findings obtained reveal a stable trend of change of the vegetation indices, thus, establishing accurate and reliable results as for the monitoring of agricultural areas with unmanned aerial vehicles.

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29

Steyer, Gregory D., Brady R. Couvillion y John A. Barras. "Monitoring Vegetation Response to Episodic Disturbance Events by using Multitemporal Vegetation Indices". Journal of Coastal Research 63 (abril de 2013): 118–30. http://dx.doi.org/10.2112/si63-011.1.

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30

Wang, Mengjia, Lei Fan, Frédéric Frappart, Philippe Ciais, Rui Sun, Yi Liu, Xiaojun Li, Xiangzhuo Liu, Christophe Moisy y Jean-Pierre Wigneron. "An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales". Remote Sensing of Environment 263 (septiembre de 2021): 112556. http://dx.doi.org/10.1016/j.rse.2021.112556.

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31

Du, Jinyang, Jiancheng Shi, Qiang Liu y Lingmei Jiang. "Refinement of Microwave Vegetation Index Using Fourier Analysis for Monitoring Vegetation Dynamics". IEEE Geoscience and Remote Sensing Letters 10, n.º 5 (septiembre de 2013): 1205–8. http://dx.doi.org/10.1109/lgrs.2012.2236297.

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32

Alexandridis, T. K., N. Oikonomakis, I. Z. Gitas, K. M. Eskridge y N. G. Silleos. "The performance of vegetation indices for operational monitoring of CORINE vegetation types". International Journal of Remote Sensing 35, n.º 9 (8 de abril de 2014): 3268–85. http://dx.doi.org/10.1080/01431161.2014.902548.

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33

Dong, Shi Wei, Dan Feng Sun y Hong Li. "Vegetation Fraction Change Monitoring in Beijing by Remote Sensing". Advanced Materials Research 955-959 (junio de 2014): 859–62. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.859.

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Vegetation fraction was a most important index to score the vegetation coverage on the land surface. Improved dimidiate pixel model was applied to calculate and analyze the vegetation fraction change from September 2000 to September 2013 in Beijing, China. The results showed that vegetation coverage of Beijing in 2013 year was much better than 2000 year. The area of low and middle-low coverage of Beijing in 2013 decreased 379 km2 and 591 km2 respectively, and the area of high and middle coverage increased 885 km2 and 85 km2 respectively. The research provided a necessary reference for the related researches of vegetation fraction.

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34

Gharbi, M. A. y H. N. Mukhlif. "Using of Temporal Variability for Monitoring Change of Vegetation via Remote Sensing in Anbar Province". IOP Conference Series: Earth and Environmental Science 904, n.º 1 (1 de noviembre de 2021): 012038. http://dx.doi.org/10.1088/1755-1315/904/1/012038.

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Abstract The use of spectral indicators and evidence based on remote sensing and the results of space data is one of the most important means of detecting changes in vegetation and land cover. Three space scenes were selected to assess vegetation changes in Anbar governorate, consisting of 33 satellite shots in different dates for the years 1999, 2009 and 2019, captured by the Landsat 5 and Landsat 8 for the two sensors TM and OLI. Spectral data were used to calculate NDVI normal vegetative variation index values. The results showed that the area of barren land continued to increase by 24,809. 19 km2 by 11.47, 11.98 and 30.56% for 1999, 2009 and 2019 respectively and the area of dense vegetation decreased by 857.73 km2 by 0.30, 0.17 and 1.40% for the same years respectively.

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35

Turner, David P. "Global vegetation monitoring: toward a sustainable technobiosphere". Frontiers in Ecology and the Environment 9, n.º 2 (22 de abril de 2010): 111–16. http://dx.doi.org/10.1890/090171.

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36

Merenlender, Adina M., Kerry L. Heise, James W. Bartolome y Barbara H. Allen-Diaz. "Monitoring shows vegetation change at multiple scales". California Agriculture 55, n.º 6 (noviembre de 2001): 42–47. http://dx.doi.org/10.3733/ca.v055n06p42.

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37

Tueller, P. T. "Remote sensing applications for monitoring rangeland vegetation". Journal of the Grassland Society of Southern Africa 8, n.º 4 (diciembre de 1991): 160–67. http://dx.doi.org/10.1080/02566702.1991.9648284.

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38

JUSTICE, C. O., B. N. HOLBEN y M. D. GWYNNE. "Monitoring East African vegetation using AVHRR data". International Journal of Remote Sensing 7, n.º 11 (noviembre de 1986): 1453–74. http://dx.doi.org/10.1080/01431168608948948.

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39

Morisette, Jeff, Faith Ann Heinsch y Steven W. Running. "Monitoring global vegetation using moderate-resolution satellites". Eos, Transactions American Geophysical Union 87, n.º 50 (2006): 568. http://dx.doi.org/10.1029/2006eo500009.

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40

Krapivin, Vladimir F., Anatolij M. Shutko, Alexander A. Chukhlantsev, Sergei P. Golovachev y Gary W. Phillips. "GIMS-based method for vegetation microwave monitoring". Environmental Modelling & Software 21, n.º 3 (marzo de 2006): 330–45. http://dx.doi.org/10.1016/j.envsoft.2004.11.005.

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41

Eng L, Soon, Rozita Ismail, Wahidah Hashim, Rajina R. Mohamed y Aslina Baharum. "Vegetation Monitoring Using UAV : a Preliminary Study". International Journal of Engineering & Technology 7, n.º 4.35 (30 de noviembre de 2018): 223. http://dx.doi.org/10.14419/ijet.v7i4.35.22736.

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Remote sensing using drone or UAV (unmanned aerial vehicle) is the current trends and this technology can provide unrevealed life-altering benefits to mankind. Drones are being used in many sectors such as for military, research, agricultural and recreational means. This technology not only can reduce the time of inspection, but it is also giving many benefits such as provides real-time live video for site inspection that can help user to analyze site logistic and speeding up the overall tasks. However, vegetation monitoring using remote sensing has its own challenges in terms of processing the captured image and data. Somehow, previous research has suggested a lot of different possible algorithm that could be used for post-processing the data gathered. Nevertheless, most of the algorithm requires a specific sensor in order to get the result. The objective of this paper is to identify and verify the algorithm that is suitable to process the vegetation image. This research will use the data gathered from various area by using consumer camera and process by using Visible Atmospherically Resistant Index (VARI) indices. Finally, this research will observe the accuracy of the result analyzed using the VARI and identify the characteristic of the algorithm.

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42

Huete, Alfredo R. "Vegetation Indices, Remote Sensing and Forest Monitoring". Geography Compass 6, n.º 9 (septiembre de 2012): 513–32. http://dx.doi.org/10.1111/j.1749-8198.2012.00507.x.

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43

Paloscia, S. y P. Pampaloni. "Microwave polarization index for monitoring vegetation growth". IEEE Transactions on Geoscience and Remote Sensing 26, n.º 5 (1988): 617–21. http://dx.doi.org/10.1109/36.7687.

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44

Br�kenhielm, Sven y Liu Qinghong. "Comparison of field methods in vegetation monitoring". Water, Air, & Soil Pollution 79, n.º 1-4 (enero de 1995): 75–87. http://dx.doi.org/10.1007/bf01100431.

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45

He, Dong, Xianglin Huang, Qingjiu Tian y Zhichao Zhang. "Changes in Vegetation Growth Dynamics and Relations with Climate in Inner Mongolia under More Strict Multiple Pre-Processing (2000–2018)". Sustainability 12, n.º 6 (24 de marzo de 2020): 2534. http://dx.doi.org/10.3390/su12062534.

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Inner Mongolia Autonomous Region (IMAR) is related to China’s ecological security and the improvement of ecological environment; thus, the vegetation’s response to climate changes in IMAR has become an important part of current global change research. As existing achievements have certain deficiencies in data preprocessing, technical methods and research scales, we correct the incomplete data pre-processing and low verification accuracy; use grey relational analysis (GRA) to study the response of Enhanced Vegetation Index (EVI) in the growing season to climate factors on the pixel scale; explore the factors that affect the response speed and response degree from multiple perspectives, including vegetation type, longitude, latitude, elevation and local climate type; and solve the problems of excessive ignorance of details and severe distortion of response results due to using average values of the wide area or statistical data. The results show the following. 1. The vegetation status of IMAR in 2000-2018 was mainly improved. The change rates were 0.23/10° N and 0.25/10° E, respectively. 2. The response speed and response degree of forests to climatic factors are higher than that of grasslands. 3. The lag time of response for vegetation growth to precipitation, air temperature and relative humidity in IMAR is mainly within 2 months. The speed of vegetation‘s response to climate change in IMAR is mainly affected by four major factors: vegetation type, altitude gradient, local climate type and latitude. 4. Vegetation types and altitude gradients are the two most important factors affecting the degree of vegetation’s response to climate factors. It is worth noting that when the altitude rises to 2500 m, the dominant factor for the vegetation growth changes from precipitation to air temperature in terms of hydrothermal combination in the environment. Vegetation growth in areas with relatively high altitudes is more dependent on air temperature.

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46

Džubáková, K., P. Molnar, K. Schindler y M. Trizna. "Monitoring of riparian vegetation response to flood disturbances using terrestrial photography". Hydrology and Earth System Sciences 19, n.º 1 (13 de enero de 2015): 195–208. http://dx.doi.org/10.5194/hess-19-195-2015.

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Abstract. Flood disturbance is one of the major factors impacting riparian vegetation on river floodplains. In this study we use a high-resolution ground-based camera system with near-infrared sensitivity to quantify the immediate response of riparian vegetation in an Alpine, gravel bed, braided river to flood disturbance with the use of vegetation indices. Five large floods with return periods between 1.4 and 20.1 years in the period 2008–2011 in the Maggia River were analysed to evaluate patterns of vegetation response in three distinct floodplain units (main bar, secondary bar, transitional zone) and to compare the sensitivity of seven broadband vegetation indices. The results show both a negative (damage) and positive (enhancement) response of vegetation within 1 week following the floods, with a selective impact determined by pre-flood vegetation vigour, geomorphological setting and intensity of the flood forcing. The spatial distribution of vegetation damage provides a coherent picture of floodplain response in the three floodplain units. The vegetation indices tested in a riverine environment with highly variable surface wetness, high gravel reflectance, and extensive water–soil–vegetation contact zones differ in the direction of predicted change and its spatial distribution in the range 0.7–35.8%. We conclude that vegetation response to flood disturbance may be effectively monitored by terrestrial photography with near-infrared sensitivity, with potential for long-term assessment in river management and restoration projects.

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47

Gong, Bin, Cheng Shu, Song Han y Sheng-Gao Cheng. "Mine Vegetation Identification via Ecological Monitoring and Deep Belief Network". Plants 10, n.º 6 (30 de mayo de 2021): 1099. http://dx.doi.org/10.3390/plants10061099.

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Based on the characteristics of remote sensing images of mine vegetation, this research studied the application of deep belief network model in mine vegetation identification. Through vegetation identification and classification, the ecological environment index of mining area was determined according to the analysis of vegetation and coverage. Deep learning algorithm is adopted to improve the depth study, the vegetation coverage in the analysis was studied. Parameters and parameter values were selected for identification by establishing the optimal experimental design. The experimental results were compared with remote sensing images to determine the accuracy of deep learning identification and the effectiveness of the algorithm. When the sample size is 2,000,000 pixels, through repeated tests and classification effect comparison, the optimal parameter setting suitable for mine vegetation identification is obtained. Parameter setting: the number of network layers is 3 layers; the number of hidden layer neurons is 60. The learning rate is 0.01 and the number of iterations is 2. The average recognition rate of vegetation coverage was 95.95%, outperforming some other models, and the accuracy rate of kappa coefficient was 0.95, which can accurately reflect the vegetation coverage. The clearer the satellite image is, the more accurate the recognition result is, and the accuracy is closer to 100%. The identification of vegetation coverage has important guiding significance for determining the area and area of ecological restoration.

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48

Liu, Jun, Xi Yang, Hao Long Liu y Zhi Qiao. "Algorithms and Applications in Grass Growth Monitoring". Abstract and Applied Analysis 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/508315.

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Monitoring vegetation phonology using satellite data has been an area of growing research interest in recent decades. Validation is an essential issue in land surface phenology study at large scale. In this paper, double logistic function-fitting algorithm was used to retrieve phenophases for grassland in North China from a consistently processed Moderate Resolution Spectrodiometer (MODIS) dataset. Then, the accuracy of the satellite-based estimates was assessed using field phenology observations. Results show that the method is valid to identify vegetation phenology with good success. The phenophases derived from satellite and observed on ground are generally similar. Greenup onset dates identified by Normalized Difference Vegetation Index (NDVI) and in situ observed dates showed general agreement. There is an excellent agreement between the dates of maturity onset determined by MODIS and the field observations. The satellite-derived length of vegetation growing season is generally consistent with the surface observation.

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49

Zhang, Aiwu, Shaoxing Hu, Xizhen Zhang, Taipei Zhang, Mengnan Li, Haiyu Tao y Yan Hou. "A Handheld Grassland Vegetation Monitoring System Based on Multispectral Imaging". Agriculture 11, n.º 12 (13 de diciembre de 2021): 1262. http://dx.doi.org/10.3390/agriculture11121262.

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Monitoring grassland vegetation growth is of vital importance to scientific grazing and grassland management. People expect to be able to use a portable device, like a mobile phone, to monitor grassland vegetation growth at any time. In this paper, we propose a handheld grassland vegetation monitoring system to achieve the goal of monitoring grassland vegetation growth. The system includes two parts: the hardware unit is a hand-held multispectral imaging tool named ASQ-Discover based on a smartphone, which has six bands (wavelengths)—including three visible bands (450 nm, 550 nm, 650 nm), a red-edge band (750 nm), and two near-infrared bands (850 nm, 960 nm). The imagery data of each band has a size of 5120 × 3840 pixels with 8-bit depth. The software unit improves image quality through vignetting removal, radiometric calibration, and misalignment correction and estimates and analyzes spectral traits of grassland vegetation (Fresh Grass Ratio (FGR), NDVI, NDRE, BNDVI, GNDVI, OSAVI and TGI) that are indicators of vegetation growth in grassland. We introduce the hardware and software unit in detail, and we also experiment in five pastures located in Haiyan County, Qinghai Province. Our experimental results show that the handheld grassland vegetation growth monitoring system has the potential to revolutionize the grassland monitoring that operators can conduct when using a hand-held tool to achieve the tasks of grassland vegetation growth monitoring.

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50

Almalki, Raid, Mehdi Khaki, Patricia M. Saco y Jose F. Rodriguez. "Monitoring and Mapping Vegetation Cover Changes in Arid and Semi-Arid Areas Using Remote Sensing Technology: A Review". Remote Sensing 14, n.º 20 (14 de octubre de 2022): 5143. http://dx.doi.org/10.3390/rs14205143.

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Vegetation cover change is one of the key indicators used for monitoring environmental quality. It can accurately reflect changes in hydrology, climate, and human activities, especially in arid and semi-arid regions. The main goal of this paper is to review the remote sensing satellite sensors and the methods used for monitoring and mapping vegetation cover changes in arid and semi-arid. Arid and semi-arid lands are eco-sensitive environments with limited water resources and vegetation cover. Monitoring vegetation changes are especially important in arid and semi-arid regions due to the scarce and sensitive nature of the plant cover. Due to expected changes in vegetation cover, land productivity and biodiversity might be affected. Thus, early detection of vegetation cover changes and the assessment of their extent and severity at the local and regional scales become very important in preventing future biodiversity loss. Remote sensing data are useful for monitoring and mapping vegetation cover changes and have been used extensively for identifying, assessing, and mapping such changes in different regions. Remote sensing data, such as satellite images, can be obtained from satellite-based and aircraft-based sensors to monitor and detect vegetation cover changes. By combining remotely sensed images, e.g., from satellites and aircraft, with ground truth data, it is possible to improve the accuracy of monitoring and mapping techniques. Additionally, satellite imagery data combined with ancillary data such as slope, elevation, aspect, water bodies, and soil characteristics can detect vegetation cover changes at the species level. Using analytical methods, the data can then be used to derive vegetation indices for mapping and monitoring vegetation.

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