Relationships between Hyperion-derived vegetation indices, biophysical parameters, and elevation data in a Brazilian savannah environment

2010 ◽  
Vol 1 (1) ◽  
pp. 55-64 ◽  
Author(s):  
A. A. Souza ◽  
L. S. Galvão ◽  
J. R. Santos
Keyword(s):  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Brazilian Savannah ◽  
Elevation Data

scholarly journals Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2755
Author(s):  
Peng Fang ◽  
Nana Yan ◽  
Panpan Wei ◽  
Yifan Zhao ◽  
Xiwang Zhang
Keyword(s):  
Aboveground Biomass ◽  
Production Management ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Edge Information ◽  
Casa Model ◽  
Red Edge ◽  
Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

scholarly journals Mapping Wheat Dry Matter and Nitrogen Content Dynamics and Estimation of Wheat Yield Using UAV Multispectral Imagery Machine Learning and a Variety-Based Approach: Case Study of Morocco

2021 ◽  
Vol 3 (1) ◽  
pp. 29-49
Author(s):  
Ghizlane Astaoui ◽  
Jamal Eddine Dadaiss ◽  
Imane Sebari ◽  
Samir Benmansour ◽  
Ettarid Mohamed
Keyword(s):  
Random Forest ◽  
Nitrogen Uptake ◽  
Dry Matter ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Wheat Crop ◽  
Multispectral Imagery ◽  
Biophysical Parameters ◽  
Phenological Stages ◽  
Wheat Varieties

Our work aims to monitor wheat crop using a variety-based approach by taking into consideration four different phenological stages of wheat crop development. In addition to highlighting the contribution of Red-Edge vegetation indices in mapping wheat dry matter and nitrogen content dynamics, as well as using Random Forest regressor in the estimation of wheat yield, dry matter and nitrogen uptake relying on UAV (Unmanned Aerial Vehicle) multispectral imagery. The study was conducted on an experimental platform with 12 wheat varieties located in Sidi Slimane (Morocco). Several flight missions were conducted using eBee UAV with MultiSpec4C camera according to phenological growth stages of wheat. The proposed methodology is subdivided into two approaches, the first aims to find the most suitable vegetation index for wheat’s biophysical parameters estimation and the second to establish a global model regardless of the varieties to estimate the biophysical parameters of wheat: Dry matter and nitrogen uptake. The two approaches were conducted according to six main steps: (1) UAV flight missions and in-situ data acquisition during four phenological stages of wheat development, (2) Processing of UAV multispectral images which enabled us to elaborate the vegetation indices maps (RTVI, MTVI2, NDVI, NDRE, GNDVI, GNDRE, SR-RE et SR-NIR), (3) Automatic extraction of plots by Object-based image analysis approach and creating a spatial database combining the spectral information and wheat’s biophysical parameters, (4) Monitoring wheat growth by generating dry biomass and wheat’s nitrogen uptake model using exponential, polynomial and linear regression for each variety this step resumes the varietal approach, (5) Engendering a global model employing both linear regression and Random Forest technique, (6) Wheat yield estimation. The proposed method has allowed to predict from 1 up to 21% difference between actual and estimated yield when using both RTVI index and Random Forest technique as well as mapping wheat’s dry biomass and nitrogen uptake along with the nitrogen nutrition index (NNI) and therefore facilitate a careful monitoring of the health and the growth of wheat crop. Nevertheless, some wheat varieties have shown a significant difference in yield between 2.6 and 3.3 t/ha.

scholarly journals MONITORAMENTO DA COBERTURA VEGETAL POR SENSORIAMENTO REMOTO NO SEMIÁRIDO BRASILEIRO ATRAVÉS DE ÍNDICES DE VEGETAÇÃO

Nativa ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 708
Author(s):  
Caio Victor Santos Silva ◽  
Jhon Lennon Bezerra da Silva ◽  
Geber Barbosa De Albuquerque Moura ◽  
Pabrício Marcos Oliveira Lopes ◽  
Cristina Rodrigues Nascimento ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Environmental Changes ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Digital Processing ◽  
Semiarid Region ◽  
Dry Period ◽  
Biophysical Parameters ◽  
Brazilian Semiarid

São necessárias medidas que visem à proteção e conservação dos recursos hídricos e naturais de forma rápida e eficiente. As técnicas de sensoriamento remoto são essenciais para o monitoramento ambiental dos recursos no semiárido no espaço e no tempo. Objetivou-se monitorar e analisar à dinâmica da cobertura vegetal através da variabilidade espaço-temporal do albedo da superfície e índices de vegetação em região de Caatinga do semiárido brasileiro por sensoriamento remoto. A área de estudo é o município de Arcoverde, localizado no semiárido de Pernambuco. O estudo foi desenvolvido através de seis imagens orbitais do Landsat-5 do sensor TM. O processamento digital dos parâmetros biofísicos foi realizado pelo algoritmo SEBAL. Os resultados foram analisados através da estatística descritiva e quanto a sua variabilidade. Áreas possivelmente degradadas foram identificadas pelos altos valores de albedo e índices de vegetação significativamente menores, localizadas à sudoeste e noroeste da região. Os índices apresentaram comportamento similares, principalmente no período seco, com baixos valores sendo próximos de zero, áreas afetadas pelo período de seca no semiárido. O SAVI apresentou maior precisão, destacando melhor resposta espectral da vegetação. O sensoriamento remoto promoveu monitoramento espaço-temporal adequado, destacando principalmente o período classificado como climaticamente seco através do albedo e índices de vegetação.Palavras-chave: Caatinga; NDVI; SAVI; mudanças ambientais; SEBAL. MONITORING OF VEGETATION COVER BY REMOTE SENSING IN BRAZILIAN SEMIARID THROUGH VEGETATION INDICES ABSTRACT: Measures are needed aimed at the protection and conservation of water and natural resources quickly and efficiently. Remote sensing techniques are essential for the environmental monitoring of resources in the semiarid region in space and time. Aimed to monitor and analyze the dynamics of vegetation cover through the spatial-temporal variability of the surface albedo and indices of vegetation in the Caatinga region of the Brazilian semiarid by remote sensing. The study area is the municipality of Arcoverde, located in the semiarid of Pernambuco. The study was developed through six orbital images of Landsat-5 of the TM sensor. The digital processing of the biophysical parameters was performed by the SEBAL algorithm. The results were analyzed through descriptive statistics and their variability. Possibly degraded areas were identified by high albedo values and significantly lower vegetation indices, located in the southwest and northwest of the region. The indexes showed similar behavior, mainly in the dry period, with low values being close to zero, areas affected by the dry period in the semiarid. The SAVI presented higher accuracy, highlighting better spectral response of the vegetation. Remote sensing promoted adequate space-time monitoring, highlighting mainly the period classified as climatically dry through the albedo and vegetation indexes.Keywords: Caatinga; NDVI; SAVI; environmental changes; SEBAL.

scholarly journals NEW VEGETATION INDICES FOR FULL AND COMPACT POLARIMETRIC SAR DATA: IN PREPARATION FOR THE RADARSAT CONSTELLATION MISSION (RCM)

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 41-46
Author(s):  
D. Ratha ◽  
D. Mandal ◽  
S. Dey ◽  
A. Bhattacharya ◽  
A. Frery ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Geodesic Distance ◽  
Vegetation Indices ◽  
Series Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
Plant Area Index ◽  
Sar Data

Abstract. In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version uses the ideal depolariser to model the randomness in the vegetation. We have utilized the RADARSAT Constellation Mission (RCM) time-series data from the SAMPVEX16-MB campaign in the Manitoba region of Canada for comparing and assessing the indices in terms of the change in the biophysical parameters as well. The compact-pol data for comparison is simulated from the full-pol RCM time series. Both the indices show better performance at correlating with biophysical parameters such as Plant Area Index (PAI) and Volumetric Water Content (VWC) for wheat and soybean crops, in comparison to the state-of-art Radar Vegetation Index (RVI) of Kim and van Zyl. These indices are timely for the upcoming release of the data from the RCM, which will provide data in both full and compact-pol modes, aimed at better crop monitoring from space.

scholarly journals Detection of changes of the soil use and vegetation in the low San Francisco

2019 ◽  
Vol 9 (5) ◽  
pp. 310
Author(s):  
Douglas Alberto De Oliveira Silva ◽  
Frederico abraão Costa Lins ◽  
Jhon Lennon Bezerra da Silva ◽  
Landson Carlos da Silva ◽  
Geber Barbosa De Albuquerque Moura ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Environmental Monitoring ◽  
Satellite Images ◽  
Environmental Changes ◽  
Vegetation Indices ◽  
Operating Cost ◽  
Semiarid Region ◽  
Landsat 8 ◽  
Biophysical Parameters

The quantification and spatialization of environmental degradation is an essential element in the planning of agricultural activities and in the management of the water and natural resources in the semiarid. Thus, the detection of changing land use conditions is necessary for understand with more accurately the dynamics of the different types of soil coverage. Remote sensing techniques make it possible to evaluate this type of environmental monitoring in a practical and efficient manner, and low operating cost in a short time. The objective of this study was to monitor and evaluate the environmental changes caused about the Caatinga vegetation coverage by remote sensing using satellite images in the municipality of Petrolina, semiarid region of Pernambuco state. The study was developed using two Landsat-8 satellite images, processed using SEBAL algorithm steps, in the development of thematic maps of the surface biophysical parameters. The maps expressed the spatial distribution of the albedo parameters and surface temperature, and of the NDVI and SAVI vegetation indices, which served for highlight the dynamics of environmental changes in the Caatinga natural environment of semiarid region. The results showed increased of the albedo and surface temperature when there was a decrease in vegetation indices. This behavior was mainly favored by the region's dry season, which coincides with the satellite's days of passage. The biophysical parameters are effective in the spatial monitoring of semiarid regions, highlighting the spatial variability of the soil uses, identifying possibly degraded areas. Remote sensing environmental monitoring is a viable alternative for mitigate environmental changes caused by anthropogenic actions and drought events. 

scholarly journals Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery

Agronomy ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 711
Author(s):  
Amparo Cisneros ◽  
Peterson Fiorio ◽  
Patricia Menezes ◽  
Nieves Pasqualotto ◽  
Shari Van Wittenberghe ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Agricultural Science ◽  
Grass Species ◽  
Biomass Estimation ◽  
Panicum Maximum ◽  
Biophysical Parameters ◽  
Area Index ◽  
Tropical Grasslands ◽  
Urochloa Brizantha ◽  
Sentinel 2

Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)’s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species’ cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall’Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)–near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg−1. The estimation of grassland productivity based on red-edge and NIR bands showed a R2 = 0.54 and a RMSE = 1800 kg ha−1. For the LAI, the best index was the NAOC (R2 = 0.57 and RMSE = 1.4 m2 m−2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization.

Biophysical parameters analysis in the detection of coverage changes and land use in a conservation unit in the Brazilian Pantanal

2020 ◽  
Vol 11 (5) ◽  
pp. 664-676
Author(s):  
Victor Hugo de Morais Danelichen ◽  
Marcelo Sacardi Biudes
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Complex Dynamics ◽  
Vegetation Indices ◽  
Conservation Unit ◽  
Biophysical Parameters ◽  
Significant Difference ◽  
Parameters Analysis ◽  
Brazilian Pantanal ◽  
Dry And Rainy Seasons

In order to preserve the Private Reserve of SESC Pantanal Natural Heritage (RPPN) was established in 1997. The physiognomies there show rapid transitions, which makes it difficult to classify land use and complex dynamics of the flood regime. Therefore, the object of this work was to evaluate the temperature dynamics and biophysical parameters in a reserve in the Brazilian Pantanal. Precipitation data and images from the Landsat 5 satellite were used to compute biophysical parameters. Our results demonstrate that the largest area is occupied by shrubby vegetation, followed by pasture, cambarazal and murundus fields. The albedo, the surface temperature, and the NDVI showed significant differences over the studied period and in the dry and rainy seasons. The vegetation indices showed the highest values in the years of 1989 during the rainy season, while the highest values of albedo and surface temperature were obtained a decade later, periods that precede the formation and expansion of the RPPN SESC-Pantanal. The LAI did not show any significant difference between the entire period studied and between the dry and rainy seasons, which demonstrates that most of the vegetation maintained during the period of formation, expansion and preservation of the RPPN.

scholarly journals Comparison of spectro-biophysical and yield parameters of cotton in irrigated lowlands of Amudaria River

2020 ◽  
Vol 26 (3) ◽  
pp. 294-308
Author(s):  
Shavkat Kenjabaev ◽  
Christopher Conrad ◽  
Odilbek Eshchanov ◽  
Aybek Arifjanov ◽  
Dilbar Abduraimova ◽  
...  
Keyword(s):  
Data Collection ◽  
Vegetation Indices ◽  
P Value ◽  
Growth Stages ◽  
Landsat 8 ◽  
Strong Positive Correlation ◽  
Cotton Yield ◽  
Biophysical Parameters ◽  
Area Index ◽  
Function Relationship

This study aims defining the best predictors of biophysical parameters and yield with vegetation indices derived from Landsat 8 OLI surface reflectance data. The study was conducted in 2015 at five crop fields in Kulavat canal irrigation system in Khorezm province, Uzbekistan. The Environment for Visualizing Images (ENVI) ver. 4.5 and R programming software ver. 1.0.143 were used to process, calculate seven vegetation indices (VIs) and predict biophysical parameters and yield of cotton. The trend analysis show that in-situ measured biophysical parameters for the whole growth stage of cotton follows the 3rd order polinomial curve (R2 = 0.96-0.99). The NDVI, SAVI, TVI and RVI had linear interrelationship between each other with strong positive correlation of R2>0.9 (highly significant with p-value=0). The VIs showed a logarithmic function relationship with crop height (crht), power function relationship with green biomass (gbm) and leaf area index (LAI), and linear function relationship with the fraction of photosyntetically active radiation below the plant canopy (FPAR) during the entire growing period of cotton. Among seven VIs tested in this study, the NDVI/SAVI and GCI explained 88 and 91 % of variation in crht, respectively. These three indices also well explained gbm variation (R2=0.86). The TVI was slightly better explained FPAR than NDVI and SAVI (all R2>0.87). The NDVI, SAVI and TCG explained 82 % of variation in LAI. Among all VIs, GCI, NDGI and RVI were found to be the best predictor of cotton yield during August, explaining 76-79 % variability (p<0.001). Based on spectro-biophysical analysis, VIs derived from RS data on July and August (anthesis and peak growth stages of cotton) is more reliable to use for modeling cotton yield (seed and lint yields together). Therefore, field data collection is recommended to perform during these months taking into account in-field crop condition and remotely sensed data acquisition date. In addition, September 5-20 is the second important period (i.e., cotton pick-up) to conduct yield data collection for establishment of relationships between cotton yields with VIs (July-August).

scholarly journals The application of APEX images in the assessment of the state of non-forest vegetation in the Karkonosze Mountains

2016 ◽  
Vol 20 (1) ◽  
pp. 21-27 ◽  
Author(s):  
Anna M. Jarocińska ◽  
Monika Kacprzyk ◽  
Adriana Marcinkowska-Ochtyra ◽  
Adrian Ochtyra ◽  
Bogdan Zagajewski ◽  
...  
Keyword(s):  
Photosynthetically Active Radiation ◽  
Vegetation Indices ◽  
Good Condition ◽  
Biosphere Reserve ◽  
Plant Structure ◽  
Biophysical Parameters ◽  
Area Index ◽  
Active Radiation ◽  
Natural Grasslands ◽  
Absorbed Photosynthetically Active Radiation

Abstract Information about vegetation condition is needed for the effective management of natural resources and the estimation of the effectiveness of nature conservation. The aim of the study was to analyse the condition of non-forest mountain communities: synanthropic communities and natural grasslands. UNESCO’s M&B Karkonosze Transboundary Biosphere Reserve was selected as the research area. The analysis was based on 40 field test polygons and APEX hyperspectral images. The field measurements allowed the collection of biophysical parameters - Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and chlorophyll content - which were correlated with vegetation indices calculated using the APEX images. Correlations were observed between the vegetation indices (general condition, plant structure) and total area of leaves (LAI), as well as fraction of Absorbed Photosynthetically Active Radiation (fAPAR). The outcomes show that the non-forest communities in the Karkonosze are in good condition, with the synanthropic communities characterised by better condition compared to the natural communities.

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