The Use of Remote Sensing to Assess the Effects of Water Stress on Wheat

1991 ◽  
Vol 27 (4) ◽  
pp. 423-429 ◽  
Author(s):  
R. K. Mahey ◽  
Rajwant Singh ◽  
S. S. Sidhu ◽  
R. S. Narang
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Grain Yield ◽  
Spectral Data ◽  
Growth And Development ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Canopy Cover ◽  
Area Index

SUMMARYGround-based radiometric measurements in the red and infrared bands were used to monitor the growth and development of wheat under irrigated and stressed conditions throughout the 1987–88 and 1988–89 growth cycles. Spectral data were correlated with plant height, leaf area index, total fresh and total dry biomass, plant water content and grain yield. The radiance ratio (R) and normalized difference vegetation index (NDVI) were highly and linearly correlated with yield, establishing the potential which remote sensing has for predicting grain yield. The correlation for R and NDVI was at a maximum between 75 and 104 days after sowing, corresponding with maximum green crop canopy cover. The differences in spectral response over time between irrigated and unirrigated crops allowed detection of water stress effects on the crop, indicating that a hand-held radiometer can be used to collect spectral data which can supply information on wheat growth and development.Efectos de lafalta de agua en el trigo

scholarly journals Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 940
Author(s):  
Rocío Ballesteros ◽  
Miguel A. Moreno ◽  
Fellype Barroso ◽  
Laura González-Gómez ◽  
José F. Ortega
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Canopy Cover ◽  
Remote Sensing Data ◽  
Multilinear Regression ◽  
Area Index ◽  
Canopy Development

The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features.

Prediction of crop productivity and evapotranspiration with two photosynthetic parameter regionalization methods

2012 ◽  
Vol 152 (1) ◽  
pp. 119-133 ◽  
Author(s):  
S. HU ◽  
X. MO
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Census Data ◽  
Wheat Yield ◽  
Ecosystem Model ◽  
Crop Productivity ◽  
Photosynthetic Parameter ◽  
Area Index

SUMMARYParameter regionalization is the foundation for the spatial application of an ecosystem model at the canopy level and has been improved greatly by remote sensing (RS). Photosynthetic rate is restricted by the carboxylation rate, which is limited by the activity of the enzyme Rubisco. By including RS normalized difference vegetation index (NDVI) and census data of grain yield at the county level in an ecosystem model (vegetation interface processes (VIP) model), the pattern of photosynthetic parameter Vcmax (maximum catalytic activity of Rubisco) of winter wheat was obtained and then used to simulate the wheat yield and evapotranspiration (ET) in the North China Plain (referred to as the Vcmax method). To evaluate its performance, the simulated yield and ET were compared with those derived by the leaf area index (LAI) method using the retrieved LAI from NDVI to drive the VIP model. The results showed that the Vcmax method performed better than the LAI method in highly productive fields, while the LAI method described the inter-annual variations of yield more favourably in fields with low productivity. Over the study area, average yield (4520 kg/ha) and seasonal ET (360 mm) simulated by the LAI method was slightly lower than those simulated using the Vcmax method (4730 kg/ha for yield and 372 mm for ET). Compared with the census data of yield, the relative root mean square error (RMSE) of grain yield with Vcmax method (0·17) was lower than that of the LAI method (0·20). In conclusion, the physical model with spatial Vcmax pattern from remote sensing is reliable for regional crop productivity prediction.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

Remote Sensing of Arctic Vegetation: Relations between the NDVI, Spatial Resolution and Vegetation Cover on Boothia Peninsula, Nunavut

ARCTIC ◽  
2009 ◽  
Vol 61 (1) ◽  
pp. 1 ◽  
Author(s):  
Gita J. Laidler ◽  
Paul M. Treitz ◽  
David M. Atkinson
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Moisture Regime ◽  
Percent Cover ◽  
Multispectral Data ◽  
Arctic Vegetation

Arctic tundra environments are thought to be particularly sensitive to changes in climate, whereby alterations in ecosystem functioning are likely to be expressed through shifts in vegetation phenology, species composition, and net ecosystem productivity (NEP). Remote sensing has shown potential as a tool to quantify and monitor biophysical variables over space and through time. This study explores the relationship between the normalized difference vegetation index (NDVI) and percent-vegetation cover in a tundra environment, where variations in soil moisture, exposed soil, and gravel till have significant influence on spectral response, and hence, on the characterization of vegetation communities. IKONOS multispectral data (4 m spatial resolution) and Landsat 7 ETM+ data (30 m spatial resolution) were collected for a study area in the Lord Lindsay River watershed on Boothia Peninsula, Nunavut. In conjunction with image acquisition, percent cover data were collected for twelve 100 m × 100 m study plots to determine vegetation community composition. Strong correlations were found for NDVI values calculated with surface and satellite sensors, across the sample plots. In addition, results suggest that percent cover is highly correlated with the NDVI, thereby indicating strong potential for modeling percent cover variations over the region. These percent cover variations are closely related to moisture regime, particularly in areas of high moisture (e.g., water-tracks). These results are important given that improved mapping of Arctic vegetation and associated biophysical variables is needed to monitor environmental change.

scholarly journals Determining nitrogen deficiencies for maize using various remote sensing indices

2022 ◽  
Author(s):  
Brayden W. Burns ◽  
V. Steven Green ◽  
Ahmed A. Hashem ◽  
Joseph H. Massey ◽  
Aaron M. Shew ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Nitrogen Fertilizer ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nitrogen Deficiency ◽  
Maize Grain Yield ◽  
Nitrogen Inputs ◽  
Chlorophyll Index ◽  
Maize Grain

AbstractDetermining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), triangle greenness index (TGI), normalized area vegetation index (NAVI) and chlorophyll index-green (CIgreen)] were evaluated for their ability to detect nitrogen deficiency and predict grain maize grain yield. Strip trials were established at two locations in Arkansas, USA, with nitrogen rate as the primary treatment. Remote sensing data was collected weekly with an unmanned aerial system (UAS) equipped with a multispectral and thermal sensor. Relationships among index value, nitrogen fertilizer rate and maize growth stage were evaluated. Green NDVI, RENDVI and CIgreen had the strongest relationship with nitrogen fertilizer treatment. Chlorophyll Index-green and GNDVI were the best predictors of maize grain yield early in the growing season when the application of additional nitrogen was still agronomically feasible. However, the logistics of late season nitrogen application must be considered.

scholarly journals Analyzing the Relationship between Animal Diversity and the Remote Sensing Vegetation Parameters: The Case of Xinjiang, China

2021 ◽  
Vol 13 (17) ◽  
pp. 9897
Author(s):  
Jinhui Wu ◽  
Haoxin Li ◽  
Huawei Wan ◽  
Yongcai Wang ◽  
Chenxi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Bird Diversity ◽  
Area Index ◽  
Sensing Data ◽  
Animal Diversity ◽  
The Mean ◽  
The Impact

An explicit analysis of the impact for the richness of species of the vegetation phenological characteristics calculated from various remote sensing data is critical and essential for biodiversity conversion and restoration. This study collected long-term the Normalized Difference Vegetation Index (NDVI), the Leaf Area Index (LAI), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and the Fractional Vegetation Cover (FVC), and calculated the six vegetation phenological characteristic parameters: the mean of the growing season, the mean of the mature season, the mean of the withered season, the annual difference value, the annual cumulative value, and the annual standard deviation in the Xinjiang Uygur Autonomous Region. The relationships between the vegetation phenological characteristics and the species richness of birds and mammals were analyzed in spatial distribution. The main findings include: (1) The correlation between bird diversity and vegetation factors is greater than that of mammals. (2) For remote sensing data, FAPAR is the most important vegetation parameter for both birds and mammals. (3) For vegetation phenological characteristics, the annual cumulative value of the LAI is the most crucial vegetation phenological parameter for influencing bird diversity distribution, and the annual difference value of the NDVI is the most significant driving factor for mammal diversity distribution.

scholarly journals Investigating Sentinel 2 Multispectral Imagery Efficiency in Describing Spectral Response of Vineyards Covered with Plastic Sheets

Agronomy ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1909
Author(s):  
Enrico Borgogno-Mondino ◽  
Laura de Palma ◽  
Vittorino Novello
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Table Grape ◽  
Optical Data ◽  
Strong Impact ◽  
Spectral Indices ◽  
Intrinsic Nature ◽  
Sentinel 2

The protection of vineyards with overhead plastic covers is a technique largely applied in table grape growing. As with other crops, remote sensing of vegetation spectral reflectance is a useful tool for improving management even for table grape viticulture. The remote sensing of the spectral signals emitted by vegetation of covered vineyards is currently an open field of investigation, given the intrinsic nature of plastic sheets that can have a strong impact on the reflection from the underlying vegetation. Baring these premises in mind, the aim of the present work was to run preliminary tests on table grape vineyards covered with polyethylene sheets, using Copernicus Sentinel 2 (Level 2A product) free optical data, and compare their spectral response with that of similar uncovered vineyards to assess if a reliable spectral signal is detectable through the plastic cover. Vine phenology, air temperature and shoot growth, were monitored during the 2016 growing cycle. Twenty-four Copernicus Sentinel 2 (S2, Level 2A product) images were used to investigate if, in spite of plastic sheets, vine phenology can be similarly described with and without plastic covers. For this purpose, time series of S2 at-the-ground reflectance calibrated bands and correspondent normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index, version two (MSAVI2) and normalized difference water index (NDWI) spectral indices were obtained and analyzed, comparing the responses of two covered vineyards with different plastic sheets in respect of two uncovered ones. Results demonstrated that no significant limitation (for both bands and spectral indices) was introduced by plastic sheets while monitoring spectral behavior of covered vineyards.

scholarly journals REMOTE SENSING APPROACH TO EVALUATE POST-FIRE VEGETATION STRUCTURE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1031-1038
Author(s):  
A. Novo ◽  
H. González-Jorge ◽  
J. Martínez-Sánchez ◽  
H. Lorenzo
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Point Cloud ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Critical Role ◽  
Canopy Cover ◽  
Forest Recovery ◽  
Average Height ◽  
Natural Ecosystems

Abstract. Spain is included in the top five European countries with the highest number of wildfires. Forest fire can produce significant impacts on the structure and functioning of natural ecosystems. After a forest fire, the evaluation of the damage severity and spatial patterns are important for forest recovery planning, which plays a critical role in the sustainability of the forest ecosystem. The process of forest recovery and the ecological and physiological functions of the burned forest area should be continuously monitored. Remote sensing technologies and in special LiDAR are useful to describe the structure of vegetation. The vegetation modelling and the initial changes of forest plant composition are studied in the forest after mapping the burned areas using Landsat-7 images and Sentinel-2 images. Normalized Burn Ratio (NBR) index and Normalized Difference Vegetation Index (NVVI) is calculated as well as the difference before and after fire. The evaluation of temporal changes of vegetation are analysed by statistical variables of the point cloud, average height, standard deviation and variance. Fraction Canopy Cover (FCC) also is calculated and the point cloud is classified following the fuel model by Prometheus. An analysis method based on satellite images was completed in order to analyse the evolution of vegetation in areas that suffer forest fire.

scholarly journals Investigação do Uso de Parâmetros Biofísicos na Influência do Microclima Dentro de Áreas Urbanas

2021 ◽  
Vol 25 (1) ◽  
pp. 16-20
Author(s):  
Denise Pereira Canedo Meira Vieira ◽  
Victor Hugo De Morais Danelichen ◽  
Mariane Batista de Lima Moraes Brandão Campos
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Spaces ◽  
Biophysical Parameters ◽  
Electronic Library ◽  
Area Index

Diante da necessidade de obtenção de informações relacionadas ao microclima e influência da vegetação dentro de um perímetro urbano na qualidade de vida dos seus habitantes se define  como parâmetros biofísicos a serem estudados nesta pesquisa o Normalized Difference Vegetation Index (NDVI) e Leaf Area Index (LAI).  Considerando que o Sensoriamento Remoto é uma tecnologia de baixo custo e fácil aquisição podendo ser obtidas gratuitamente, via banco de dados disponíveis na Internet, verifica-se que  o sensoriamento pode ser utilizado como fonte confiável de levantamento desses parâmetros. Este estudo tem como objetivo analisar a produção científica sobre o uso do sensoriamento remoto como tecnologia alternativa para obtenção de informações de parâmetros biofísicos, como o NDVI e LAI, possibilitando a preservação e o planejamento da vegetação nos espaços urbanos. O artigo se trata de uma revisão narrativa realizada através de consultas aos bancos de dados Scientific Electronic Library Online (SciELO), Literatura Latino-Americana e, principalmente, do banco de dados Scopus (Elsevier) da CAPES. Como critérios de inclusão foram aplicados: artigos com disponibilidade completa de 2010 até 2020 e com relação direta com o estudo. É possível concluir que como o ambiente sofre alterações constantes pela ação antrópica e a interpretação de imagens de satélite é uma fonte direta de se determinar a dinâmica dos processos envolvidos em tais alterações, a fotointerpretação e o processamento digital de imagens assumem papel de grande importância. Tais ferramentas permitem fornecer subsídios para a compreensão dos fenômenos ambientais, além da possibilidade de planejamento estratégico no planejamento urbano. As revisões bibliográficas realizadas indicam a fotointerpretação e o processamento digital de imagens como ferramentas ainda pouco utilizadas para estimar os parâmetros biofísicos no contexto urbano.   Palavras-chave: Sensoriamento Remoto. Espaços Urbanos. NDVI e LAI.   AbstractGiven the need to obtain information related to the vegetation microclimate and influence within an urban perimeter on the quality of life of its inhabitants, it was defined as biophysical parameters to be studied in this research the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI).  Considering that Remote Sensing is a low cost and easy acquisition technology and can be obtained free of charge via the database available on the Internet, it will be verified that sensing can be used as a reliable source of survey of these parameters. This study aims to analyze the scientific production on the use of remote sensing as an alternative technology to obtain information from biophysical parameters, such as NDVI and LAI, enabling the  vegetation preservation and planning in urban spaces. The article is a narrative review carried out through consultations with the Databases Scientific Electronic Library Online (SciELO), Latin American Literature and mainly the  Scopus database (Elsevier)  of CAPES. Inclusion criteria were applied: articles with complete availability from 2010 to 2020 and with direct relation to the study. It is possible to conclude that the environment undergoes constant changes by anthropic action and the satellite images interpretation is a direct source for  determining the processes dynamics involved in such changes, photointerpretation and digital image processing play a major role. Such tools allow to provide subsidies for the understanding of environmental phenomena, in addition to the possibility of strategic planning in urban planning. The bibliographic reviews performed indicate photointerpretation and digital image processing as tools still little used to estimate biophysical parameters in the urban context.   Keywords: Remote Sensing. Urban Spaces. NDVI and LAI

scholarly journals Orbital spectral variables, growth analysis and sugarcane yield

2009 ◽  
Vol 66 (4) ◽  
pp. 451-461 ◽  
Author(s):  
Maurício dos Santos Simões ◽  
Jansle Vieira Rocha ◽  
Rubens Augusto Camargo Lamparelli
Keyword(s):  
Spectral Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Analysis ◽  
Total Biomass ◽  
Large Area ◽  
Landsat Images ◽  
Biophysical Parameters ◽  
Area Index ◽  
Crop Development

Temporal analysis of crop development in commercial fields requires tools for large area monitoring, such as remote sensing. This paper describes the temporal evolution of sugar cane biophysical parameters such as total biomass (BMT), yield (TSS), leaf area index (LAI), and number of plants per linear meter (NPM) correlated to Landsat data. During the 2000 and 2001 cropping seasons, a commercial sugarcane field in Araras, São Paulo state, Brazil, planted with the SP80-1842 sugarcane variety in the 4th and 5th cuts, was monitored using nine Landsat images. Spectral data were correlated with agronomic data, obtained simultaneously to the imagery acquisition. Two methodologies were used to collect spectral data from the images: four pixels (2 × 2) window and average of total pixels in the field. Linear and multiple regression analysis was used to study the spectral behavior of the plants and to correlate with agronomic variables (days after harvest-DAC, LAI, NPM, BMT and TSS). No difference was observed between the methodologies to collect spectral data. The best models to describe the spectral crop development in relation to DAC were the quadratic and cubic models. Ratio vegetation index and normalized difference vegetation index demonstrated correlation with DAC, band 3 (B3) was correlated with LAI, and NDVI was well correlated with TSS and BMT. The best fit curves to estimate TSS and BMT presented r² between 0.68 and 0.97, suggesting good potential in using orbital spectral data to monitor sugarcane fields.

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