scholarly journals Remote sensing as a tool to determine biophysical parameters of irrigated seed corn crop

2020 ◽  
Vol 41 (2) ◽  
pp. 435-446
Author(s):  
Robson Argolo dos Santos ◽  
◽  
Jesiele Silva da Divincula ◽  
Karine Rabelo de Oliveira ◽  
Luan Peroni Venancio ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Biophysical Parameters ◽  
Seed Corn ◽  
Corn Crop

scholarly journals Monitoramento Espaço-Temporal da Detecção de Mudanças em Vegetação de Caatinga por Sensoriamento Remoto no Semiárido Brasileiro

2020 ◽  
Vol 13 (1) ◽  
pp. 286 ◽  
Author(s):  
Alan Cézar Bezerra ◽  
Jhon Lennon Bezerra da Silva ◽  
Douglas Alberto De Oliveira Silva ◽  
Pedro Henrique Dias Batista ◽  
Liliane Da Cruz Pinheiro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Environmental Degradation ◽  
Vegetation Index ◽  
Digital Processing ◽  
Soil Condition ◽  
Response Patterns ◽  
Biophysical Parameters ◽  
Surface Data ◽  
Caatinga Vegetation

O sensoriamento remoto pode ser utilizado no monitoramento ambiental de parâmetros biofísicos micrometeorológicos nas regiões semiáridas do Brasil. Objetivou-se monitorar o risco da degradação ambiental através da detecção de mudanças da superfície no semiárido por sensoriamento remoto. A pesquisa foi desenvolvida através do processamento digital de imagens de satélite para Serra Talhada, Pernambuco. Foram coletados dados de superfície para subsidiar o algoritmo do balanço de energia da superfície terrestre (SEBAL) na estimativa do albedo e temperatura da superfície e o índice de vegetação ajustado as condições do solo (SAVI). Além disso, se desenvolveu mapas temáticos do grau do risco de degradação. Os mapas da degradação foram submetidos a avaliação estatística de qualidade temática, por matriz de confusão. O SAVI apresentou-se sensível à chuva, tendo na estação chuvosa os maiores valores e na estação seca os menores, período que o albedo e a temperatura apresentaram valores elevados, indicando vulnerabilidade à degradação das áreas com pouca vegetação e solo exposto. Os mapas do risco de degradação destacaram características semelhantes aos padrões de respostas do SAVI, albedo e temperatura. O monitoramento espaço-temporal dos parâmetros biofísicos e do risco de degradação permitirá o planejamento e gestão dos recursos hídricos e naturais da região semiárida. Spatial-Temporal Monitoring Detection of Changes in Caatinga Vegetation by Remote Sensing in the Brazilian Semiarid A B S T R A C TRemote sensing can be used for environmental monitoring of micrometeorological biophysical parameters in the semiarid regions of Brazil. The present investigation aimed to monitor the risk of environmental degradation by detecting surface changes in the semiarid by means of remote sensing. The research was developed through digital processing of satellite images for Serra Talhada, Pernambuco, Brazil. Surface data were collected to support the Surface Energy Balance algorithm (SEBAL) to estimate the albedo and the surface temperatures as well as the soil condition adjusted to the vegetation index (SAVI). Furthermore, thematic maps were developed for the levels of risk of degradation and statistical evaluation was performed on the thematic quality by means of confounding matrix. The SAVI was sensitive to precipitation, displaying the highest values for the rainy season and the lowest for the dry season, for which the albedo and the surface temperature presented higher values, thus indicating vulnerability to degradation in areas of scarce vegetation and exposed soil. The risk of degradation maps highlighted characteristics similar to SAVI response patterns, albedo and surface temperature. The spatiotemporal monitoring of biophysical parameters and the risk of degradation will enable both the planning and the management of water and natural resources in the semiarid region.Keywords: Agrometeorology, Caatinga, Environmental Degradation, Deforestation; Environmental Impacts.

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 Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest

2020 ◽  
Vol 12 (9) ◽  
pp. 1519 ◽  
Author(s):  
Sujit Madhab Ghosh ◽  
Mukunda Dev Behera ◽  
Somnath Paramanik
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Forest Canopy ◽  
Canopy Height ◽  
Series Data ◽  
Learning Models ◽  
Biophysical Parameters ◽  
Height Estimation ◽  
Area Index ◽  
Machine Learning Models

Canopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, while Interferometric SAR data from commercial satellites are costly. High temporal decorrelation makes freely available Sentinel-1 interferometric data mostly unsuitable for tropical forests. Alternatively, other remote sensing and biophysical parameters have shown good correlation with forest canopy height. The study objective was to establish and validate a methodology by which forest canopy height can be estimated from SAR and optical remote sensing data using machine learning models i.e., Random Forest (RF) and Symbolic Regression (SR). Here, we analysed the potential of Sentinel-1 interferometric coherence and Sentinel-2 biophysical parameters to propose a new method for estimating canopy height in the study site of the Bhitarkanika wildlife sanctuary, which has mangrove forests. The results showed that interferometric coherence, and biophysical variables (Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) have reasonable correlation with canopy height. The RF model showed a Root Mean Squared Error (RMSE) of 1.57 m and R2 value of 0.60 between observed and predicted canopy heights; whereas, the SR model through genetic programming demonstrated better RMSE and R2 values of 1.48 and 0.62 m, respectively. The SR also established an interpretable model, which is not possible via any other machine learning algorithms. The FVC was found to be an essential variable for predicting forest canopy height. The canopy height maps correlated with ICESat-2 estimated canopy height, albeit modestly. The study demonstrated the effectiveness of Sentinel series data and the machine learning models in predicting canopy height. Therefore, in the absence of commercial and rare data sources, the methodology demonstrated here offers a plausible alternative for forest canopy height estimation.

Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

Pedosphere ◽  
2009 ◽  
Vol 19 (2) ◽  
pp. 176-188 ◽  
Author(s):  
Xiao-Hua YANG ◽  
Fu-Min WANG ◽  
Jing-Feng HUANG ◽  
Jian-Wen WANG ◽  
Ren-Chao WANG ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Regression Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Biophysical Parameters ◽  
Radial Basis

scholarly journals Estimating biophysical parameters of rice with remote sensing data using support vector machines

2011 ◽  
Vol 54 (3) ◽  
pp. 272-281 ◽  
Author(s):  
XiaoHua Yang ◽  
JingFeng Huang ◽  
YaoPing Wu ◽  
JianWen Wang ◽  
Pei Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Support Vector Machines ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Biophysical Parameters ◽  
Sensing Data ◽  
Vector Machines

Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems

1999 ◽  
Vol 23 (3) ◽  
pp. 359-390 ◽  
Author(s):  
Paul M. Treitz ◽  
Philip J. Howarth
Keyword(s):  
Remote Sensing ◽  
Ecosystem Health ◽  
Forest Ecosystems ◽  
Structural Features ◽  
Biophysical Parameters ◽  
Physiological Character ◽  
Forest Condition ◽  
Hyperspectral Sensors ◽  
Physiological Information ◽  
The Relationship

Remote sensing has demonstrated wide applicability in the area of estimating and mapping forest physical and structural features. Focus in recent years has been directed towards measuring the biophysical/physiological character of forest ecosystems in order to estimate and predict forest ecosystem health and sustainability. The following reviews the relationship between forest condition and reflectance; remote-sensing measurements (and derivatives) that provide biophysical/physiological information; and the potential of hyperspectral sensors in the measurement of these parameters.

scholarly journals Potential contributions of remote sensing to ecosystem service assessments

2014 ◽  
Vol 38 (3) ◽  
pp. 328-353 ◽  
Author(s):  
Margaret E. Andrew ◽  
Michael A. Wulder ◽  
Trisalyn A. Nelson
Keyword(s):  
Remote Sensing ◽  
Ecosystem Services ◽  
Land Use Planning ◽  
Ecosystem Service ◽  
Service Providers ◽  
Plant Traits ◽  
Environmental Data ◽  
Data Sets ◽  
Biophysical Parameters ◽  
Ecological Processes

Ecological and conservation research has provided a strong scientific underpinning to the modeling of ecosystem services (ESs) over space and time, by identifying the ecological processes and components of biodiversity (ecosystem service providers, functional traits) that drive ES supply. Despite this knowledge, efforts to map the distribution of ESs often rely on simple spatial surrogates that provide incomplete and non-mechanistic representations of the biophysical variables they are intended to proxy. However, alternative data sets are available that allow for more direct, spatially nuanced inputs to ES mapping efforts. Many spatially explicit, quantitative estimates of biophysical parameters are currently supported by remote sensing, with great relevance to ES mapping. Additional parameters that are not amenable to direct detection by remote sensing may be indirectly modeled with spatial environmental data layers. We review the capabilities of modern remote sensing for describing biodiversity, plant traits, vegetation condition, ecological processes, soil properties, and hydrological variables and highlight how these products may contribute to ES assessments. Because these products often provide more direct estimates of the ecological properties controlling ESs than the spatial proxies currently in use, they can support greater mechanistic realism in models of ESs. By drawing on the increasing range of remote sensing instruments and measurements, data sets appropriate to the estimation of a given ES can be selected or developed. In so doing, we anticipate rapid progress to the spatial characterization of ecosystem services, in turn supporting ecological conservation, management, and integrated land use planning.

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