scholarly journals Estimation of soil heat flux in a neotropical Wetland region using remote sensing techniques

2014 ◽  
Vol 29 (4) ◽  
pp. 469-482 ◽  
Author(s):  
Victor Hugo de Morais Danelichen ◽  
Marcelo Sacardi Biudes ◽  
Maísa Caldas Souza ◽  
Nadja Gomes Machado ◽  
Bernardo Barbosa da Silva ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Vegetation Index ◽  
Correlation Coefficients ◽  
Remote Sensing Data ◽  
Soil Heat Flux ◽  
Biophysical Parameters ◽  
Area Index ◽  
Satellite Sensors ◽  
Remote Sensing Techniques

The direct estimation of the soil heat flux (G) by remote sensing data is not possible. For this, several models have been proposed empirically from the relation of G measures and biophysical parameters of various types of coverage or not vegetated in different places on earth. Thus, the objective of this study was to evaluate the relation between G/Rn ratio and biophysical variables obtained by satellite sensors and evaluate the parameterization of different models to estimate G spatially in three sites with different soil cover types. The net radiation (Rn) and G were measured directly in two pastures at Miranda Farm and Experimental Farm and and Monodominant Forest of Cambará. Rn, G, and G/Rn ratio and MODIS products, such as albedo (α), surface temperature (LST), vegetation index (NDVI) and leaf area index (LAI) varied seasonally at all sites and inter-sites. The sites were different from each other by presenting different relation between measures of Rn, G and G/Rn ratio and biophysical parameters. Among the original models, the model proposed by Bastiaanssen (1995) showed the best performance with r = 0.76, d = 0.95, MAE = 5.70 W m-2 and RMSE = 33.68 W m-2. As the reparameterized models, correlation coefficients had no significant change, but the coefficient Willmott (d) increased and the MAE and RMSE had a small decrease.

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.

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.

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 RELATIONSHIP OF SPECTRAL REFLECTANCE AND NDVI TO SOME SOIL PROPERTIES OF BRICKS FACTORIES SOILS IN NAHRAWAN AREA,BAGHDAD IRAQ

2019 ◽  
Vol 50 (3) ◽  
Author(s):  
R. K. Abdullatiff
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Spectral Reflectivity ◽  
Tigris River ◽  
The Difference ◽  
Remote Sensing Techniques ◽  
The One ◽  
Relationship Of

A study was conducted to investigate the effect of the brick industry on the environmental system of these project soils of the brick factories in Alnahrawan district. Remote sensing techniques was used to study the relationship between the spectral reflectivity and the vegetative index on the one hand and some surface soil characters of the project and to determine the variation in vegetation cover for the same area and for two different periods.Ten sites were selected to study spectral reflectivity under similar geomorphological conditions near the brickworks project in the Anahrawan district with an area of 10,000 hectares. Soil samples were taken from the surface and at a depth of 0-30 cm. Some chemical and physical characters of research soil were analyzed in the soil department laboratories, college of Agriculture, Baghdad University.Several satellite images taken from the satellite Land sat (ETM) 2013 and another from same satellite in 1990 T.M to determining the change between the two periods. After obtaining remote sensing data (reflectivity and vegetation index).the correlation analysis was carried out between these data. It was observed that the soil salinity values were decreased due to the drainage that the area was confined between the Tigris River and the Diyala tributary which leads to good natural drainage.The attached tables indicate that thedigital numbers of the soil sampling sites in 2013 are highly significant correlated, While some of the characters did not show the use of this region industrially. After calculating the difference between the two images to determine the change. A 100% change was observed and the vegetation cover was sharply reduced between the two images. as well as the extension of the land of empty land, although these lands are still suitable for agriculture.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

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 Analisis Tutupan Mangrove Taman Nasional Berbak–Sembilang melalui Citra Landsat-8 dan Pemantauan LAI

2021 ◽  
Vol 24 (3) ◽  
pp. 393-401
Author(s):  
Tengku Zia Ulqodry ◽  
Andreas Eko Aprianto ◽  
Andi Agussalim ◽  
Riris Aryawati ◽  
Afan Absori
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Area Index ◽  
Sensing Data ◽  
Canopy Coverage

Berbak Sembilang National Park of South Sumatra Region (BSNP South Sumatera) is the largest mangrove ecosystem in the western part of Indonesia. Monitoring of mangrove coverage in BSNP South Sumatera carried out using Landsat-8 imagery data based on NDVI values (Normalized Difference Vegetation Index) integrated with mangrove LAI (Leaf Area Index) data. The research purpose was to analyze the mangrove coverage and mapping the density of the mangrove vegetation canopy with the integration of remote sensing data and LAI. This research conducted field survey with LAI measurement of mangrove canopy coverage and integrated with remote sensing data to validate map. The determination and correlation coefficient of NDVI and LAI value of canopy coverage was high (R2 = 0.69 ; r = 83.07).The results of research indicated that the overall distribution of the mangrove area was 94,622.05 ha. The NDVI image integration map with LAI resulted in 4 mangrove canopy density classes consisted of rare canopy (688.80 ha ; 0.73%), moderately dense canopy (1,139.55 ha ; 1.2%), dense canopy (35,003.46 ha ; 37%), and very dense canopy (57,790.20 ha ; 61.07%). Taman Nasional Berbak Sembilang wilayah Sumatera Selatan (TNBS Sumsel) merupakan kawasan ekosistem mangrove terluas di wilayah Indonesia bagian barat. Pemantauan kerapatan kanopi vegetasi mangrove di TNBS Sumsel dilakukan menggunakan data Citra Landsat-8 berdasarkan nilai NDVI (Normalized Difference Vegetation Index) yang diintegrasikan dengan data LAI (Leaf Area Index) mangrove di lapangan. Penelitian ini bertujuan untuk menganalisis tutupan vegetasi mangrove dan memetakan sebaran kerapatan kanopi mangrove dengan integrasi data penginderaan jauh dan LAI. Penelitian ini menggunakan metode pengolahan data survei lapangan dan hasil pengolahan citra satelit. Nilai koefisien determinasi dan korelasi antara nilai NDVI dengan nilai LAI tutupan Kanopi di Lapangan dikategorikan tinggi (R2 = 0,69 ; r = 83,07). Hasil penelitian menunjukkan tutupan mangrove secara keseluruhan seluas 94.622,05 ha. Peta integrasi citra NDVI dengan LAI mangrove di lapangan menghasilkan 4 kelas kerapatan kanopi mangrove yakni kanopi jarang seluas 688,80 ha (0,73%), kanopi sedang seluas 1.139,55 ha (1,2%), kanopi lebat seluas 35.003,46 ha (37%), dan kanopi sangat lebat seluas 57.790,20 ha (61,07%).

scholarly journals Comparison of different methods for measuring leaf area index in a mixed grassland

2007 ◽  
Vol 87 (4) ◽  
pp. 803-813 ◽  
Author(s):  
Yuhong He ◽  
Xulin Guo ◽  
John F Wilmshurst
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Canopy Reflectance ◽  
Area Index ◽  
Grassland Ecosystems ◽  
Grass Communities

Available LAI instruments have greatly increased our ability to estimate leaf area index (LAI) non-destructively. However, it is difficult to infer from existing studies which instrument has the advantages in measuring LAI over other instruments for grassland ecosystems. The objective of our study was to compare the LAI estimates by two instruments (AccuPAR, and LAI2000), and correlate the LAI measurements to remote sensing data for a mixed grassland. Leaf area index of four grass communities was measured by both the destructive method and instruments. Ground canopy reflectance was measured and further calculated to be LAI-related vegetation indices. Statistical analysis showed that destructively sampled LAI ranged from 0.61 to 5.7 in the study area. Both instruments underestimated LAI in comparison with the destructive method. However, the LAI2000 is better than AccuPAR for estimating LAI. Comparison of four grass communities indicated that the lower the grass LAI, the greater the underestimated percentage of LAI values collected by both instruments. The adjusted transformed soil-adjusted vegetation index (ATSAVI), was the best LAI estimator in the mixed grassland. Key words: Leaf area index, sward structure, nondestructive vegetation sampling, hyperspectral remote sensing, mixed grass prairie

scholarly journals ESTIMATIVA DE PARÂMETROS BIOFÍSICOS E EVAPOTRANSPIRAÇÃO REAL NO SEMIÁRIDO PERNAMBUCANO UTILIZANDO SENSORIAMENTO REMOTO

Irriga ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 64-75 ◽  
Author(s):  
Frederico Abraão Costa Lins ◽  
Diego Cezar Dos Santos Araújo ◽  
Jhon Lennon Bezerra Da Silva ◽  
Pabricio Marcos Oliveira Lopes ◽  
José Diorgenes Alves Oliveira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Distribution ◽  
Semiarid Region ◽  
Landsat 8 ◽  
Biophysical Parameters ◽  
Area Index ◽  
Rainfall Index ◽  
E Mail

ESTIMATIVA DE PARÂMETROS BIOFÍSICOS E EVAPOTRANSPIRAÇÃO REAL NO SEMIÁRIDO PERNAMBUCANO UTILIZANDO SENSORIAMENTO REMOTO  FREDERICO ABRAÃO COSTA LINS1; DIEGO CEZAR DOS SANTOS ARAÚJO2; JHON LENNON BEZERRA DA SILVA2; PABRÍCIO MARCOS OLIVEIRA LOPES3; JOSÉ DIORGENES ALVES OLIVEIRA2 E ANDREY THYAGO CARDOSO SANTOS GOMES DA SILVA1 1 Mestrandos em Engenharia Agrícola – Departamento de Engenharia Agrícola, Universidade Federal Rural de Pernambuco (UFRPE). Av. D. Manoel de Medeiros, SN; Dois Irmãos, Recife, Pernambuco, Brasil; CEP: 52171-900. E-mail: [email protected] (Autor para correspondência); [email protected]; 2 Mestres em Engenharia Agrícola e Doutorandos – Departamento de Engenharia Agrícola da UFRPE. E-mail: [email protected]; [email protected]; [email protected]; 3 Doutor em Sensoriamento Remoto; Professor adjunto da Universidade Federal Rural de Pernambuco (UFRPE), Recife, Pernambuco, Brasil. E-mail: [email protected].   1        RESUMO Objetivou-se estimar e avaliar a distribuição espaço-temporal de parâmetros biofísicos e a evapotranspiração real diária para o município de Arcoverde, Pernambuco, durante estações seca e chuvosa, utilizando imagens orbitais do satélite Landsat-8 de sensores OLI/TIRS, para as datas de passagem: 14/01/2015 e 02/12/2016, processadas com o algoritmo SEBAL. Foram gerados os seguintes mapas temáticos: Índice de Vegetação da Diferença Normalizada (NDVI), Índice de Área Foliar (IAF), albedo e temperatura de superfície (Ts), saldo de radiação instantâneo (Rn) e evapotranspiração real diária (ETr). O NDVI foi maior em janeiro de 2015 e o albedo e Ts foram maiores em 2016 (0,23 e 37,44 °C), ao passo que em 2015, os valores foram de 0,20 e 34,11 °C, relacionando-se às condições meteorológicas e uso do solo. O Rn variou de 520,06 a 540,22 W m-2 nos dois anos e, para a ETr, verificou-se a maior média em janeiro de 2015 (3,41 mm dia-1), devido ao maior NDVI e precipitações, evidenciando maior disponibilidade de água na vegetação e no solo. As técnicas de sensoriamento remoto possibilitaram o monitoramento do município de Arcoverde-PE, determinando os parâmetros biofísicos nos diferentes usos do solo, predizendo os processos futuros de degradação e consequente desertificação na localidade. Palavras-chave: caatinga, vegetação, monitoramento ambiental, uso do solo, agrometeorologia.  LINS, F. A. C.; ARAÚJO, D. C. dos S.; SILVA, J. L. B. da; LOPES, P. M. O.; OLIVEIRA, J. D. A.; SILVA, A. T. C. S. G. daBIOPHYSICAL PARAMETERS ESTIMATE AND ACTUAL EVAPOTRANSPIRATION IN THE SEMIARID REGION OF THE STATE OF PERNAMBUCO, BRAZIL, USING REMOTE SENSING                                                     2        ABSTRACT The purpose of this paper is to estimate and evaluate the spatial-temporal distribution of biophysical parameters and the actual daily evapotranspiration index for the municipality of Arcoverde, Pernambuco, during the dry and rainy season, using orbital images from the Landsat-8 satellite and OLI/TIRS sensors for the following dates in which the satellite passed over the region: 01/14/2015 and 02/12/2016, processed using the SEBAL algorithm. The following thematic maps were generated: Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), albedo and surface temperature (Ts), Net radiation (Rn) and daily reference evapotranspiration (ETr). The NDVI was higher on January 2015 and the albedo and Ts were higher in 2016 (0.23 and 37.44 °C), whereas in 2015, the values were 0.20 and 34.11 °C, related to the meteorological conditions and the land use. The Rn ranged from 520.06 to 540.22 W m-² in two years of study and, for the ETr, the highest average was recorded on January 2015 (3.41 mm day-1), due to the higher NDVI and rainfall index, evidencing greater availability of water in the vegetation and soil. The remote sensing techniques allowed the monitoring of the municipality of Arcoverde, determining the biophysical parameters in the different uses of soil, anticipating the future degradation processes and consequent desertification in the place. Keywords: caatinga, vegetation, environmental monitoring, use of the soil, agrometeorology. 

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