scholarly journals VALIDATION OF SOIL USES AROUND RESERVOIRS IN THE SEMI-ARID THROUGH IMAGE CLASSIFICATION

2021 ◽  
Vol 34 (3) ◽  
pp. 670-681
Author(s):  
EFRAIM MARTINS ARAÚJO ◽  
GEORGE LEITE MAMEDE
Keyword(s):  
Supervised Classification ◽  
Spectral Response ◽  
Kappa Coefficient ◽  
High Spectral Resolution ◽  
Native Vegetation ◽  
Landsat 8 ◽  
Method Of Maximum Likelihood ◽  
Remotely Piloted Aircraft ◽  
Hyperion Image ◽  
Semi Arid

ABSTRACT The work evaluated the potential for discrimination of land use and occupation around reservoirs, using spectral information obtained by multispectral, hyperspectral satellites and images obtained with an ARP (remotely piloted aircraft). The research analyzed the performance of different images classification techniques applied to multispectral and hyperspectral sensors for the detection and differentiation of soil classes around the Paus Brancos and Marengo reservoirs, located in Settlement 25 of Maio. The classes identified based on surveys in campaigns carried out in 2014 and 2015 around the reservoirs were: water, macrophytes, exposed soil, native vegetation, agriculture, thin and ebbing vegetation, in addition to the cloud and cloud shadow targets. The performance of the classifiers applied to the image of the Hyperion sensor was, in general, superior to those obtained in Landsat 8 image, which can be explained by the high spectral resolution of the first, which facilitates the differentiation of targets with similar spectral response. For validation of the supervised classification method of Maximum Likelihood, Landsat 8 (08/24/2015) and Hyperion (08/28/2015) images were used. The results of the application indicated a good performance of the classifier associated with the RGB composition of the chosen Hyperion image (bands R - 51, G - 161, B - 19) in the detection of the classes around this reservoir, producing a Kappa coefficient of 0.83. The availability of data from the Hyperion sensor is very restricted, which hinders the development of continued research, thus the use of images surpassed by RPA is extremely viable.

scholarly journals Procedures for calculation of the albedo with OLI-Landsat 8 images: Application to the Brazilian semi-arid

2016 ◽  
Vol 20 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Bernardo B. da Silva ◽  
Alexandra C. Braga ◽  
Célia C. Braga ◽  
Leidjane M. M. de Oliveira ◽  
Suzana M. G. L. Montenegro ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Land Use ◽  
Surface Albedo ◽  
Elevation Angle ◽  
Native Vegetation ◽  
Landsat 8 ◽  
Image Metadata ◽  
Degree Of Differentiation ◽  
Semi Arid ◽  
Calculation Procedures

ABSTRACT The surface albedo plays an important role in the exchanges of energy and mass in the planetary boundary layer. Therefore, changes in albedo affect the balance of radiation and energy at the surface, which can be detected with its monitoring. Albedo determination has been performed through various sensors, but there is not yet any publication dealing with albedo calculation procedures using OLI (Operational Land Imager) - Landsat 8 images. The objective of the study is to present the procedures for computing the albedo with OLI images and map it in irrigated areas of the São Gonçalo Irrigated District, PB, Brazil. Images of the year 2013, path 215 and row 65, were selected. The data necessary for calculating the albedo were extracted from each image metadata: additive and multiplicative terms of radiance and reflectance, and sun elevation angle. There were large differences between the albedo values of irrigated plots, water bodies and native vegetation. The albedo obtained with OLI images provides a higher degree of differentiation of the various types of land use, due to the substantial increase in the radiometric resolution of this new sensor.

scholarly journals Geoprocessing and Landscape Ecology for Assessment Fragmentation and Connectivity of the Habitats of the Microregion of Ceres, Goiás (Brazil)

2021 ◽  
Vol 17 (3) ◽  
Author(s):  
Josimar dos Reis de Souza ◽  
Laís Naiara Gonçalves dos Reis
Keyword(s):  
Economic Development ◽  
Landscape Ecology ◽  
Supervised Classification ◽  
Contemporary Problem ◽  
Computational Algorithm ◽  
Automatic Process ◽  
Native Vegetation ◽  
Landsat 8 ◽  
Natural Environments ◽  
Homogeneous Regions

This study aimed to map and evaluate the evolution of habitat fragmentation between 2009 and 2018, using the Microregion of Ceres (Goiás) as a sample reference, using principles of Landscape Ecology. The methodology comprised the mapping of the fragments in the two years analyzed, using the OLI/Landsat 8 sensor, using scenes 222/70 and 222/71. The SPRING 5.2 software was used, where the supervised classification was performed, applying the semi-automatic process. The computational algorithm applied to classify the scenes was Maxver, which classifies pixel by pixel and groups the information of each one into homogeneous regions. After extracting the fragments of native vegetation, the methodology proposed by Juvanhol et al. (2011), in which the fragments were grouped into classes: Very Small (MP) ≤5 hectares; Small (P) ≥5.01 and ≤10 hectares; Medium (M) ≥10.01 and ≤100 hectares and Large (G) ≥100.01 hectares. For the analysis based on metrics in Landscape Ecology, the ArcGis 9.2 Patch Analyst extension was used. The results showed the expansion of vegetation cover areas in the study area, concentrated on tops of hills, APP and legal reserves. However, they pointed out intense fragmentation of native vegetation, which hinders the performance of fragments as habitats. It is considered that, from the contemporary problem of degradation of natural environments to the detriment of economic development, studies like this are necessary in order to identify existing environmental problems and propose strategies to minimize and mitigate ecological imbalances.

scholarly journals Influence of Climatic Seasonality on a Survey of Land Use and Cover in the Semi-arid Region

2018 ◽  
Vol 10 (11) ◽  
pp. 311
Author(s):  
Anthony Rafael Soares Maia ◽  
Fernando Bezerra Lopes ◽  
Eunice Maia de Andrade
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Arid Region ◽  
Spectral Response ◽  
Human Action ◽  
Landsat 8 ◽  
Semi Arid Region ◽  
Classification Of Images ◽  
Semi Arid ◽  
Land Use And Cover

The dynamics of land use and land cover in watersheds of the Brazilian semi-arid region is not only influenced by human action, but also by the climatic seasonality of the region. Knowledge of the relationship between surveys of land use and land cover using geotechnology and the climatic seasonality of semi-arid regions is necessary. The aim of this study was to map and classify land use and cover in the watershed of the Orós reservoir (WSOR) with the help of geotechnology, and to identify the influence exerted by the climate on variations in the area of each class. The survey of land use and cover was carried out by means of the MAXVER method of classification of images from 2003, 2005, 2008 and 2013 from the LANDSAT 5 and LANDSAT 8 satellites. The areas of each class displayed dynamics influenced not only by human action but also by such factors as climate, topography and plant physiology. Years with high rainfall favoured classes such as thin scrub and dense scrub, with the opposite being seen in years considered as dry, when there was a considerable increase in areas of the anthropogenic class. Changes in the areas are caused by alterations in the deciduous vegetation; with leaf-fall during the dry season, these areas come to have the spectral response of areas with similar characteristics to the anthropogenic class. More-elevated regions favoured the presence of the dense-scrub class due to the microclimate and to the greater difficulty such areas present to human action.

PEMANFAATAN DATA PENGINDERAAN JAUH DAN TEKNOLOGI SIG UNTUK MENGIDENTIFIKASI BUILT UP AREA DAN KAITANNYA DENGAN RTRW DI KABUPATEN KENDAL

2019 ◽  
Vol 3 ◽  
pp. 521
Author(s):  
Mailendra Mailendra
Keyword(s):  
Supervised Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm

Integrasi data penginderaan jauh dengan sistem informasi geografis telah banyak dikembangkan, dan salah satunya dalam melihat perkembangan lahan terbangun. Tujuan penelitian ini adalah untuk melihat perkembangan lahan terbangun dan kesesuaiannya dengan Rencana Pola Ruang Kabupaten Kendal. Kemudian metode yang digunakan yaitu metode supervised classification dengan memanfaatkan data citra landsat 5 TM dan landsat 8 OLI yang selanjutnya dihitung luas dari masing lahan terbangun berdasarkan data temporal tahun 1990, tahun 2015 dan tahun 2017. Setelah diketahui luas lahan terbangun selanjutnya dioverlay dengan peta rencana pola ruang Kabupaten Kendal untuk melihat sesuai atau tidaknya penempatan lahan terbangun tersebut. Adapun hasil penelitiannya yaitu setiap tahunnya lahan terbangun terus meningkat di Kabupaten Kendal, terjadi peningkatan yang cukup signifikan dalam dua tahun terakhir yaitu tahun 2015 hingga tahun 2017. Selanjutnya diperkirakan 88 % lahan terbangun tersebut telah sesuai dengan RTRW karena sudah berada pada kawasan budidaya.

scholarly journals Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  
Keyword(s):  
Land Cover ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Fuel Mapping ◽  
Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

scholarly journals Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

scholarly journals EXTRACTING BUILT-UP FEATURES IN COMPLEX BIOPHYSICAL ENVIRONMENTS BY USING A LANDSAT BANDS RATIO

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 79-85
Author(s):  
A. H. Ngandam Mfondoum ◽  
P. G. Gbetkom ◽  
R. Cooper ◽  
S. Hakdaoui ◽  
M. B. Mansour Badamassi
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Experimental Site ◽  
Soil Features ◽  
Mixed Pixels ◽  
Shortwave Infrared ◽  
Normalization Process

Abstract. This paper addresses the remote sensing challenging field of urban mixed pixels on a medium spatial resolution satellite data. The tentatively named Normalized Difference Built-up and Surroundings Unmixing Index (NDBSUI) is proposed by using Landsat-8 Operational Land Imager (OLI) bands. It uses the Shortwave Infrared 2 (SWIR2) as the main wavelength, the SWIR1 with the red wavelengths, for the built-up extraction. A ratio is computed based on the normalization process and the application is made on six cities with different urban and environmental characteristics. The built-up of the experimental site of Yaoundé is extracted with an overall accuracy of 95.51% and a kappa coefficient of 0.90. The NDBSUI is validated over five other sites, chosen according to Cameroon’s bioclimatic zoning. The results are satisfactory for the cities of Yokadouma and Kumba in the bimodal and monomodal rainfall zones, where overall accuracies are up to 98.9% and 97.5%, with kappa coefficients of 0.88 and 0.94 respectively, although these values are close to those of three other indices. However, in the cities of Foumban, Ngaoundéré and Garoua, representing the western highlands, the high Guinea savannah and the Sudano-sahelian zones where built-up is more confused with soil features, overall accuracies of 97.06%, 95.29% and 74.86%, corresponding to 0.918, 0.89 and 0.42 kappa coefficients were recorded. Difference of accuracy with EBBI, NDBI and UI are up to 31.66%, confirming the NDBSUI efficiency to automate built-up extraction and unmixing from surrounding noises with less biases.

scholarly journals MINERALOGICAL MAPPING IN THE PART OF A GOLD PROSPECT USING EO-1 HYPERION DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 991-993 ◽  
Author(s):  
V. K. Sengar ◽  
A. S. Venkatesh ◽  
P. K. Champaty Ray ◽  
S. L. Chattoraj ◽  
R. U. Sharma
Keyword(s):  
Hyperspectral Data ◽  
Reflectance Spectra ◽  
High Spectral Resolution ◽  
Mineral Potential Mapping ◽  
Spectral Angle Mapper ◽  
Potential Mapping ◽  
Hyperion Data ◽  
End Members ◽  
Resolution Of Imaging ◽  
Hyperion Image

The satellite data obtained from various airborne as well as space-borne Hyperspectral sensors, often termed as imaging spectrometers, have great potential to map the mineral abundant regions. Narrow contiguous bands with high spectral resolution of imaging spectrometers provide continuous reflectance spectra for different Earth surface materials. Detailed analysis of resultant reflectance spectra, derived through processing of hyperspectral data, helps in identification of minerals on the basis of their reflectance characteristics. EO-1 Hyperion sensor contains 196 unique channels out of 242 bands (L1R product) covering 0.4&amp;ndash;2.5&amp;thinsp;μm range has also been proved significant in the field of spaceborne mineral potential mapping. <br><br> Present study involves the processing of EO-1 Hyperion image to extract the mineral end members for a part of a gold prospect region. Mineral map has been generated using spectral angle mapper (SAM) method of image classification while spectral matching has been done using spectral analyst tool in ENVI. Resultant end members found in this study belong to the group of minerals constituting the rocks serving as host for the gold mineralisation in the study area.

scholarly journals GEOTECNOLOGIAS COMO SUPORTE PARA ANÁLISE DA VEGETAÇÃO NATURAL NA SUB-BACIA HIDROGRÁFICA DO RIO GAVIÃO (1988 A 2015)

2020 ◽  
Vol 15 (01) ◽  
pp. 98-113
Author(s):  
Carlos Magno Santos Clemente ◽  
Pablo Santana Santos
Keyword(s):  
Hydrological Cycle ◽  
Natural Vegetation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Palabras Clave ◽  
El Sistema ◽  
History Of ◽  
Main Canal ◽  
Del Rio ◽  
Semi Arid

O histórico de ocupação da sub-bacia do rio Gavião passou por transformações socioeconômicas expressivas nos últimos 30 anos. Desse modo,preocupações com preservação ou recuperação da cobertura vegetal influência, positivamente, na manutenção do ciclo hidrológico da sub-bacia. A presente pesquisa teve como objetivo analisar a modificação da vegetal natural entre os anos de 1988a 2015 na sub-bacia hidrográfico do rio Gavião (semiárido brasileiro). Foram utilizados as técnicas sensoriamento remoto e Processamento Digital de Imagens - PDI para aquisição e processamento dos produtos orbitais (satélites landsat5 TM e landsat 8 OLI). E o Sistema de Informações Geográficas – SIG para armazenamento e análise do banco de dados alfanumérico georreferenciado. Os resultados indicam redução da cobertura vegetal de 751,69 km², entre os anos de 1988 a 2015. Também, manchas de desmatamento em áreas de nascentes, na parte alta da rede de drenagem e no dessegue do canal principal. Assim, a presente pesquisa chama atenção para os efeitos da mudança da vegetação natural para outros usos da terra (solo exposto, plantio, entre outros), a concentração do desmatamento em áreas de fragilidade ambiental. Palavras-chave: Landsat; Desmatamento; Semiárido brasileiro.   GEOTECHNOLOGIES AS SUPPORT FOR ANALYSIS OF NATURAL VEGETATION IN THE HYDROGRAPHIC BASIN OF HAWK RIVER (1988 A 2015) Abstract  The occupation history of the Hawk River sub-basin underwent significant socioeconomic transformations in the last 30 years. Thus, concerns for preservation or recovery of vegetation cover positively influence the maintenance of the sub-basin's hydrological cycle. The present research had as objective to analyze the modification of the natural vegetal between the years of 1988 to 2015 in the hydrographic sub-basin of the river Gavião (semi-arid Brazilian).The techniques of remote sensing and Digital Image Processing (PDI) were used for the acquisition and processing of orbital products (landsat 5 TM and landsat 8 OLI satellites). The Geographic Information System - GIS for storage and analysis of the georeferenced alphanumeric database. The results indicate a reduction of the vegetal cover of 751,69 km ², between the years of 1988 to 2015. In addition, deforestation patches in areas of springs, in the upper part of the drainage network and in the main canal deregulation. Thus, the present research draws attention to the effects of changing natural vegetation to other land uses (exposed soil, planting, among others), the concentration of deforestation in areas of environmental fragility.  Keywords: Landsat; deforestation; Brazilian semi-arid.   GEOTECNOLOGÍA COMO SOPORTE PARA EL ANÁLISIS DE VEGETACIÓN NATURAL DE LA SUBCUENCA DEL RÍO GAVILÁN (1988 A 2015) Resumen La historia de laocupación de lasub-cuencadelrío Gavião fue sometido a importantes cambios socioeconómicos enlos últimos 30 años. De este modo, preocupación por lapreservación o restauración de lacubierta vegetal influencia positiva enelmantenimientodel ciclo hidrológico de lasubcuenca. Esta investigacióntuvo como objetivo analizarlamodificación de lavegetación natural entre losaños 1988-2015 enlasubcuenca hidrográfica delrío Gavião (semiárido brasileño). Como apoyo técnico, lateledetección y la técnica de imagen digital se utiliza Procesamiento - PDI para laadquisición y procesamiento de productosorbitales (satélites Landsat 5 y Landsat TM 8 OLI). Y el Sistema de Información Geográfica - SIG para elalmacenamiento y análisis de la base de datos alfanuméricos georeferenciada. Los resultados indicanlareducción de lacubierta vegetal de 751.69 km², entre losaños 1988-2015. Tambiénlas manchas de deforestaciónenlascabecerasenla parte superior del sistema de drenaje y dessegue el canal principal. Así, estainvestigaciónllamalaatención sobre losefectosdelcambio de lavegetación natural a otros usos de latierra (sueloexpuesto, ,plantación, etc.), laconcentración de ladeforestaciónen áreas ambientalmente frágiles. Palabras clave: Landsat; deforestación; semiárido brasileño.

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