Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan

Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the management, remediation, monitoring and utilization of saline soils. This study investigated the potential of Landsat 8 OLI satellite data and vegetation, soil salinity and moisture indices in estimating surface salinity of 1014.6 ha agricultural land located in Dushak, Turkmenistan. Linear regression model was developed between land measurements and remotely sensed indicators. A systematic regular grid-sampling method was used to collect 50 soil samples from 0–20 cm depth. Sixteen indices were extracted from Landsat-8 OLI satellite images. Simple and multivariate regression models were developed between the measured electrical conductivity values and the remotely sensed indicators. The highest correlation between remote sensing indicators and soil EC values in determining soil salinity was calculated in SAVI index (r = 0.54). The reliability indicated by R2 value (0.29) of regression model developed with the SAVI index was low. Therefore, new model was developed by selecting the indicators that can be included in the multiple regression model from the remote sensing indicators. A significant (r = 0.74) correlation was obtained between the multivariate regression model and soil EC values, and salinity was successfully mapped at a moderate level (R2: 0.55). The classification of the salinity map showed that 21.71% of the field was non-saline, 29.78% slightly saline, 31.40% moderately saline, 15.25% strongly saline and 1.44% very strongly. The results revealed that multivariate regression models with the help of Landsat 8 OLI satellite images and indices obtained from the images can be used for modeling and mapping soil salinity of small-scale lands.

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Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽

10.3390/proceedings2231430 ◽

2018 ◽

Vol 2 (23) ◽

pp. 1430

Author(s):

V. M. Fernández-Pacheco ◽

C. A. López-Sánchez ◽

E. Álvarez-Álvarez ◽

M. J. Suárez López ◽

L. García-Expósito ◽

...

Keyword(s):

Remote Sensing ◽

Air Pollutants ◽

Satellite Images ◽

Satellite Image ◽

Remote Sensing Image ◽

Regional Studies ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Landsat 5 Tm ◽

Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

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Developing Logistic Regression Models to Identify Salt-Affected Soils Using Optical Remote Sensing

Interdisciplinary Approaches to Information Systems and Software Engineering - Advances in Systems Analysis, Software Engineering, and High Performance Computing ◽

10.4018/978-1-5225-7784-3.ch010 ◽

2019 ◽

pp. 233-256

Author(s):

Nirmal Kumar ◽

S. K. Singh ◽

G. P. Obi Reddy ◽

R. K. Naitam

Keyword(s):

Remote Sensing ◽

Logistic Regression ◽

Spatial Distribution ◽

Soil Salinity ◽

Regression Models ◽

Landsat 8 ◽

Gangetic Plain ◽

Logistic Regression Models ◽

Salt Affected Soils ◽

Indo Gangetic Plain

A major part of Indo-Gangetic plain is affected with soil salinity/alkalinity. Information on spatial distribution of soil salinity is important for planning management practices for its restoration. Remote sensing has proven to be a powerful tool in quantifying and monitoring the development of soil salinity. The chapter aims to develop logistic regression models, using Landsat 8 data, to identify salt affected soils in Indo-Gangetic plain. Logistic regression models based on Landsat 8 bands and several salinity indices were developed, individually and in combination. The bands capable of differentiating salt affected soils from other features were identified as green, red, and SWIR1. The logistic regression model developed in the study area was found to be 81% accurate in identifying salt-affected soils. A total area of 34558.49 ha accounting to ~10% of the total geographic area of the district was found affected with salinity/alkalinity. The spatial distribution of salt-affected soils in the district showed an association of shallow ground water depth with salinity.

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Space-temporal evaluation of biophysical parameters in the High Ipanema watershed by remote sensing

Journal of Hyperspectral Remote Sensing ◽

10.29150/jhrs.v7.6.p357-366 ◽

2018 ◽

Vol 7 (6) ◽

pp. 357

Author(s):

Jose Diorgenes Alves Oliveira ◽

Biancca Correia De Medeiros ◽

Jhon Lennon Bezerra Da Silva ◽

Geber Barbosa De Albuquerque Moura ◽

Frederico Abraão Costa Lins ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Images ◽

Environmental Changes ◽

The United States ◽

Semiarid Region ◽

United States Geological Survey ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Biophysical Parameters ◽

Environmental Implications

The High Ipanema watershed is located in a semiarid region and because of this, becomes more vulnerable and susceptible to the effects of environmental changes and the degradation process, it has serious economic and socio-environmental implications. In recent years with the advancement of remote sensing based on satellite imagery or other platforms, it has become possible to monitor different and large areas of the various biomes in the world. The objective of this study was to identify changes in the vegetation cover conditions in the Alto Ipanema watershed, using spectral analyzes of Landsat-8 OLI / TIRS satellite images, using remote sensing techniques. Landsat-8 OLI / TIRS satellite images were obtained from the United States Geological Survey – USGS, on 10/12/2013, 14/01/2015 and 12/08/2016, where they were processed from ERDAS IMAGINE® Software, version 9.1. The thematic maps of biophysical parameters were processed by ArcGis® 10.2.2 Software. With the biophysical parameters analyzed, it was found that the northwest portion of the watershed presents a considerable area of exposed soils with indication of a high degree of susceptibility to degradation and that the biophysical parameters evaluated by the SEBAL algorithm are efficient in understanding the dynamics of spatial and temporal areas of semiarid environments.

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Using multispectral images to determine the salinity of soils in Ingulets irrigation system in the justification of management measures for chemical reclamation

Agroecological journal ◽

10.33730/2077-4893.3.2021.240319 ◽

2021 ◽

Author(s):

V. Kovalchuk ◽

O. Voitovych

Keyword(s):

Information Technology ◽

Soil Salinity ◽

Satellite Images ◽

Irrigation System ◽

Soil Samples ◽

Satellite Monitoring ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Management Measures ◽

Salinity Index

The publication presents the use of data from multispectral space images to substantiate management measures for chemical reclamation of saline soils of the Ukraine irrigation system. The study is to develop a method of using satellite data to remotely determine the salinity of soils to support the management measures on the chemical soil improvement by example some fields of Ingulets irrigation system. Landsat 8 OLI images were used to obtain NDVI, soil salinity assessment indices, namely VSSI, Salinity index and normalized soil salinity index (NDSI). The verification data on the spatial distribution of salinity are taken from the report on soil-salt survey of the Ingulets irrigated massif. Work with satellite images and calculation of indices was performed using ERDAS IMAGINE 2015. Quantitative analysis of pixels, their visualization using the color spectrum was carried out in ArcMap 10.8. Validation for the conditions of Ukraine on the southern chernozems of the salt indices (VSSI, NDSI, SI) showed the best suitability of NDSI. Using the calculated ranges of index that characterize the gradation of salinity, their area percentage has been determined for each experimental field of the pilot object. The salinity areas according to each gradation were obtained on the basis of NDSI. Quantitative values of the management measure, the dose of chemical ameliorants, differentially for each of the fields are obtained using calculation methods using the author’s information technology. The total amount of phosphogypsum for chemical reclamation is recommended. The results of the study show that Landsat 8 OLI images show a high potential for spatio-temporal monitoring of salinity of the upper soil layers. This study will be useful for planning agricultural activities by mapping the salinity of the soil with the calculation of doses of chemical ameliorants to reduce economic losses in climate change. The proposed norms of gypsum application can serve as a guide for the agricultural producer when making a decision on chemical reclamation of agricultural lands. Based on a combination of satellite monitoring data and calculations of chemical reclamation doses according to the author’s online calculator, the farmer will be able to assess the feasibility and economic efficiency of chemical land reclamation measures. An additional practical value of the method of remote determination of boundaries and area of salinity is the ability to plan the selection of soil samples from certain points of the field, optimizing the number of soil samples for their chemical analysis in the laboratory. Our proposed method, which estimates soil salinity using satellite images and suggests doses of chemical ameliorants using information technology, can be potentially useful as a rapid approach to detecting soil salinity in other regions of Ukraine and the world at low cost and high accuracy

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TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

Revista da Casa da Geografia de Sobral (RCGS) ◽

10.35701/rcgs.v21n2.548 ◽

2019 ◽

Vol 21 (2) ◽

pp. 1310-1320

Author(s):

Cícera Celiane Januário da Silva ◽

Vinicius Ferreira Luna ◽

Joyce Ferreira Gomes ◽

Juliana Maria Oliveira Silva

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Temperature Measurement ◽

Rainy Season ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Low Density ◽

Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia. ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

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

Remote Sensing ◽

10.3390/rs12162587 ◽

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.

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Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

Journal of Geography and Geology ◽

10.5539/jgg.v10n1p1 ◽

2017 ◽

Vol 10 (1) ◽

pp. 1 ◽

Cited By ~ 4

Author(s):

Clement Kwang ◽

Edward Matthew Osei Jnr ◽

Adwoa Sarpong Amoah

Keyword(s):

Remote Sensing ◽

Satellite Images ◽

Remote Sensing Data ◽

Unsupervised Classification ◽

Water Bodies ◽

Water Extraction ◽

Landsat 8 ◽

Sensing Data ◽

Optimal Classifier ◽

Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

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FOREST DISTURBANCE ANALYSIS WITH LANDSAT-8 OLI DATA RELATED TO A PARAMETRIC WIND FIELD: A CASE STUDY FOR TYPHOON RAMMASUN (201409)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1629-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1629-1633

Author(s):

C. Tan ◽

W. Fang

Keyword(s):

Remote Sensing ◽

Wind Field ◽

Forest Disturbance ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Dependence Relation ◽

Wind Field Model ◽

Wind Speeds ◽

Sensing Technology ◽

And Function

Forest disturbance induced by tropical cyclone often has significant and profound effects on the structure and function of forest ecosystem. Detection and analysis of post-disaster forest disturbance based on remote sensing technology has been widely applied. At present, it is necessary to conduct further quantitative analysis of the magnitude of forest disturbance with the intensity of typhoon. In this study, taking the case of super typhoon Rammasun (201409), we analysed the sensitivity of four common used remote sensing indices and explored the relationship between remote sensing index and corresponding wind speeds based on pre-and post- Landsat-8 OLI (Operational Land Imager) images and a parameterized wind field model. The results proved that NBR is the most sensitive index for the detection of forest disturbance induced by Typhoon Rammasun and the variation of NBR has a significant linear dependence relation with the simulated 3-second gust wind speed.

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Coupling threshold theory and satellite-derived channel width to estimate the formative discharge of Himalayan foreland rivers

Earth Surface Dynamics ◽

10.5194/esurf-9-47-2021 ◽

2021 ◽

Vol 9 (1) ◽

pp. 47-70

Author(s):

Kumar Gaurav ◽

François Métivier ◽

Rajiv Sinha ◽

Amit Kumar ◽

Sampat Kumar Tandon ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Images ◽

Force Balance ◽

Channel Width ◽

Landsat 8 ◽

Remote Sensing Images ◽

Physical Force ◽

Regulated Rivers ◽

Threshold Theory ◽

Discharge Regime

Abstract. We propose an innovative methodology to estimate the formative discharge of alluvial rivers from remote sensing images. This procedure involves automatic extraction of the width of a channel from Landsat Thematic Mapper, Landsat 8, and Sentinel-1 satellite images. We translate the channel width extracted from satellite images to discharge using a width–discharge regime curve established previously by us for the Himalayan rivers. This regime curve is based on the threshold theory, a simple physical force balance that explains the first-order geometry of alluvial channels. Using this procedure, we estimate the formative discharge of six major rivers of the Himalayan foreland: the Brahmaputra, Chenab, Ganga, Indus, Kosi, and Teesta rivers. Except highly regulated rivers (Indus and Chenab), our estimates of the discharge from satellite images can be compared with the mean annual discharge obtained from historical records of gauging stations. We have shown that this procedure applies both to braided and single-thread rivers over a large territory. Furthermore, our methodology to estimate discharge from remote sensing images does not rely on continuous ground calibration.

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MANEJO, QUALIDADE E DINÂMICA DA DEGRADAÇÃO DE PASTAGENS NA MATA ATLÂNTICA DE MINAS GERAIS - BRASIL

Nativa ◽

10.31413/nativa.v6i4.5542 ◽

2018 ◽

Vol 6 (4) ◽

pp. 370 ◽

Cited By ~ 1

Author(s):

Luís Flávio Pereira ◽

Cecilia Fátima Carlos Ferreira ◽

Ricardo Morato Fiúza Guimarães

Keyword(s):

Remote Sensing ◽

Atlantic Forest ◽

Quality Index ◽

Minas Gerais ◽

Google Earth ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Mata Atlântica ◽

Mata Atlantica ◽

Forest Biome

Pastagens sob práticas de manejo ineficientes tornam-se degradadas, provocando sérios problemas socioambientais e econômicos. Assim, entender a dinâmica dos sistemas pastoris e suas interações com o meio físico torna-se essencial na busca de alternativas sustentáveis para a agropecuária. Estudou-se manejo, dinâmica anual e interações socioambientais em pastagens de uma bacia hidrográfica no bioma Mata Atlântica em Minas Gerais, Brasil, durante o ano hidrológico 2016/2017. Utilizou-se dados de campo, relatos de agricultores e sensoriamento remoto via imagens LANDSAT 8 OLI e Google Earth Pro®. Foi proposto um índice de qualidade para pastagens da região. As pastagens apresentaram, em média, qualidade moderada. Níveis de degradação foram altos, oscilando de forma quadrática (níveis 2, 4, 5 e IDP) e potencial (nível 1) com a precipitação (p < 0,01), o que sugere que a irrigação possa ser prática eficiente no controle da degradação. Durante o ano, pelo menos 51,27% das pastagens apresentaram algum sinal de degradação, atingindo-se a marca de 91,32%, no período seco. Os resultados sugerem pior qualidade e maiores níveis de degradação de pastagens em terras elevadas e declivosas. Devido às condições socioambientais locais, indica-se o uso de sistemas silvipastoris agroecológicos no manejo das pastagens.Palavras-chave: uso da terra, sensoriamento remoto, relação solo paisagem, Zona da Mata, índice de qualidade. MANAGEMENT, QUALITY AND DEGRADATION DYNAMICS OF PASTURES IN ATLANTIC FOREST BIOME, MINAS GERAIS – BRASIL ABSTRACT:Pastures under inefficient management practices get degraded, leading to serious socioeconomic and environmental issues. That being said, understanding the dynamics of such systems and their interaction with the environment is essential when it comes to looking towards sustainable alternatives for livestock activities. The management, annual dynamics and socio-environmental interactions in pastures in an hydrographic basin located in Atlantic Forest biome, Minas Gerais, Brasil, were studied during the hydrological year of 2016/2017. Field data and farmers reports were utilized, such as remote sensing via images from LANDSAT 8 OLI and Google Earth Pro®. A quality index was proposed for the pastures, which usually presented medium quality. Degradation levels were high, oscillating in a quadratic basis (levels 2, 4, 5 and IDP) and potential (level 1) with precipitation (p < 0,01), which suggests that irrigation might be an efficient practice when it comes to degradation control. During the year, at least 51,27% of pastures have presented signs of degradation, achieving 91,32% in dry periods. The results suggest less quality and bigger degradation levels in pastures located in high and steep areas. Considering the local environmental conditions, agroecological silvopasture systems are recommended regarding the pastures management.Keywords: land use, remote sensing, soil/landscape relationships, Zona da Mata, quality index.

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