scholarly journals Comparação entre diferentes algoritmos de classificação supervisionada em imagens Landsat-8 no mapeamento temático da Bacia Hidrográfica do rio Caldas, Goiás

2019 ◽  
Vol 12 (3) ◽  
pp. 961
Author(s):  
Leovigildo Aparecido Costa Santos ◽  
Paulo Eliardo Morais de Lima
Keyword(s):  
Land Use ◽  
Maximum Likelihood ◽  
River Basin ◽  
Supervised Classification ◽  
Likelihood Method ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Entire Area ◽  
Area Of Interest

Diferentes métodos são empregados para a classificação digital de imagens, porém, podem apresentar desempenhos diferentes, sendo importante testá-los para verificar suas eficácias no mapeamento de uso e cobertura da terra com intuito de se selecionar o classificador que apresente os melhores resultados e maior veracidade em relação à verdade de campo. O objetivo deste estudo foi avaliar e comparar os desempenhos de quatro algoritmos de classificação supervisionada para o mapeamento do uso e cobertura da terra da bacia hidrográfica do Rio Caldas – GO, utilizando imagens Landsat-8. Para tanto, foram utilizadas as cenas de órbita/ponto 222/71 e 222/72, com datas de passagem em 24/10/2017 e 22/10/2017, mosaicadas para formar uma única imagem de dimensões que abrangesse toda a área de interesse. A composição RGB utilizada foi das bandas 6, 5 e 4 (R=6, G=5, B=4). Para a realização do processamento digital da imagem foi empregado o software ENVI versão 5.0 e à elaboração de mapas temáticos o QGIS 2.18. Os algoritmos testados foram: Paralelepípedo, Distância de Mahalanobis, Distância Mínima e Máxima-verossimilhança. Como parâmetros de comparação foram utilizados os coeficientes de Kappa, acurácias global e matrizes de confusão. Os melhores resultados para a classificação de uso e cobertura foram obtidos pelo método da Máxima-verossimilhança (MaxVer), os piores pelo método do Paralelepípedo, os outros classificadores apresentaram resultados intermediários entre o melhor e o pior. Com os resultados obtidos pela classificação por MaxVer, constatou-se que atualmente a maior parte do solo da bacia é ocupada pelas classes Pastagem (63,14%) e Vegetação nativa (22,07%). Comparison between different supervised classification algorithms in Landsat-8 images in the thematic mapping of the caldas river basin, GoiásA B S T R A C TDifferent methods are used for a digital classification of images, however, they can present different performances, being important to test them to verify their efficiencies in the mapping of land use and coverage in order to select the classifier that presents the best results and greater truthfulness In relation to the truth of the field. The objective of this study was to evaluate and compare the performance of four supervised classification algorithms for the mapping of the land use and land cover of the Caldas river basin - GO, using Landsat-8 images. To do so, they were like the orbit / dot scenes 222/71 and 222/72, with passing date on 10/24/2017 and 10/22/2017, mosaicked to form a single image of dimensions covering an entire area of interest . An RGB composition used for bands 6, 5 and 4 (R = 6, G = 5, B = 4). For the realization of digital image processing and the use of ENVI version 5.0 software and the development of thematic maps, QGIS 2.18. The algorithms tested were: Parallelepiped, Mahalanobis Distance, Minimum Distance and Maximum Likelihood. As the comparison parameter is used by Kappa coefficients, global accuracy and matrices of confusion. The best results for a classification of use and coverage are obtained by the Maximum-likelihood method (MaxVer), the most common methods, the other classifiers presented the intermediates between the best and the worst. With the results obtained by classification by MaxVer, it was verified that at the moment it is part of the soil of the basin is occupied by classes Pasture (63.14%) and native vegetation (22.07%).Keywords: Use and coverage; remote sensing; geoprocessing; Landsat.

scholarly journals Genus-Physiognomy-Ecosystem (GPE) System for Satellite-Based Classification of Plant Communities

Ecologies ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 203-213
Author(s):  
Ram C. Sharma
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Plant Communities ◽  
Dominant Species ◽  
Geographical Area ◽  
Vegetation Mapping ◽  
Species Level ◽  
Landsat 8 ◽  
Area Of Interest

Vegetation mapping and monitoring is important as the composition and distribution of vegetation has been greatly influenced by land use change and the interaction of land use change and climate change. The purpose of vegetation mapping is to discover the extent and distribution of plant communities within a geographical area of interest. The paper introduces the Genus-Physiognomy-Ecosystem (GPE) system for the organization of plant communities from the perspective of satellite remote sensing. It was conceived for broadscale operational vegetation mapping by organizing plant communities according to shared genus and physiognomy/ecosystem inferences, and it offers an intermediate level between the physiognomy/ecosystem and dominant species for the organization of plant communities. A machine learning and cross-validation approach was employed by utilizing multi-temporal Landsat 8 satellite images on a regional scale for the classification of plant communities at three hierarchical levels: (i) physiognomy, (ii) GPE, and (iii) dominant species. The classification at the dominant species level showed many misclassifications and undermined its application for broadscale operational mapping, whereas the GPE system was able to lessen the complexities associated with the dominant species level classification while still being capable of distinguishing a wider variety of plant communities. The GPE system therefore provides an easy-to-understand approach for the operational mapping of plant communities, particularly on a broad scale.

scholarly journals Selection of an algorithm for automatic classification of satellite images for the study of agricultural crops on the territory of Vietnam

2021 ◽  
Vol 937 (3) ◽  
pp. 032082
Author(s):  
B N Olzoev ◽  
H Z Huang ◽  
L A Plastinin ◽  
V E Gagin ◽  
O V Danchenko
Keyword(s):  
Maximum Likelihood ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Classification Algorithms ◽  
Agricultural Activity ◽  
Landsat 8 ◽  
Agricultural Crop ◽  
Agricultural Crops ◽  
Selection Of

Abstract The paper is devoted to the choice of an algorithm for automatic controlled classification of multi-zone satellite images of Landsat 8 OLI for the purposes of agricultural crop research based on the analysis of various mathematical classification algorithms and comparison of the practical results of these algorithms when using the ENVI 5.4 software package. In the period from June to August 2020, a field survey was conducted by coordinating and ground-based object recognition for the purpose of compiling decryption standards based on images. The paper analyzes four frequently used popular algorithms for automatic controlled classification – maximum likelihood, minimum distance, Mahalanobis distance, parallelepiped. As a result, it is concluded that when classifying objects with very close brightness values, the maximum likelihood algorithm gives optimal and objective results. This conclusion was confirmed by the cameral method by evaluating the reliability of the classification results. The result of the study can be used for mapping agricultural crops and solving other problems of agricultural activity in Vietnam. The methodology presented in the paper can be applied when choosing controlled classification algorithms for other groups of plant complexes and objects based on remote sensing data from space.

scholarly journals ANÁLISE COMPARATIVA DE MÉTODOS DE CLASSIFICAÇÃO SUPERVISIONADA APLICADA AO MAPEAMENTO DA COBERTURA DO SOLO NO MUNICÍPIO DE MEDICILÂNDIA, PARÁ

2018 ◽  
Vol 4 (13) ◽  
pp. 26
Author(s):  
Jones Remo Barbosa Vale ◽  
Jamer Andrade da Costa ◽  
Jefferson Ferreira dos Santos ◽  
Elton Luis Silva da Silva ◽  
Artur Trindade Favacho
Keyword(s):  
Remote Sensing ◽  
Maximum Likelihood ◽  
Minimum Distance ◽  
Supervised Classification ◽  
Satellite Images ◽  
Soil Cover ◽  
Landsat 8 ◽  
Kappa Index ◽  
Global Accuracy

COMPARATIVE ANALYSIS THE METHODS OF SUPERVISED CLASSIFICATION APPLIED TO THE MAPPING OF SOIL COVER IN THE MUNICIPALITY OF MEDICILÂNDIA, PARÁANÁLISIS COMPARATIVO DE MÉTODOS DE CLASIFICACIÓN SUPERVISADA APLICADA AL MAPEO DE LA COBERTURA DEL SUELO EN EL MUNICIPIO DE MEDICILÂNDIA, PARÁAs imagens de satélite são produtos gerados por sensoriamento remoto e, estão associadas aos fenômenos e eventos que ocorrem na superfície a partir do registro e da análise das interações entre a radiação eletromagnética e os alvos. O objetivo do trabalho é comparar métodos de classificação supervisionada de imagens de satélite para o mapeamento da cobertura do solo. A área de estudo compreende o município de Medicilândia, localizado no sudoeste paraense. Para a realização do trabalho foram utilizados imagens do satélite Landsat 8, sensor OLI-TIRS, cenas 226/062 e 227/062. Foram realizados os testes de classificação, utilizando três classificadores: Distância Mínima, Distância Mahalanobis e Máxima Verossimilhança. Na etapa de classificação foram identificadas as seguintes classes: água, nuvem, sombra de nuvem, solo exposto, vegetação primária e vegetação secundária. Para fins de avaliação da fidedignidade da classificação de cada método foram calculados, o Índice Kappa e a Exatidão Global. A classificação pelo método Máxima Verossimilhança obteve maior exatidão apresentando Índice Kappa de 0,920 e Exatidão Global 96% quando comparada à classificação pelos métodos Distância Mínima e Distância Mahalanobis, que apresentaram Índice Kappa de 0,842 e 0,845 e Exatidão Global 92% respectivamente. As técnicas de classificação supervisionada são ferramentas essenciais no processo de mapeamento da cobertura do solo de grandes áreas, visto que dispondo-se de recursos limitados, imagens de baixo custo e de sistemas livres para processamento e integração das informações, é possível obter parâmetros com altos níveis de precisão, sendo fundamentais para subsidiar o planejamento territorial e ambiental.Palavras-chave: Sensoriamento Remoto; Classificação de Imagens de Satélite; Cobertura do Solo.ABSTRACTThe satellite images are products generated by remote sensing and are associated with phenomena and events that occur on the surface from the recording and analysis of interactions between electromagnetic radiation and targets. The objective of this work is to compare methods of supervised classification of satellite images for the mapping of the soil cover. The study area comprises the municipality of Medicilândia, located in southwest of Para. In order to perform the work, were used images from the Landsat 8 satellite, OLI-TIRS sensor, scenes 226/062 and 227/062. The classification tests were performed using three classifiers: Minimum Distance, Mahalanobis Distance and Maximum Likelihood. In the classification processe were identified the following classes: water, cloud, cloud shadow, exposed soil, primary vegetation and secondary vegetation. For the purposes of evaluating the reliability of the classification of each method were calculated, Kappa Index and Global Accuracy. The classification by the Maximum Likelihood method obtained a greater accuracy presenting Kappa Index of 0,920 and Global Accuracy 96% when compared to the classification by the Minimum Distance and Mahalanobis Distance, which presented Kappa Index of 0,842 and 0,845 and Global Accuracy 92% respectively. The supervised classification techniques are essential tools in the mapping process of large-area soil cover, since with limited resources, low-cost images and free systems for processing and integrating information, it is possible to obtain parameters with high levels of precision, being fundamental to subsidize territorial and environmental planning.Keywords: Remote Sensing; Classification of Satellite Images; Soil Cover.RESUMENLas imágenes de satélite son productos generados por la detección remota y están asociados a los fenómenos y eventos que ocurren en la superficie a partir del registro y del análisis de las interacciones entre la radiación electromagnética y los blancos. El objetivo del trabajo es comparar métodos de clasificación supervisada de imágenes de satélite para el mapeo de la cobertura del suelo. El área de estudio comprende el municipio de Medicilândia, ubicado en el suroeste paraense. Para la realización del trabajo se utilizaron imágenes del satélite Landsat 8, sensor OLI-TIRS, escenas 226/062 y 227/062. Se utilizaron tres clasificadores: Distancia Mínima, Distancia Mahalanobis y Máxima Verosimilitud. En la etapa de clasificación se identificaron las siguientes clases: agua, nube, sombra de nube, suelo expuesto, vegetación primaria y vegetación secundaria. Para evaluar la confianza de la clasificación de cada método se ha calculado, el Índice Kappa y la Exactitud Global. La clasificación por Máxima Verosimilitud obtuvo mayor exactitud presentando Índice Kappa de 0,920 y Exactitud Global 96% cuando comparada a la clasificación por Distancia Mínima y Distancia Mahalanobis, que presentaron Índice Kappa de 0,842 y 0,845 y Exactitud Global 92% respectivamente. Las técnicas de clasificación supervisada son herramientas esenciales en el proceso de mapeo de la cobertura del suelo de grandes áreas, ya que disponiendo de recursos limitados, imágenes de bajo costo y de sistemas libres para procesamiento e integración de la información, es posible obtener parámetros con altos niveles de precisión, siendo fundamentales para subsidiar la planificación territorial y ambiental.Palabras clave: Sensoramiento Remoto; Clasificación de Imágenes de Satélite; Cobertura del Suelo.

scholarly journals Processing and Use of Satellite Images in Order to Extract Useful Information in Precision Agriculture

2016 ◽  
Vol 73 (2) ◽  
pp. 238 ◽  
Author(s):  
Mihai Valentin Herbei ◽  
Cosmin Alin Popescu ◽  
Radu Bertici ◽  
Adrian Smuleac ◽  
George Popescu
Keyword(s):  
Maximum Likelihood ◽  
Precision Agriculture ◽  
Urban Areas ◽  
Supervised Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Likelihood Method ◽  
Large Area ◽  
Area Of Interest ◽  
Maximum Similarity

Image analysis methods were developed and diversified greatly in recent years due to increasing speed and accuracy in providing information regarding land cover and vegetation in urban areas. The aim of this paper is to process satellite images for monitoring agricultural areas. Satellite images used in this study are medium and high resolution images taken from QuickBird and SPOT systems. Based on these images, a supervised classification was performed of a very large area, having as result the land use classes. Supervised classification can be defined as the ability to group the pixels that compose the satellite image, digitally, in accordance with their real significance. Gaussian algorithm of maximum similarity (Maximum likelihood) was used, referred to in the specialty literature as maximum likelihood method or probabilistic classification, and based on the use of probability theory (function Gaussian) to compare the spectral values of each pixel in hand with statistical " fingerprint "of each area of interest. Practically, conditional probabilities were calculated of belonging to one class or another. The points in the middle of the group have a higher probability of belonging to the certain class, probability intervals (concentric isolines or contours of equal probability) being delimited graphically by izocontours expressing spectral variations within each set of training.

scholarly journals INFLUÊNCIA DO USO E OCUPAÇÃO DO SOLO NA DISPONIBILIDADE HÍDRICA DA BACIA HIDROGRÁFICA DO RIO URUÇUÍ-PRETO, PIAUÍ

Nativa ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 567
Author(s):  
Kaíse Barbosa Souza ◽  
João Batista Lopes Silva ◽  
Rafael Felippe Ratke ◽  
Gerson Santos Lisboa ◽  
Karla Nayara Santos Almeida
Keyword(s):  
Land Use ◽  
River Basin ◽  
Supervised Classification ◽  
Thematic Mapper ◽  
Large Area ◽  
Simple Correlation ◽  
Burned Areas ◽  
Crop Field ◽  
Annual Duration

Objetivou-se verificar a influência das mudanças no uso e ocupação do solo na disponibilidade hídrica da bacia hidrográfica do rio Uruçuí-Preto, Piauí, no período de 1984 a 2007. Para a avaliação do uso do solo foram utilizadas imagens do Satélite Landsat 5, sensor TM (Thematic Mapper) e realizou-se a classificação automática supervisionada auxiliada pelo algoritmo de Máxima Verossimilhança. Para a análise do comportamento hidrológico foram utilizados dados da vazão média, máxima e mínima anual; vazão mínima com sete dias de duração anual (Q7); e as vazões associadas às permanências de 90% (Q90) e 95% (Q95). Para a associação entre as vazões e o uso do solo, fez-se a correlação simples entre as variáveis, testando seu nível de significância a 5% (p < 0,05) de probabilidade. Os resultados demonstraram que ao comparar os anos de 1984 e 2007, as classes Cerrado e Mata Ciliar reduziram 20,9% e 2,4% respectivamente, a classe Solo Exposto/Talhão Agrícola aumentou 13,48% e a classe Queimada aumentou 9,83%. Não ocorreram correlações significativas entre as variáveis classes de mudanças no uso e ocupação do solo e as vazões, devido a extensa área da bacia que amenizou consequentemente os efeitos hidrológicos.Palavras-chave: desmatamento; sensoriamento remoto; vazões. INFLUENCE OF THE LAND USE AND OCCUPATION IN HIDROLOGIC AVAILABILITY OF RIVER BASIN URUÇUÍ-PRETO, PIAUÍ ABSTRACT: The aim’s was to verify the influence of changes in land use and occupation in water availability in the river basin Uruçuí-Preto, Brazil, in the period 1984 to 2007. For the assessment of land use were used images from satellite Landsat 5 TM sensor (Thematic Mapper) and the classification of imagens was made by automatic supervised classification with the maximum likelihood algorithm. For the analysis of the hydrological behavior were used average flow data, maximum and minimum annual, minimum flow with seven days of annual duration (Q7) and flow rates associated with stays of 90% (Q90) and 95% (Q95) of the year. For the association between flow and land use was made the simple correlation between the variables, testing its 5% significance (p < 0.05). The results showed that when comparing the years 1984 and 2007, the Cerrado and Mata Riparian classes decreased 20.9% and 2.4% respectively, Solo Exposed / Crop Field class increased 13.48% and Burned Areas class increased by 9.83%. There were no significant correlations between changes of class variables in the use and occupation of land and the flows, due to the large area of the basin that has consequently reduced the hydrological effects.Keywords: deforestation; remote sensing; flow.

scholarly journals Transboundary analysis of the land cover in Razdolnaya river basin

2021 ◽  
pp. 70-77
Author(s):  
Т.К. МУЗЫЧЕНКО ◽  
М.Н. МАСЛОВА
Keyword(s):  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Rice Fields ◽  
Landsat 8 ◽  
Agricultural Lands ◽  
Transboundary River ◽  
Russian Part ◽  
Land Use Structure ◽  
Sentinel 2

В статье рассмотрено пространственное распределение типов земель в пределах трансграничного бассейна р. Раздольная. На основе дешифрирования космических снимков Sentinel-2 и Landsat 8 составлена карта пространственного распределения типов земель по состоянию на 2019 г. Исходя из геоэкологической классификации ландшафтов В.А. Николаева в данной работе было выделено 12 типов земель: используемые и неиспользуемые сельскохозяйственные земли, используемые и неиспользуемые рисовые поля, карьеры, леса, лесопосадки, рубки, луга, застроенные земли, водные объекты, а также кустарники и редколесья. Представлены абсолютные и относительные площади для каждого типа земель по трансграничному бассейну в целом, а также отдельно для его российской и китайской частей. По результатам дешифрирования данных дистанционного зондирования установлено, что российская и китайская части бассейна р. Раздольная имеют существенные трансграничные различия в структуре земель. На российской части бассейна лесами покрыто чуть более половины площади, но при этом значительные площади занимают сельскохозяйственные земли и луга. В некоторых местах луга и сельскохозяйственные земли преобладают в большей степени, чем леса. На китайской части лесные территории доминируют над другими типами земель. Сельскохозяйственные земли и луга образуют узкие и длинные полосы и имеют более мозаичное распространение, чем на российской части. Здесь заметно меньше площади застроенных земель, а площади рубок и лесопосадок больше, чем на российской части. Площади карьеров примерно равны в обеих частях бассейна. The transboundary Razdolnaya river basin is nearly evenly split up between Primorsky Krai of Russian Federation and Heilongjiang and Jilin provinces of People’s Republic of China. The Chinese and the Russian parts of the transboundary river have developed independently of each other. Therefore, the two have a different land cover and land use structure. The analysis of land cover and land use structure is of utmost importance for the understanding the modern state of land development and the possibilities of its future development. Using the remote sensing data, such as Sentinel-2 and Landsat 8 satellite imagery, the land cover and land use map of the Razdolnaya transboundary river basin for 2019 has been composed by means of the ArcMap 10.5 software package. According to V.A. Nikolaev’s geoecological classification of landscapes, we have identified 12 land types: forests, meadows, shrubs and woodlands, agricultural lands, unused agricultural lands, rice fields, unused rice fields, built-up areas, reforestation lands, logging, quarries, and bodies of water. We have provided area coverage for each type of land of the whole transboundary basin, and for the Russian and Chinese parts. According to the results of computer-aided visual deciphering and automatic deciphering, forests are the most common land use type in the basin. In the Chinese part of the basin, forests dominate over the other types of land. Agricultural lands and meadows have assumed narrow and linear shapes. Built-up areas have less coverage here than in the Russian part of the basin. However, the coverage of logging and reforestation lands is considerably larger than in the Russian part of the basin. In the Russian part of the basin, forests co-dominate with the agricultural lands and meadows. In some areas of this part of the basin forests disappear almost completely. The Russian part of the basin also has the larger coverage of shrubs and woodlands, unused agricultural lands, rice fields and unused rice fields. The coverage of quarries is roughly equal in both parts of the basin.

scholarly journals Agricultural land use and cover change in the Cerrado/Amazon ecotone: A case study of the upper Teles Pires River basin

2018 ◽  
Vol 48 (2) ◽  
pp. 168-177 ◽  
Author(s):  
Ana Paula Sousa Rodrigues ZAIATZ ◽  
Cornélio Alberto ZOLIN ◽  
Laurimar Goncalves VENDRUSCULO ◽  
Tarcio Rocha LOPES ◽  
Janaina PAULINO
Keyword(s):  
Land Use ◽  
River Basin ◽  
Large Scale ◽  
Agricultural Land ◽  
Temporal Dynamics ◽  
Direct Conversion ◽  
Landsat 8 ◽  
Error Matrix ◽  
Mato Grosso ◽  
Land Use And Cover

ABSTRACT The upper Teles Pires River basin is a key hydrological resource for the state of Mato Grosso, but has suffered rapid land use and cover change. The basin includes areas of Cerrado biome, as well as transitional areas between the Amazon and Cerrado vegetation types, with intensive large-scale agriculture widely-spread throughout the region. The objective of this study was to explore the spatial and temporal dynamics of land use and cover change from 1986 to 2014 in the upper Teles Pires basin using remote sensing and GIS techniques. TM (Thematic Mapper) and TIRS (Thermal Infrared Sensor) sensor images aboard the Landsat 5 and Landsat 8, respectively, were employed for supervised classification using the “Classification Workflow” in ENVI 5.0. To evaluate classification accuracy, an error matrix was generated, and the Kappa, overall accuracy, errors of omission and commission, user accuracy and producer accuracy indexes calculated. The classes showing greatest variation across the study period were “Agriculture” and “Rainforest”. Results indicated that deforested areas are often replaced by pasture and then by agriculture, while direct conversion of forest to agriculture occured less frequently. The indices with satisfactory accuracy levels included the Kappa and Global indices, which showed accuracy levels above 80% for all study years. In addition, the producer and user accuracy indices ranged from 59-100% and 68-100%, while the errors of omission and commission ranged from 0-32% and 0-40.6%, respectively.

scholarly journals Influence of the use and the land cover of the catchment in the water quality of the semiarid tropical reservoirs

2018 ◽  
Vol 7 (7) ◽  
pp. 389
Author(s):  
Herika Cavalcante ◽  
Patrícia Silva Cruz ◽  
Leandro Gomes Viana ◽  
Daniely De Lucena Silva ◽  
José Etham De Lucena Barbosa
Keyword(s):  
Water Quality ◽  
Supervised Classification ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Dissolved Phosphorus ◽  
Sparse Vegetation ◽  
Tropical Reservoirs

The aim of this study was to evaluate some parameters of water quality of semiarid reservoirs under different uses and occupation of the catchment’s soil. For this, the reservoirs Acauã and Boqueirão, belonging to the Paraíba do Norte river watershed and Middle and Upper course sub catchments, respectively, were studied. For this, water samples were collected in August, September and October 2016. From these samples, total and dissolved phosphorus, nitrate, nitrite, ammonia, chlorophyll, dissolved and suspended solids were analyzed. In addition, images of the Landsat 8 satellite were acquired for the calculation of the Normalized Difference Vegetation Index (NDVI), and for the supervised classification of the use and occupation of the sub catchments. Thus, it was observed that, in general, the Acauã reservoir presented values of phosphorus and nitrogen and solids larger than the Boqueirão reservoir, due to the greater urban area, even though it had a smaller total area of the basin. Both reservoirs presented low vegetation rates and high areas of sparse vegetation and exposed soil, increasing the propensity to soil erosion and the transport of nutrients from the basin to the reservoirs, making water quality worse or impossible.

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