scholarly journals Uso e ocupação do solo do município de novo progresso no Estado do Pará-Brasil

2021 ◽  
Vol 10 (1) ◽  
pp. e51210112060
Author(s):  
Raimara Reis do Rosário ◽  
Mateus Trindade Barbosa ◽  
Francimary da Silva Carneiro ◽  
Merilene do Socorro Silva Costa
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
De Novo ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Google Earth Engine ◽  
Para Determinar

O objetivo foi analisar o processo de uso e ocupação do solo do município de Novo Progresso no Estado do Pará, interligando-o com as atividades de maior importância econômica desenvolvidas nesta região. Utilizou-se o shapefile de limite do município de Novo Progresso na plataforma online Google Earth Engine (GEE), que disponibilizou um mosaico de imagens orbitais, do satélite Landsat-8/OLI-TIRS, referentes ao ano de 2019. O processo de classificação foi feito a partir do Code Editor do GEE, utilizando um Índice espectral de vegetação para auxiliar a classificação (Normalized Difference Vegetation Index – NDVI). Foi utilizado o Software QGis 3.10.6 para elaborar os mapas de localização do município e o de classificação de uso e cobertura do solo. Os dados foram tabulados em planilhas para determinar as taxas de crescimento do período analisado. Para realizar a avaliação da confiabilidade da classificação foi utilizado o método de Exatidão Global e o Índice Kappa. Foi possível identificar que no ano de 2019, houve a incidência de 3.064.396,65 ha (80,3%) de floresta densa, uma área de 496.104,07 ha (13,0%) com solo exposto, 248.052,03 ha (6,5%) de floresta secundária, e apenas 7.632,37 ha (0,2%) com predominância de hidrografia, totalizando uma área de 3.816.185,13 ha.  As áreas que encontram-se com o solo exposto não estão diretamente relacionadas com o crescimento populacional, mas sim a forma como é estabelecido o uso do solo, com base nas principais atividades desenvolvidas na região considerando que a lógica produtiva ocorre de forma desordenada, não respeitando os critérios de desenvolvimento sustentável.

scholarly journals Mapping the Natural Distribution of Bamboo and Related Carbon Stocks in the Tropics Using Google Earth Engine, Phenological Behavior, Landsat 8, and Sentinel-2

2020 ◽  
Vol 12 (18) ◽  
pp. 3109 ◽  
Author(s):  
Manjunatha Venkatappa ◽  
Sutee Anantsuksomsri ◽  
Jose Alan Castillo ◽  
Benjamin Smith ◽  
Nophea Sasaki
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Carbon Stocks ◽  
Landsat 8 ◽  
Threshold Values ◽  
Landsat 8 Oli ◽  
Natural Distribution ◽  
Siem Reap ◽  
Google Earth Engine ◽  
Sentinel 2

Although vegetation phenology thresholds have been developed for a wide range of mapping applications, their use for assessing the distribution of natural bamboo and the related carbon stocks is still limited, especially in Southeast Asia. Here, we used Google Earth Engine (GEE) to collect time-series of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 images and employed a phenology-based threshold classification method (PBTC) to map the natural bamboo distribution and estimate carbon stocks in Siem Reap Province, Cambodia. We processed 337 collections of Landsat 8 OLI for phenological assessment and generated 121 phenological profiles of the average vegetation index for three vegetation land cover categories from 2015 to 2018. After determining the minimum and maximum threshold values for bamboo during the leaf-shedding phenology stage, the PBTC method was applied to produce a seasonal composite enhanced vegetation index (EVI) for Landsat collections and assess the bamboo distributions in 2015 and 2018. Bamboo distributions in 2019 were then mapped by applying the EVI phenological threshold values for 10 m resolution Sentinel-2 satellite imagery by accessing 442 tiles. The overall Landsat 8 OLI bamboo maps for 2015 and 2018 had user’s accuracies (UAs) of 86.6% and 87.9% and producer’s accuracies (PAs) of 95.7% and 97.8%, respectively, and a UA of 86.5% and PA of 91.7% were obtained from Sentinel-2 imagery for 2019. Accordingly, carbon stocks of natural bamboo by district in Siem Reap at the province level were estimated. Emission reductions from the protection of natural bamboo can be used to offset 6% of the carbon emissions from tourists who visit this tourism-destination province. It is concluded that a combination of GEE and PBTC and the increasing availability of remote sensing data make it possible to map the natural distribution of bamboo and carbon stocks.

scholarly journals Analisis Fenomena Pulau Panas Perkotaan Kota Bandung Menggunakan Google Earth Engine

2019 ◽  
Author(s):  
Muhammad Malik Ar-Rahiem ◽  
Muhamad Riza Fakhlevi
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat ◽  
Google Earth Engine

Pulau Panas Perkotaan (Urban Heat Island) adalah fenomena antropogenik akibat pengaruh urbanisasi. Kawasan perkotaan yang terbangun memiliki temperatur yang lebih hangat dibandingkan kawasan sekitarnya. Fenomena Pulau Panas Perkotaan di Kota Bandung diteliti menggunakan data Suhu Permukaan Tanah (Land Surface Temperature) yang diakuisisi dari satelit Landsat 8. Lima tahun data satelit dianalisis menggunakan piranti daring Google Earth Engine untuk menganalisis variasi temporal Pulau Panas Perkotaan di Kota Bandung dan sekitarnya. Suhu yang diakuisisi dari satelit dikonversi menjadi estimasi suhu permukaan dengan mempertimbangkan nilai Normalized Difference Vegetation Index. Hasil dari penelitian ini adalah peta persebaran rata-rata dan median suhu permukaan di Cekungan Bandung tahun 2013-2018, serta grafik seri waktu suhu permukaan di 3 jenis tata guna lahan yang mewakili daerah kota (sekitar Jalan Sudirman), hutan kota (Hutan Babakan Siliwangi), dan hutan (Tamah Hutan Raya Djuanda). Suhu rata-rata Kota Bandung pada tahun 2013-2018 adalah 26,93 oC (median seluruh data) dan 25,57oC (rata-rata seluruh data). Sementara perbandingan berdasarkan tata guna lahan; daerah kota memiliki suhu permukaan rata-rata 27,30 oC, daerah hutan kota memiliki suhu 21,31oC, dan daerah hutan memiliki suhu 18,60oC. Peta persebaran suhu panas permukaan dari citra Landsat 8 menunjukkan bahwa daerah hutan secara konsisten memiliki suhu paling rendah, diikuti dengan hutan kota, dan kemudian daerah kota menjadi area yang paling panas dengan suhu maksimal hingga 33,73oC. Penggunaan Google Earth Engine yang berbasis komputasi awan sangat memudahkan pengolahan data citra satelit dalam jumlah besar yang selama ini tidak memungkinkan dilakukan dengan cara konvensional (mengunduh dan memproses di komputer).

ANALISIS PERBANDINGAN METODE NORMALIZED VEGETATION INDEX DAN MAXIMUM LIKELIHOOD CLASSIFICATION UNTUK ANALISIS RUANG TERBUKA HIJAU (Studi Kasus di Kota Pekalongan, Jawa Tengah)

2021 ◽  
pp. 513
Author(s):  
Mohammad Slamet Sigit Prakoso ◽  
Rizki Dwi Safitri
Keyword(s):  
Maximum Likelihood ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Maximum Likelihood Classification

Ruang Terbuka Hijau (RTH) adalah suatu tempat yang luas dan terbuka yang dimaksudkan untuk penghijauan suatu kota, di mana di dalamnya ditumbuhi pepohonan. Dalam analisis ruang terbuka hijau dapat menggunakan beberapa metode, di antaranya yaitu metode Normalized Difference Vegetation Index (NDVI) dan metode Maximum Likelihood Classification. Tujuan penelitian ini untuk mengetahui perbedaan hasil dari analisis metode NDVI dan Maximum Likelihood Classification yang digunakan untuk mengetahui ruang terbuka hijau di Kota Pekalongan. Metode yang digunakan pada penelitian ini yaitu dengan menggunakan metode NDVI dan metode Maximum Likelihood Classification. Data yang digunakan yaitu Citra Landsat 8 OLI. Pengolahan data menggunakan software Arcgis 10.3. Hasil dari pengolahan berupa peta ruang terbuka hijau dari masing - masing metode. Secara kuantitatif dari hasil perhitungan luas metode NDVI, luas permukiman sebesar 3.016,53 ha, persawahan 609,39 ha, hutan kota 573,3 ha, dan badan air seluas 482,04 ha. Sedangkan untuk metode Maximum Likelihood Classification didapatkan hasil luas permukiman 2.278,26 ha, persawahan 1.141,83 ha, hutan kota 738,18 ha, dan badan air seluas 522,99 ha. Berdasarkan luasan RTH terhadap luas Kota Pekalongan, pada metode NDVI sebesar 25,2%, sedangkan untuk metode Maximum Likelihood Classification sebesar 40,1%. Dari hasil analisis diperoleh perbedaan luasan yang cukup signifikan yaitu pada luasan persawahan dan permukiman. Perbedaan hasil analisis terjadi akibat perbedaan klasifikasi warna citra pada saat pengolahan data.

scholarly journals Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Optimal Time ◽  
Quantitative Detection ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

scholarly journals Mapeamento da Vegetação Nativa do Cerrado na Região de Três Lagoas-MS com o Google Earth Engine

2019 ◽  
Vol 71 (3) ◽  
pp. 702-725
Author(s):  
Nayara Vasconcelos Estrabis ◽  
José Marcato Junior ◽  
Hemerson Pistori
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Google Earth ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Google Earth Engine

O Cerrado é um dos biomas existentes no Brasil e o segundo mais extenso da América do Sul. Possui grande importância devido a sua biodiversidade, ecossistema e principalmente por servir como um reservatório, ou “esponja”, que distribui água para os demais biomas, além de ser berço de nascentes de algumas das maiores bacias da América do Sul. No entanto, devido às atividades antrópicas praticadas (com destaque para a pecuária e silvicultura) e a redução da vegetação nativa, este bioma está ameaçado. Considerado como hotspot em biodiversidade, o Cerrado pode não existir em 2050. Com a necessidade de sua preservação, o objetivo desse trabalho consistiu em investigar o uso de algoritmos de aprendizado de máquina para realizar o mapeamento da vegetação nativa existente na região do município de Três Lagoas, utilizando a plataforma em nuvem Google Earth Engine. O processo foi realizado com uma imagem Landsat-8 OLI, datada de 10 de outubro de 2018, e com os algoritmos Random Forest (RF) e Support Vector Machine (SVM). Na validação da classificação, o RF e o SVM apresentaram índices kappa iguais a 0,94 e 0,97, respectivamente. O RF, quando comparado ao SVM, apresentou classificação mais ruidosa. Por fim, verificou-se a existência de vegetação nativa de aproximadamente 2556 km² ao adotar o RF e 2873 km² ao adotar SVM.

scholarly journals Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

2020 ◽  
Vol 9 (4) ◽  
pp. 257 ◽  
Author(s):  
Kiwon Lee ◽  
Kwangseob Kim ◽  
Sun-Gu Lee ◽  
Yongseung Kim
Keyword(s):  
Open Source ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Atmospheric Correction ◽  
Urban Forestry ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Reflectance Data

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

scholarly journals USO DO ÍNDICE TVDI E MODELO HAND PARA CARACTERIZAÇÃO DE CONDIÇÃO HÍDRICA

Irriga ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Lucimara Wolfarth Schirmbeck ◽  
Denise Cybis Fontana ◽  
Juliano Schirmbeck ◽  
Vagner Paz Mengue
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rio Grande ◽  
United States Geological Survey ◽  
Landsat 8 ◽  
Rio Grande Do Sul ◽  
Landsat 8 Oli ◽  
Porto Alegre ◽  
Soybean Crop ◽  
Dryness Index

USO DO ÍNDICE TVDI E MODELO HAND PARA CARACTERIZAÇÃO DE CONDIÇÃO HÍDRICA  LUCIMARA WOLFARTH SCHIRMBECK1; DENISE CYBIS FONTANA2; JULIANO SCHIRMBECK3 E VAGNER PAZ MENGUE4 1 Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia – Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, [email protected] Departamento de Plantas Forrageiras e Agrometeorologia – Faculdade de Agronomia –  Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, [email protected] Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia – Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, [email protected] Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia – Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, [email protected].  1 RESUMO O objetivo do trabalho foi avaliar a adequação do índice TVDI (Temperature Vegetation Dryness Index), obtido com sensores remotos orbitais, para caracterizar a condição hídrica de lavouras de soja no sul do Brasil. Para tanto, foram utilizadas imagens do satélite Landsat 8-OLI, obtidas da base de dados da USGS (United States Geological Survey), de três datas ao longo do ciclo da cultura da soja (5 de dezembro 2014 – implantação, 6 de janeiro 2015 - início de desenvolvimento e 7 de fevereiro de 2015 – pleno desenvolvimento vegetativo).  A área de cultivo de soja foi mapeada utilizando classificação digital (máxima verossimilhança) e validada com dados de campo. A área total mapeada foi estratificada em duas classes: áreas de várzea e áreas altas, através do uso do modelo HAND (Height Above the Nearest Drainage). Para tornar possível a comparação entre datas, o TVDI foi determinado usando um triângulo único para as três datas em conjunto, estabelecido a partir dos dados do NDVI (Normalized Difference vegetation Index) e da temperatura de superfície (TS), a qual foi estimada usando o algoritmo split-window. O TVDI permitiu diferenciar as condições hídricas na cultura da soja ao longo do ciclo e entre as classes de altitude; as áreas mais altas apresentaram maiores déficits quando comparadas às áreas de várzea. Foi possível ainda visualizar a migração dos pixels de soja dentro do triângulo evaporativo como consequência da fase de desenvolvimento da cultura e das condições hídricas. Palavras-chave: déficit hídrico, agricultura, Landsat 8-OLI.  SCHIRMBECK, L. W.; FONTANA, D. C.; SCHIRMBECK, J.; MENGUE, V.P. TVDI INDEX AND HAND MODEL FOR WATER CONDITION DESCRIPTION  2 ABSTRACT This work aims to evaluate the suitability of the Temperature Vegetation Dryness Index (TVDI), achieved through an orbital remote sensing system used to describe the condition of the water to be used on soybean crops in the South Region of Brazil. The Landsat 8-OLI satellite images were gathered from the USGS (United States Geological Survey) database of three different dates during the soybean crop cycle (December 5th, 2014 - implementation, January 6th, 2015 - beginning of growth and February 7th, 2015 - full vegetative growth). The soybean crop area was mapped using digital classification (maximum likelihood method) and validated with field data. The total mapped area was stratified into two classes: floodplain areas and high areas, using the HAND (Height Above the Nearest Drainage) model. To make the comparison between dates possible, TVDI was determined using a single triangle for all the three dates together, established using the Normalized Difference Vegetation Index (NDVI) and surface temperature (TS) data, which was estimated using Split-window algorithm. TVDI allowed us to differentiate the water conditions during the soybean crop cycle and between the two altitude classes; the higher areas presented larger deficits when compared to the floodplain areas. It was also possible to observe the migration of the soybean pixels within the evaporative triangle as a consequence of the crop’s development stage and the water conditions. Keywords: water deficit, agriculture, Landsat 8-OLI. 

scholarly journals A Long-term Spatial and Temporal Analysis of NDVI Changes in Java Island Using Google Earth Engine

2021 ◽  
Vol 936 (1) ◽  
pp. 012038
Author(s):  
Benedict ◽  
Lalu Muhamad Jaelani
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Data ◽  
Temporal Analysis ◽  
Google Earth ◽  
Vegetation Density ◽  
Angle Sensor ◽  
Study Results ◽  
Google Earth Engine

Abstract Java is Indonesia’s and the world’s most populous island. The increase in population on the island of Java reduces the area of forest and other vegetation covers. Landslides, floods, and other natural disasters are caused by reduced vegetation cover. Furthermore, it has the potential to lead to the extinction of flora and fauna. The Normalized Difference Vegetation Index (NDVI) can be used to monitor the vegetation cover. This study analyzes the NDVI changes value from 2005 to 2020 using Terra and Aqua MODIS image data processed using Google Earth Engine. Processing was carried out in some stages: down-setting, performing NDVI processing, calculating monthly average NDVI, calculating annual average NDVI, and analyzing. From the study results, the NDVI value of Terra and Aqua MODIS data has a solid but imperfect correlation coefficient due to differences in orbital time which causes differences in solar zenith angle, sensor viewing angle, and azimuth angle. Then from this study, it was found that overall, changes in vegetation density cover on the island of Java decreased, which was indicated by the NDVI decline rate of -0.00047/year. The most significant decrease in NDVI value occurred in the period 2015–2016, covering an area of 13994.630 km2, and the most significant increase in NDVI occurred in the period 2010–2011, covering an area of 2256.101 km2.

scholarly journals HazMapper: a global open-source natural hazard mapping application in Google Earth Engine

2021 ◽  
Vol 21 (5) ◽  
pp. 1495-1511
Author(s):  
Corey M. Scheip ◽  
Karl W. Wegmann
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Volcanic Eruptions ◽  
Relative Difference ◽  
Google Earth ◽  
Satellite Networks ◽  
Pyroclastic Flows ◽  
Hazard Mapping ◽  
Polar Regions ◽  
Google Earth Engine

Abstract. Modern satellite networks with rapid image acquisition cycles allow for near-real-time imaging of areas impacted by natural hazards such as mass wasting, flooding, and volcanic eruptions. Publicly accessible multi-spectral datasets (e.g., Landsat, Sentinel-2) are particularly helpful in analyzing the spatial extent of disturbances, however, the datasets are large and require intensive processing on high-powered computers by trained analysts. HazMapper is an open-access hazard mapping application developed in Google Earth Engine that allows users to derive map and GIS-based products from Sentinel or Landsat datasets without the time- and cost-intensive resources required for traditional analysis. The first iteration of HazMapper relies on a vegetation-based metric, the relative difference in the normalized difference vegetation index (rdNDVI), to identify areas on the landscape where vegetation was removed following a natural disaster. Because of the vegetation-based metric, the tool is typically not suitable for use in desert or polar regions. HazMapper is not a semi-automated routine but makes rapid and repeatable analysis and visualization feasible for both recent and historical natural disasters. Case studies are included for the identification of landslides and debris flows, wildfires, pyroclastic flows, and lava flow inundation. HazMapper is intended for use by both scientists and non-scientists, such as emergency managers and public safety decision-makers.

scholarly journals Possibility for Identifying/Extracting Rock Outcrop Using Landsat 8 OLI/TIRS - Case Study of Thua Thien Hue Province

2020 ◽  
Vol 36 (3) ◽  
Author(s):  
Nguyen Quang Tuan ◽  
Do Thi Viet Huong ◽  
Doan Ngoc Nguyen Phong ◽  
Nguyen Dinh Van
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Object Orientation ◽  
Image Method ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Kappa Index ◽  
Exposed Rock ◽  
Ratio Image

This paper approaches the ratio image method to extract the exposed rock information from the Landsat 8 OLI/TIRS satellite image (2019) according to the object orientation classification. Combining automatic interpretation and interpretation through threshold of image index values according to interpretation key the object orientation classification to separate soil object containing exposed rock and no exposed rock in Thua Thien Hue province. Using the Topsoil Grain Size Index (TGSI), the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI) and other related analytical problems have identified 40 exposed rock storage areas in the study area. The results have been verified in the field and the Kappa index is 85.10%.

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