scholarly journals Dynamique De L’occupation Du Sol Dans Les Zones Humides De La Commune D’allada Au Sud-Benin (Sites Ramsar 1017 Et 1018)

2018 ◽  
Vol 14 (12) ◽  
pp. 59
Author(s):  
L. Estelle Brun ◽  
Djego J. Gaudence ◽  
Moussa Gibigaye ◽  
Brice Tente
Keyword(s):  
Sustainable Management ◽  
Supervised Classification ◽  
Landsat 8 ◽  
Maximum Likelihood Algorithm ◽  
Landsat 8 Oli ◽  
Human Pressure ◽  
Integral Element ◽  
Benin Republic ◽  
Anthropic Pressure

The wetlands are the integral element of the natural resource of Benin Republic. However, anthropic pressure on those “fragil” environments, contribute to the reducing of their surface and accordingly, to a loss their biodiversity. The target objective is to make cartography of land units from 1990 to 2014 in order to identify the various pressures upon the wet ecosystems. A 2014 Landsat 8 OLI-TIRS image and a 1990 map of Benin land cover were used to establish the cartography. We used the Maximum likelihood algorithm to execute the supervised classification of the landsat image in ERDAS. The mapping of the land’s units in the wetlands was then carried out in ArcGIS. The results revealed that the tree savana have completely disappeared. It represents 11.47 % of the landscape in 1990 against 0 % in 2014. The mosaics of fields and fallows under palm plantations have reduced to -30.42 % in 2014. They represent 66.63 % of the landscape. The land units which progressed are the mosaic of fields and fallow (12.06 %), the swamps (10.47 %), the plantations (5.26 %) and the agglomerations (2.71 %). This shows strong human pressure exerted on the natural vegetation of the wetlands in the Allada district. These results will provide the local authorities with a tool for decision support, for an efficient use and a sustainable management of these natural wet ecosystems.

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 ANALISIS PERUBAHAN CADANGAN KARBON DI KAWASAN GUNUNG PADANG KOTA PADANG

JURNAL BUANA ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 94
Author(s):  
Rina Suksesi ◽  
Dedi Hermon ◽  
Endah Purwaningsih
Keyword(s):  
Land Cover ◽  
Supervised Classification ◽  
Sampling Method ◽  
Forest Cover ◽  
Sampling Technique ◽  
Residential Area ◽  
Carbon Stocks ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Quantitative Descriptive

This study aims to determine (1) changes in land cover in the Mount Padang Region in 1996, 2006 and 2016, (2) changes in carbon stocks as a result of changes in land cover in the Mount Padang Region of Padang City. The type of research is quantitative descriptive. Changes in land cover isanalyzed based on Landsat TM 5 of 1996 and 2006, as well as Landsat 8 OLI of 2016, using ENVI 4.5 and ArcGIS 10.1 and supervised classification method. Value of carbon stocks is obtained from the equation C = B ×% C (0.47), by predicting biomass on each type of carbon pool using allometric equations, which D2,62 ρ B = 0.11, B = exp {-2.134 + 2.530 × ln (D)}, B = 0.281 D2,06, and B = 0.030 D2,13, where D (diameter at breast height of trees, cm) and ρ (wood density). The sampling technique used is stratified random sampling method which refers to the technique of each plot on land cover classes which are then converted to thehectares area. The results of the analysis show that (1) the land cover in the Mount Padang Region of Padang City in 1996 has forest area of 744.23 Ha (92.6%), mixed garden area of 39.44 Ha (4.9%), shrubs of 17, 92 Ha (2.2%), and the settlement area of 2.35 Ha (0.3%). 2006 forest cover an area of 696.84 Ha (87%), mixed garden area of 18.84 Ha (2%), shrubs covering 37.55 Ha (5%), and residential area of 50.71 ha (6%). 2016 forest cover an area of 533.50 Ha (66%), mixed garden covering an area of 69.14 Ha (9%),shrubs covering 119.81 Ha (15%), and residential area of 81.49 Ha (10%). (2) the carbon stock in 1996 amounted to 495,800.03 tons, in 2006 a number of 458,165.73 tons, and in 2016 a number of 369,223.00 tons. Over the last 20 years, as a result of land cover changes in carbon stocks in Padang Mountain Region has been reduced as much as 126,577.03 tons.

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.

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 PIXEL-BASED CLASSIFICATION ANALYSIS OF LAND USE LAND COVER USING SENTINEL-2 AND LANDSAT-8 DATA

2017 ◽  
Vol XLII-4/W6 ◽  
pp. 91-93 ◽  
Author(s):  
A. Sekertekin ◽  
A. M. Marangoz ◽  
H. Akcin
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Kappa Statistics ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Landsat 8 Oli ◽  
Sentinel 2

The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpening process was carried out for Landsat-8 data before classification because the spatial resolution of Landsat-8 (30m) is far from Sentinel-2 RGB and NIR bands (10m). LULC images were generated using pixel-based Maximum Likelihood (MLC) supervised classification method. As a result of the accuracy assessment, kappa statistics for Sentinel-2 and Landsat-8 data were 0.78 and 0.85 respectively. The obtained results showed that Sentinel-2 MSI presents more satisfying LULC images than Landsat-8 OLI data. However, in some areas of Sea class Landsat-8 presented better results than Sentinel-2.

scholarly journals ANALISIS VEGETASI DI TAMAN WISATA ALAM (TWA) SUNGAI DUMAI, RIAU

2021 ◽  
Vol 16 (1) ◽  
pp. 25-36
Author(s):  
Hanifah Ikhsani
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Vegetation Density ◽  
Vegetation Analysis ◽  
Landsat 8 Oli ◽  
Density Class ◽  
Vegetation Class ◽  
Do So

TWA Sungai Dumai is a tourist forest area and ensuring the preservation of natural potential. However, there are problems that can disrupt the sustainability of it, including forest and land fires and conversion of land use to agriculture and oil palm plantations. Until now, there is no vegetation analysis using satellite imagery in TWA Sungai Dumai, so it is important to do so that can be managed sustainably. This study  classification of vegetation density classes which are presented in the form of a vegetation density class map in it. This research uses Landsat-8 OLI / TIRS images from October 2017 and October 2020 which are processed to determine density class using Normalized Difference Vegetation Index algorithm. The vegetation density class with the highest area in 2017 was the vegetation density class (2380,832 ha or 66,819% of the total area), while the lowest area was the non-vegetation class (75,737 ha or 2,126% of the total area). The vegetation density class with the highest area in 2020 in TWA Sungai Dumai is dense vegetation density class (3205,039 ha or 89,950% of the total area), while the lowest area is non-vegetation class (1,637 ha or 0.046% of the total area)

scholarly journals CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE

Irriga ◽  
2020 ◽  
Vol 25 (1) ◽  
pp. 160-169
Author(s):  
Cesar De Oliveira Ferreira Silva
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Supervised Classification ◽  
Source Code ◽  
Regression Trees ◽  
Google Earth ◽  
Landsat 8 ◽  
Classification And Regression ◽  
Google Earth Engine

CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE   CÉSAR DE OLIVEIRA FERREIRA SILVA1   1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista (UNESP) Campus de Botucatu. Avenida Universitária, n° 3780, Altos do Paraíso, CEP: 18610-034, Botucatu – SP, Brasil, e-mail: [email protected].     1 RESUMO   Identificar áreas de irrigação usando imagens de satélite é um desafio que encontra em soluções de computação em nuvem um grande potencial, como na ferramenta Google Earth Engine (GEE), que facilita o processo de busca, filtragem e manipulação de grandes volumes de dados de sensoriamento remoto sem a necessidade de softwares pagos ou de download de imagens. O presente trabalho apresenta uma implementação de classificação supervisionada de áreas irrigadas e não-irrigadas na região de Sorriso e Lucas do Rio Verde/MT com o algoritmo Classification and Regression Trees (CART) em ambiente GEE utilizando as bandas 2-7 do satélite Landsat-8 e os índices NDVI, NDWI e SAVI. A acurácia da classificação supervisionada foi de 99,4% ao utilizar os índices NDWI, NDVI e SAVI e de 98,7% sem utilizar esses índices, todas consideradas excelentes. O tempo de processamento médio, refeito 10 vezes, foi de 52 segundos, considerando todo o código-fonte desenvolvido desde a filtragem das imagens até a conclusão da classificação. O código-fonte desenvolvido é apresentado em anexo de modo a difundir e incentivar o uso do GEE para estudos de inteligência espacial em irrigação e drenagem por sua usabilidade e fácil manipulação.   Keywords: computação em nuvem, sensoriamento remoto, hidrologia, modelagem.     SILVA, C. O .F SUPERVISED CLASSIFICATION OF IRRIGATED AREA USING SPECTRAL INDEXES FROM LANDSAT-8 IMAGES WITH GOOGLE EARTH ENGINE     2 ABSTRACT   Identifying irrigation areas using satellite images is a challenge that finds great potential in cloud computing solutions as the Google Earth Engine (GEE) tool, which facilitates the process of searching, filtering and manipulating large volumes of remote sensing data without the need for paid software or image downloading. The present work presents an implementation of the supervised classification of irrigated and rain-fed areas in the region of Sorriso and Lucas do Rio Verde/MT with the Classification and Regression Trees (CART) algorithm in GEE environment using bands 2-7 of the Landsat- 8 and the NDVI, NDWI and SAVI indices. The accuracy of the supervised classification was 99.4% when using NDWI, NDVI and SAVI indices and 98.7% without using these indices, which were considered excellent. The average processing time, redone 10 times, was 52 seconds, considering all the source code developed from the filtering of the images to the conclusion of the classification. The developed source code is available in the appendix in order to disseminate and encourage the use of GEE for studies of spatial intelligence in irrigation and drainage due to its usability and easy manipulation.   Keywords: cloud computing, remote sensing, hydrology, modeling.

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