scholarly journals APPLICATION OF REMOTE SENSING AND GIS MONITORING URBANIZATION IN HO CHI MINH CITY THROUGH IMPERVIOUS SURFACES

2011 ◽  
Vol 14 (1) ◽  
pp. 65-77
Author(s):  
Van Thi Tran
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Good Accuracy ◽  
Impervious Surface ◽  
Remote Sensing And Gis ◽  
Urban Environments ◽  
Ho Chi Minh City ◽  
Impervious Surfaces ◽  
Training Samples ◽  
Gis Data

Impervious surface can be used as an indicator in assessing urban environments. In this study, we have used method of remote sensing through the impervious surface to detect urban area in Hochiminh city with good accuracy above 96%. The high accuracy of the measurements come from the application of techniques such as extraction of training samples based on brand ratios, supervised classification in combination with suplement GIS data. This method, in combination with the Landsat image database, can be ultilized in monitoring the development of urbanization in Hochiminh city.

scholarly journals Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform

2020 ◽  
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Changshan Wu ◽  
Xidong Chen ◽  
Yuan Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Human Activity ◽  
Impervious Surface ◽  
Google Earth ◽  
Impervious Surfaces ◽  
Sar Images ◽  
Training Samples ◽  
Multi Temporal ◽  
Google Earth Engine

Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, night-time light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and non-impervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, based on global training samples and multi-source and multi-temporal imagery, a random forest classifier was trained and used to generate corresponding impervious surface maps for each 5°×5° cell of a geographical grid. Finally, a global impervious surface map, produced by mosaicking numerous 5°×5° regional maps, was validated by interpretation samples and then compared with three existing impervious products (GlobeLand30, FROM_GLC and NUACI). The results indicated that the global impervious surface map produced using the proposed multi-source, multi-temporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 96.6 % and kappa coefficient of 0.903 as against 92.5 % and 0.769 for FROM_GLC, 91.1 % and 0.717 for GlobeLand30, and 87.43 % and 0.585 for NUACI. Therefore, it is concluded that a global 30-m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper are available at https://doi.org/10.5281/zenodo.3505079 (Zhang et al., 2019).

scholarly journals Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform

2020 ◽  
Vol 12 (3) ◽  
pp. 1625-1648 ◽  
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Changshan Wu ◽  
Xidong Chen ◽  
Yuan Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Human Activity ◽  
Impervious Surface ◽  
Google Earth ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Sar Images ◽  
Training Samples ◽  
Google Earth Engine

Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic-aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, nighttime light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30 m for 2015 by combining Landsat 8 Operational Land Image (OLI) optical images, Sentinel-1 SAR images and Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and nonimpervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, the local adaptive random forest classifiers, allowing for a regional adjustment of the classification parameters to take into account the regional characteristics, were trained and used to generate regional impervious surface maps for each 5∘×5∘ geographical grid using local training samples and multisource and multitemporal imagery. Finally, a global impervious surface map, produced by mosaicking numerous 5∘×5∘ regional maps, was validated by interpretation samples and then compared with five existing impervious products (GlobeLand30, FROM-GLC, NUACI, HBASE and GHSL). The results indicated that the global impervious surface map produced using the proposed multisource, multitemporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 95.1 % and kappa coefficient (one of the most commonly used statistics to test interrater reliability; Olofsson et al., 2014) of 0.898 as against 85.6 % and 0.695 for NUACI, 89.6 % and 0.780 for FROM-GLC, 90.3 % and 0.794 for GHSL, 88.4 % and 0.753 for GlobeLand30, and 88.0 % and 0.745 for HBASE using all 15 regional validation data. Therefore, it is concluded that a global 30 m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper is available at https://doi.org/10.5281/zenodo.3505079 (Zhang and Liu, 2019).

Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production

1997 ◽  
Vol 18 (16) ◽  
pp. 3459-3471 ◽  
Author(s):  
S. E. Franklin ◽  
M. B. Lavigne ◽  
M. J. Deuling ◽  
M. A. Wulder ◽  
E. R. Hunt
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Net Primary Production ◽  
Remote Sensing And Gis ◽  
Area Index ◽  
Gis Data

Distributed Watershed Model Compatible with Remote Sensing and GIS Data. I: Description of Model

2001 ◽  
Vol 6 (2) ◽  
pp. 91-99 ◽  
Author(s):  
Jean-Pierre Fortin ◽  
Richard Turcotte ◽  
Serge Massicotte ◽  
Roger Moussa ◽  
Josée Fitzback ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Watershed Model ◽  
Gis Data

scholarly journals REMOTE SENSING AND GIS FOR LAND COVER CLASSIFICATION AND CHANGE DETECTION IN GAZIPUR SADAR, BANGLADESH DURING 1973-2017

2018 ◽  
Vol 5 (2) ◽  
pp. 215
Author(s):  
Md Arafat Hassan ◽  
Rakibul Islam ◽  
Rehnuma Mahjabin
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Agricultural Land ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Remote Sensing And Gis ◽  
Vegetation Coverage ◽  
Accurate Information ◽  
Sal Forest ◽  
Shed Light

This paper has been developed to capture the land coverage change in Gazipur Sadar Upazila with the help of remote sensing data of 44 years from 1973 to 2017. After acquiring the study area image of 1973, 1991, 2006 and 2017 supervised classification method has been used to get the accurate information from the satellite image and the whole outcome has been transformed into measurable unit (sq km) and graphs. The accuracy of land coverage was ranged from 85% to 89%. The outcome says that the acceleration of economic growth and pressure of huge population took a heavy toll on the vegetation coverage which decreased -199.7%. People are destroying vegetation coverage for building up settlements and infrastructure. In the year 2017, the map shows that the built-up area increased 312.9% for industry, settlement and agricultural purpose. Moreover agricultural land also drops down from 42% to 32%.  The rapid rate of decreasing vegetation coverage and small amount of existing vegetation coverage only 57 sq km (in 2017) is a red alert for the region. The Sal forest and other special flora species of that region is valuable resource for environment. This paper shed light on the fact that it is urgent to protect vegetation coverage so it will help the authority to make good policies and use other techniques to save vegetation coverage.

COMPORTAMENTO DO SOFTWARE TERRAVIEW NA CLASSIFICAÇÃO SUPERVISIONADA EM DIFERENTES BACIAS

2016 ◽  
Vol 31 (3) ◽  
pp. 282
Author(s):  
Mikael Timóteo Rodrigues ◽  
Lincoln Gehring Cardoso ◽  
Sérgio Campos ◽  
Bruno Timóteo Rodrigues ◽  
Zacarias Xavier de Barros
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Supervised Classification ◽  
Remote Sensing Image ◽  
Remote Sensing And Gis ◽  
Spectral Pattern ◽  
Supervised Training ◽  
The Difference ◽  
Remote Sensing Image Processing ◽  
Agriculture Water

O objetivo principal desse trabalho é averiguar a atuação do software TerraView 4.2.2 desempenhando a classificação supervisiona por meio do padrão espectral em imagem Landsat 5, associada a comparação do uso da terra das bacias hidrográficas dos rios Lavapés e Capivara, inseridas no município de Botucatu/SP utilizando-se técnicas de sensoriamento remoto e geoprocessamento. As áreas de treinamento supervisionado foram definidas a partir de nove classes para bacia do Lavapés e sete para bacia do Capivara, fundamentais para o estudo e análise do uso e ocupação da terra, como mata, solo, culturas - agricultura, corpos d´água e malha urbana dentre outras classes encontradas. Tais áreas de treinamento supervisionado foram definidas por meio de polígonos que representaram as respectivas classes de uso e ocupação da terra, considerando a cor, brilho, padrão e textura emitida por cada pixel da imagem. A diferença de resultados entre as duas bacias avaliadas foi notória, onde a bacia do Capivara apresentou melhores resultados, seguramente por apresentar um número menor de classes de uso da terra e uma menor área urbana, assim causando menos confusões para o algoritmo. Outro fator evidente foi à clara diferença dos produtos derivados a partir da classificação gerada e posteriormente pós-classificados com o filtro majoritário (majority filter), onde sempre após a reclassificação a acurácia foi elevada, apresentado menos erros de omissão e comissão nas matrizes e suavização dos mapas classificados, com a eliminação de classes de 10 e 75 pixels por região, o que abrandou consideravelmente a estética dos mapas e consequentemente a diminuição de erros. PALAVRAS-CHAVE: Geoprocessamento, Sensoriamento Remoto, Processamento de Imagens, Uso do solo. BEHAVIOR TERRAVIEW SOFTWARE IN SUPERVISED CLASSIFICATION IN DIFFERENT WATERSHEDSABSTRACT: The main objective of this study is to ascertain the performance of the TerraView 4.2.2 software performing the classification oversees through the spectral pattern on Landsat 5, associated with comparing the land use of the Lavapés and Capivara’s watersheds, set in Botucatu/São Paulo using remote sensing and GIS. The areas of supervised training were set from nine classes for Lavapés watershed, and seven for Capivara watershed, fundamental for the study and analysis of the use and occupation of land as forest, soil, crops – Agriculture, Water Bodies and Mesh urban, found among other classes. Such areas of supervised training were defined by polygons representing the respective classes of use and occupation of land, considering the color, brightness, pattern and texture emitted by each pixel of the image. The difference in results between the two watersheds was evaluated notorious, where the Capivara watershed showed better results, surely by having a smaller number of land use classes and a smaller urban area, thus causing less confusion for the algorithm. Another obvious factor was the clear difference of products derived from the classification generated and subsequently post-classed with the majority filter, where ever after reclassification accuracy has always been high, presented less errors of omission and commission in the headquarters and smoothing of classified maps, with the elimination of 10 and 75 pixels per region classes, which greatly slowed the aesthetics of maps and therefore decrease errors.KEYWORDS: Geoprocessing, Remote Sensing, Image Processing, Use of the soil.

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