A Low-cost Sentinel-2 Data and Rao's Q Diversity Index-based Application for Detecting, Assessing and Monitoring Coastal Land-cover/Land-use Changes at High Spatial Resolution

Remote sensing data with high spatial resolution is very useful to provideinformation about Gross Primary Production (GPP) especially over spatial coverage in theurban area. Most models of ecosystem carbon exchange based on remote sensing data usedlight use efficiency (LUE) model. The aim of this research was to analyze the distributionof annual GPP urban area of Denpasar. Two main satellite data used in this study wereALOS/AVNIR-2 and Aster satellite data. Result showed that annual value of GPP usingALOS/AVNIR-2 varied from 0.130 gC m-2 yr-1 to 2586.181 gC m-2 yr-1. Meanwhile, usingAster the value varied from 0.144 gC m-2 yr-1 to 2595.264 gC m-2 yr-1. The annual value ofGPP ALOS was lower than the value of Aster, because ALOS have high spatial resolutionand smaller interval of spectral resolution compared to Aster. Different land use couldeffect the value of GPP, because the different land use has different vegetation type,distribution, and different photosynthetic pathway type. The high spatial resolution of theremote sensing data is crucial to discriminate different land cover types in urban region.With heterogeneous land cover surface, maximum value of GPP using ALOS/AVNIR-2was smaller than that of Aster, however, the annual mean of GPP value usingALOS/AVNIR-2 was higher than that of Aster.

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CHANGES OF LAND USE AND LAND COVER IN SMALL ISLANDS CASE STUDY: WEH-SABANG ISLAND, INDONESIA

Jurnal Koridor ◽

10.32734/koridor.v10i1.1385 ◽

2019 ◽

Vol 10 (1) ◽

pp. 48-53 ◽

Cited By ~ 1

Author(s):

Arif, A.A. ◽

Machdar,I ◽

Azmeri ◽

Achmad,A

Keyword(s):

Land Use ◽

Land Cover ◽

Land Degradation ◽

Environmental Control ◽

Diversity Index ◽

Land Use Changes ◽

Coastal Areas ◽

Google Earth ◽

Small Island ◽

Lulc Changes

Development will increase sporadic land use and tend to cause land degradation. This paper is intended to investigate the effects of land use and land cover processes on Weh-Sabang Island, Indonesia. The duration of LULC changes is analyzed using Google Earth images from 2008 to 2018. Through observations of satellite imagery detected protected area and green space area has been greatly reduced in area and transformed into built-in land which functions as a tourist area and urban built environment. Where as in the coastal areas the change from vacant land to land that was built has also become larger over the past ten years. The increase in land density since 2008 is due to the construction of tourist attractions in hilly areas to coastal areas around Weh-Sabang Island, and can cause changes in the morphology and typology of the city of Sabang. Through the method of calculating the Land Diversity Index, changes can be made in the number of areas that have changed in the period of the year being monitored. The coastal areas experience more land use changes than hilly areas, and through observations of LULC changes across Weh Island, it is expected that efforts will be made to control land use changes in areas that have directly experienced land degradation, and must consider environmental control efforts. As small island like that which happened on Weh-Sabang Island. The westernmost island of the Republic of Indonesia.

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Land use land cover classification using local multiple pattern from very high resolution satellite imagery

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

10.5194/isprsarchives-xl-8-971-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 971-976 ◽

Cited By ~ 1

Author(s):

R. Suresh Kumar ◽

A. R. Mahesh Balaji

Keyword(s):

Land Use ◽

Land Cover ◽

Spatial Resolution ◽

Spatial Information ◽

High Spatial Resolution ◽

Spectral Variation ◽

Land Use Land Cover ◽

Texture Information ◽

Very High Spatial Resolution ◽

Very High

The recent development in satellite sensors provide images with very high spatial resolution that aids detailed mapping of Land Use Land Cover (LULC). But the heterogeneity in the landscapes often results in spectral variation within the same and spectral confusion among different LU/LC classes at finer spatial resolution. This leads to poor classification performances based on traditional spectral-based classification. Many studies have been addressed to improve this classification by incorporating texture information with multispectral images. Although different methods are available to extract textures from the satellite images, only a limited number of studies compared their performance in classification. The major problem with the existing texture measures is either scale/orientation/illumination variant (Haralick textures) or computationally difficult (Gabor textures) or less informative (Local binary pattern). This paper explores the use of texture information captured by Local Multiple Patterns (LMP) for LULC classification in a spectral-spatial classifier framework. LMP preserve more structural information and involves less computational efforts. Thus LMP is expected to be more promising for capturing spatial information from very high spatial resolution images. The proposed method is implemented with spectral bands and LMP derived from WorldView-2 multispectral imagery acquired for Madurai, India study area. The Multi-Layer-Perceptron neural network is used as a classifier. The proposed classification method is compared with LBP and conventional Maximum Likelihood Classification (MLC) separately. The classification results with 89.5% clarify the improvement offered by the LMP for LULC classification in comparison with the conventional approaches.

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A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity

Remote Sensing ◽

10.3390/rs12152399 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2399 ◽

Cited By ~ 1

Author(s):

Red Willow Coleman ◽

Natasha Stavros ◽

Vineet Yadav ◽

Nicholas Parazoo

Keyword(s):

Los Angeles ◽

Land Cover ◽

Spatial Resolution ◽

Urban Areas ◽

High Spatial Resolution ◽

Water Cycle ◽

Bare Soil ◽

Urban Vegetation ◽

Optical Imagery ◽

Sentinel 2

High spatial resolution maps of Los Angeles, California are needed to capture the heterogeneity of urban land cover while spanning the regional domain used in carbon and water cycle models. We present a simplified framework for developing a high spatial resolution map of urban vegetation cover in the Southern California Air Basin (SoCAB) with publicly available satellite imagery. This method uses Sentinel-2 (10–60 × 10–60 m) and National Agriculture Imagery Program (NAIP) (0.6 × 0.6 m) optical imagery to classify urban and non-urban areas of impervious surface, tree, grass, shrub, bare soil/non-photosynthetic vegetation, and water. Our approach was designed for Los Angeles, a geographically complex megacity characterized by diverse Mediterranean land cover and a mix of high-rise buildings and topographic features that produce strong shadow effects. We show that a combined NAIP and Sentinel-2 classification reduces misclassified shadow pixels and resolves spatially heterogeneous vegetation gradients across urban and non-urban regions in SoCAB at 0.6–10 m resolution with 85% overall accuracy and 88% weighted overall accuracy. Results from this study will enable the long-term monitoring of land cover change associated with urbanization and quantification of biospheric contributions to carbon and water cycling in cities.

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High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data

Acta Amazonica ◽

10.1590/1809-4392201505504 ◽

2016 ◽

Vol 46 (3) ◽

pp. 291-302 ◽

Cited By ~ 123

Author(s):

Cláudio Aparecido de ALMEIDA ◽

Alexandre Camargo COUTINHO ◽

Júlio César Dalla Mora ESQUERDO ◽

Marcos ADAMI ◽

Adriano VENTURIERI ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Environmental Modeling ◽

Visual Interpretation ◽

Land Cover Map ◽

Land Use Policies ◽

Landsat 5 Tm ◽

Spectral Mixture

ABSTRACT Understanding spatial patterns of land use and land cover is essential for studies addressing biodiversity, climate change and environmental modeling as well as for the design and monitoring of land use policies. The aim of this study was to create a detailed map of land use land cover of the deforested areas of the Brazilian Legal Amazon up to 2008. Deforestation data from and uses were mapped with Landsat-5/TM images analysed with techniques, such as linear spectral mixture model, threshold slicing and visual interpretation, aided by temporal information extracted from NDVI MODIS time series. The result is a high spatial resolution of land use and land cover map of the entire Brazilian Legal Amazon for the year 2008 and corresponding calculation of area occupied by different land use classes. The results showed that the four classes of Pasture covered 62% of the deforested areas of the Brazilian Legal Amazon, followed by Secondary Vegetation with 21%. The area occupied by Annual Agriculture covered less than 5% of deforested areas; the remaining areas were distributed among six other land use classes. The maps generated from this project - called TerraClass - are available at INPE's web site (http://www.inpe.br/cra/projetos_pesquisas/terraclass2008.php).

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