Global, Landsat-based forest-cover change from 1990 to 2000

Kim, Do-Hyung; Sexton, Joseph O.; Noojipady, Praveen; Huang, Chengquan; Anand, Anupam; Channan, Saurabh; Feng, Min; Townshend, J. R. G.

doi:10.1016/j.rse.2014.08.017

Cited by 198 publications

(123 citation statements)

References 46 publications

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“…Each Landsat scene was processed independently, and overlapping pixels from multiple images were composited by selecting the result at each location with highest posterior (water/ nonwater) probability. This 'best-pixel' compositing maximized classification certainty and filled gaps due to clouds and their shadows (Sexton, Song, Feng, et al 2013;Kim et al 2014). Finally, inland water was distinguished from marine water by referring to the Global Database of Administrative areas (GADM) version 2 (http://www.gadm.org/).…”

Section: Post-classification Filtering and Compositingmentioning

confidence: 99%

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

Feng

Sexton

Channan

et al. 2015

International Journal of Digital Earth

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294

190

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The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km 2 of inland water globally -nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal 'inland water' biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high, with commission errors <4% and omission errors <14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds -effects which were compounded by differences in image acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset is available for open access at the GLCF website (http://www.landcover.org).

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Section: Post-classification Filtering and Compositingmentioning

confidence: 99%

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

Feng

Sexton

Channan

et al. 2015

International Journal of Digital Earth

Self Cite

294

190

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“…Landsat images have been widely used in land cover classification because of their stable imaging quality [1,31,32,[58][59][60]. In this study, Level 1 terrain-corrected (L1T) Landsat images were selected as the primary data source for the land cover classifications.…”

Section: Data and Pre-processingmentioning

confidence: 99%

Landsat-Based Land Cover Change in the Beijing-Tianjin-Tangshan Urban Agglomeration in 1990, 2000 and 2010

Yang

Sun

2017

IJGI

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Rapid urbanization dramatically changes the local environment. A hybrid classification method is designed and applied to multi-temporal Landsat images and ancillary data to obtain land cover change datasets. A support vector machine (SVM) classifier is used to classify multi-temporal Landsat Enhanced Thematic Mapper Plus (ETM+) images that were collected in 2000 at the pixel level. These images are also segmented with the mean shift method. The impervious surface is refined based on a combination of the segmented objects and the SVM classification results. The changed areas in 1990 and 2010 are determined by comparing the Thematic Mapper (TM) and ETM+ images via the re-weighted multivariate alteration detection transformation method. The TM images that were masked as changed areas in 1990 and 2000 are input into the SVM classifier. Land cover maps for 1990 and 2010 are produced by combining the unchanged area in 2000 with the new classes of the changed areas in 1990 and 2010. Land cover change has continuously accelerated since 1990. Remarkably, arable land decreased, while the impervious surface area significantly increased.

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“…Landsat data is a good choice for mostly overcoming all these obstacles and mapping large-scale LULC maps in tropical regions because of: (i) systematic data acquisition; (ii) global coverage; and (iii) high temporal repetitivity [17]. It has been extensively used for LULC mapping [18,19] or monitoring forest cover and its changes [20,21]. Particularly, the 30 m TerraClass LULC maps derived from Landsat data by the collaboration of Brazilian National Institute for Space Research (INPE) and Brazilian Agricultural Research Corporation (EMBRAPA) provided a detailed follow-up of deforested areas in the Brazilian Legal Amazonian region from 2004 (see [22] for more details).…”

Section: Introductionmentioning

confidence: 99%

Regionalization of a Landscape-Based Hazard Index of Malaria Transmission: An Example of the State of Amapá, Brazil

Catry

Dessay

et al. 2017

Data

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Identifying and assessing the relative effects of the numerous determinants of malaria transmission, at different spatial scales and resolutions, is of primary importance in defining control strategies and reaching the goal of the elimination of malaria. In this context, based on a knowledge-based model, a normalized landscape-based hazard index (NLHI) was established at a local scale, using a 10 m spatial resolution forest vs. non-forest map, landscape metrics and a spatial moving window. Such an index evaluates the contribution of landscape to the probability of human-malaria vector encounters, and thus to malaria transmission risk. Since the knowledge-based model is tailored to the entire Amazon region, such an index might be generalized at large scales for establishing a regional view of the landscape contribution to malaria transmission. Thus, this study uses an open large-scale land use and land cover dataset (i.e., the 30 m TerraClass maps) and proposes an automatic data-processing chain for implementing NLHI at large-scale. First, the impact of coarser spatial resolution (i.e., 30 m) on NLHI values was studied. Second, the data-processing chain was established using R language for customizing the spatial moving window and computing the landscape metrics and NLHI at large scale. This paper presents the results in the State of Amapá, Brazil. It offers the possibility of monitoring a significant determinant of malaria transmission at regional scale.

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Global, Landsat-based forest-cover change from 1990 to 2000

Cited by 198 publications

References 46 publications

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

Landsat-Based Land Cover Change in the Beijing-Tianjin-Tangshan Urban Agglomeration in 1990, 2000 and 2010

Regionalization of a Landscape-Based Hazard Index of Malaria Transmission: An Example of the State of Amapá, Brazil

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