A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin

Hansen, Matthew C.; Roy, David P.; Lindquist, Erik; Adusei, Bernard; Justice, Christopher O.; Altstatt, Alice

doi:10.1016/j.rse.2007.11.012

Cited by 424 publications

(275 citation statements)

References 46 publications

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“…One of the best sources for land use studies is the Landsat series of satellites. Landsat data are useful for land surface mapping, change detection and monitoring (Cohen and Goward 2004;Alesheikh et al 2007;Hansen et al 2008;Masek et al 2008;Cetin 2009). Landsat data are particularly applicable for land use classification on a regional scale because of their lower cost, longer history and higher frequency of archives than other remote sensing data sources (Rozenstein and Karnieli 2010).…”

Section: Introductionmentioning

confidence: 99%

A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran

Mohammady

Moradi

Zeinivand

et al. 2014

Int. J. Environ. Sci. Technol.

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Land use classification is often the first step in land use studies and thus forms the basis for many earth science studies. In this paper, we focus on low-cost techniques for combining Landsat images with geographic information system approaches to create a land use map. In the Golestan region of Iran, we show that traditional supervised and unsupervised methods do not result in sufficiently accurate land use maps. Therefore, we evaluated a synthetic approach combining supervised and unsupervised methods with decision rules based on easily accessible ancillary data. For accuracy assessment, confusion matrices and kappa coefficients were calculated for the maps created with the supervised, unsupervised and synthetic approaches. Overall accuracy of the synthetic approach was 98.2 %, which is over the 85 % level that is considered satisfactory for planning and management purposes. This shows that integration of remote sensing data, ancillary data and decision rules provides better classification accuracy than traditional methods, without significant additional use of resources.

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Section: Introductionmentioning

confidence: 99%

A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran

Mohammady

Moradi

Zeinivand

et al. 2014

Int. J. Environ. Sci. Technol.

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“…The TM images were geo-metrically corrected using the Landsat GeoCover data set and the nearest neighborhood resampling method [31]. All images were co-registered to the UTM system (zone 48 N).…”

Section: Image Pre-processingmentioning

confidence: 99%

Rubber Tree Distribution Mapping in Northeast Thailand

Li¹,

Fox²

2011

IJG

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In many parts of mainland Southeast Asia rubber plantations are expanding rapidly in areas where the crop was not historically found. Monitoring and mapping the distribution of rubber trees in the region is necessary for developing a better understanding of the consequences of land-cover and land-use change on carbon and water cycles. In this study, we conducted rubber tree growth mapping in Northeast Thailand using Landsat 5 TM data. A Mahalanobis typicality method was used to identify different age rubber trees. Landsat 5 TM 30 m non-thermal reflective bands, NDVI and tasseled cap transformation components were selected as the model input metrics. The validation was carried out using provincial level agricultural statistical data on the rubber tree growth area. At regional (Northeast Thailand) and provincial scales, the estimates of mature and middle-age rubber stands produced from 30 m Landsat 5 TM data compared well (high statistical significance) with the provincial rubber tree growth statistical data.

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“…Asner et al, 2009;GOFC-GOLD, 2010Hansen et al, 2013;Hansen et al, 2008;Pratihast et al, 2012;Verbesselt et al, 2012). The extent to which existing institutional arrangements for forest monitoring in developing countries fulfil the technical and governance requirements imposed by REDD+ remain under studied.…”

Section: Scholarship On Forest Monitoring For Redd+ and Emerging Resementioning

confidence: 99%

“…This is so because in most scholarly debates (e.g. Asner et al, 2009;GOFC-GOLD, 2013;Hansen et al, 2013;Hansen et al, 2008;Herold, 2009;Herold and Johns, 2007;Romijn et al, 2012; forest monitoring for REDD+ is considered a highly technical, value-free and apolitical activity. However, building on the work of others Kowler and Larson, 2016;Vijge, 2016;Visseren-Hamakers et al, 2012), this dissertation argues that the concept of forest monitoring, and MRV in particular, is largely political.…”

Section: Scholarship On Forest Monitoring For Redd+ and Emerging Resementioning

confidence: 99%

“…Although the variables included in national forest surveys have been influenced by international developments (Holmgren and Persson, 2002a, b;Lei et al, 2009) Although the development of forest inventories in developing countries has been examined in terms of technical aspects, such as sampling strategies, plot designs, use of RS and information technologies, et cetera (Hansen et al, 2008;Holmgren and Persson, 2002a;Joseph et al, 2013a;Lei et al, 2010;McRoberts and Tomppo, 2007;Romijn et al, 2012;Tomppo et al, 2010a;Xie et al, 2011), exactly how and to what extent the international discussion on REDD+ MRV has shaped institutional arrangements for forest measurements in developing countries is unknown. This paper aims to bridge this knowledge gap by examining: (1) the historical development of institutional arrangements for forest assessments in Peru; and (2) how and to what extent the evolution of these institutional arrangements have been shaped by international processes on forests, especially REDD+ MRV.…”

Section: Introductionmentioning

confidence: 99%

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The role of forests in climate change mitigation : a discursive-institutional analysis of REDD+ MRV

Ochieng

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A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin

Cited by 424 publications

References 46 publications

A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran

A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran

Rubber Tree Distribution Mapping in Northeast Thailand

The role of forests in climate change mitigation : a discursive-institutional analysis of REDD+ MRV

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