A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Yu, Wenjuan; Zhou, Weiqi; Qian, Yuguo; Yan, Jingli

doi:10.1016/j.rse.2016.02.030

Cited by 155 publications

(98 citation statements)

References 54 publications

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“…We first generated the LULC map of 2010 by an object-based classification approach [20,21]. With this approach, the multi-resolution segmentation algorithm was used to create image objects.…”

Section: Classification Methodsmentioning

confidence: 99%

“…With this approach, the multi-resolution segmentation algorithm was used to create image objects. We created three levels of objects with the scale parameters setting as 10 (Level 1), 30 (Level 2), and 50 (Level 3) by testing different parameter values [20,21]. Objects at Level 1 were used for classification of water, grass, and barren land.…”

Section: Classification Methodsmentioning

confidence: 99%

“…Remote sensing has long been used to quantify changes in forested land [17][18][19][20], as it can explicitly reveal its change in space in a recurrent and consistent way [21]. Changes in spatial pattern of forested land have been typically studied by combining remote sensing data with landscape metrics [22].…”

Section: Introductionmentioning

confidence: 99%

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Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China

Zhou

Zhang

et al. 2017

Remote Sensing

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Abstract:Urban expansion has significant effects on forest loss and fragmentation. Previous studies mostly focused on how the amount of developed land affected forest loss and fragmentation, but neglected the impacts of its spatial pattern. This paper examines the effects of both the amount and spatial pattern of urban expansion on forest loss and fragmentation. We conducted a comparison study in the six largest urban megaregions in China-Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Wuhan (WH), Chengdu-Chongqing (CY), and Changsha-Zhuzhou-Xiangtan (CZT) urban megaregions. We first quantified both the magnitude and speed of urban expansion, and forest loss and fragmentation from 2000 to 2010. We then examined the relationships between urban expansion and forest loss and fragmentation by Pearson correlation and partial correlation analysis using the prefecture city as the analytical unit. We found: (1) urban expansion was a major driver of forest loss in the CZT, PRD, and CY megaregions, with 34.05%, 22.58%, and 19.65% of newly-developed land converted from forests. (2) Both the proportional cover of developed land and its spatial pattern (e.g., patch density) had significant impacts on forest fragmentation at the city level. (3) Proportional cover of developed land was the major factor for forest fragmentation at the city level for the PRD and YRD megaregions, but the impact of the spatial pattern of developed land was more important for the BTH and WH megaregions.

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Section: Classification Methodsmentioning

confidence: 99%

Section: Classification Methodsmentioning

confidence: 99%

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Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China

Zhou

Zhang

et al. 2017

Remote Sensing

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“…Many studies have revealed that an objected orient approach based on image segmentation has greater potential for land use mapping compare topixel-based approaches and can improve the accuracy of land use mapping (Oruc et al, 2004;Im et al, 2008;. Yu et al (2016) claimed that OBIC method has higher accuracy and efficiency, so effectively incorporates spatial information and expert knowledge. The results of Watmough et al (2017) revealed that OBIC consistently high accuracies for images with varying characteristics.…”

Section: Introductionmentioning

confidence: 99%

Comparison of conventional and advanced classification approaches by Landsat-8 imagery

Varamesh¹

2017

Appl Ecol Env Res

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Abstract.Over the last few years, most areas of Iran including Golestan province have posed considerable risks to human use and future development. Therefore, several reliable techniques are required to quantify, monitor and update land use maps of these areas to explore rates of environmental retreats. In this study, Landsat-8 ETM+ imageries of 2013 were consequently processed via Object Based Image Classification (OBIC) as an advanced approach and Maximum Likelihood Classification (MLC) as a conventional pixel based approaches to classify land use in Golestan province in Northeastern of Iran using an ENVI 4.8 and eCognition software packages. Overall accuracy and kappa coefficients were calculated to assess these approaches, which were respectively 94.69% and 0.93 for OBIC and 81.53% and 0.75 for MLC. In addition, accuracy rate over 85% indicates satisfactory for land use mapping and planning purposes. Our result showed that pixel-based classification approach do not lead to sufficiently accurate land use maps; and land use mapping using OBIC approach provides better classification accuracy especially when we have an extensive study area. Thus, development of this approach is appropriate for satellite images with moderate resolution.

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“…Rapid development of GIS and remote sensing techniques gives new opportunities in vegetation science (Cserhalmi and Erdos, 2016). Object-based image analysis is quickly gaining acceptance among remote sensors, and has demonstrated great potential for classification and change detection, compared to pixel-based approach (Blanschke, 2010; Myint et al, 2011;Zhou and Troy, 2008;Yu et al, 2016). The advantage of the object-based approach is that it offers new possibilities for image analysis because image objects can be characterised by features of different origin incorporating spectral values, texture, shape, context relationships and thematic or continuous information supplied by ancillary data.…”

Section: Introductionmentioning

confidence: 99%

Biotope Mapping in an Urban Environment for Sustainable Urban Development ‒ A Case Study in Southern Part of Turkey

Gürkan¹

2016

Appl Ecol Env Res

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Abstract. The aim of this contribution is mapping and analysing of biotopes sensitivity in Antakya urban city with its surroundings. The three objectives were: (1) to identify the biotope types which display the structural characteristics of the study area, (2) to map the biotopes using object-oriented classification and (3) to determine sensitivity of these biotopes using rarity and reestablishment of research area. The study mainly carried out in two stages: the first one determination of biotope types using information from field surveys and aerial images; the second stage determination of biotope sensitivity to human disturbance. The sensitivity assessment based on two parameters; the rarity and the re-establishment ability of biotopes. Based on sensitivity results, 1.581 ha of the research area -forest, river banks and river and banks-is very sensitive, 454,6 ha of the research area -cemetery, settlement with site, and maquie-of land sensitive.

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A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Cited by 155 publications

References 54 publications

Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China

Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China

Comparison of conventional and advanced classification approaches by Landsat-8 imagery

Biotope Mapping in an Urban Environment for Sustainable Urban Development ‒ A Case Study in Southern Part of Turkey

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