Research on Land Use Change in Beijing Hanshiqiao Wetland Nature Reserve Using Remote Sensing and GIS

Zhang, W.W.; Yao, Ling; Li, H.; Sun, Deyong; Zhou, Li-An

doi:10.1016/j.proenv.2011.09.094

Cited by 19 publications

(10 citation statements)

References 7 publications

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“…The trend is hypothesised to have reduced the area of dambos from 12.5% in 1984, 17.36% in 2016, to 10.42% in 2050 (Tables 1 and 2). Results of this study are consistent with regional and global trends as reported in Malawi by Gondwe et al (2020), Alam et al (2011) in the Himalayas; Zhang et al (2011) in Beijing; Franke et al (2009) and Mwita (2013) in Tanzania. The results have shown that ‐NQC were recorded for woodlands and dambos while +NQC were recorded in plantations, settlements, grasslands, croplands and bareland further confirming that the latter five are degrading land uses.…”

Section: Discussionsupporting

confidence: 91%

Land use induced land cover changes and future scenarios in extent of Miombo woodland and Dambo ecosystems in the Copperbelt province of Zambia

et al. 2021

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The pattern of Miombo woodland conversion to other land uses and the attendant impacts on vital Miombo ecosystems such as dambos is not well understood. Using the Copperbelt province of Zambia as a case study, we assessed the spatio‐temporal patterns of Miombo woodland and dambo conversion to other land uses between 1984 and 2016 and predicted the changes to 2050. The effects of land use land cover change (LULCC) on the extent of Miombo woodlands and dambos was determined by intersecting layers of croplands, settlements, plantations, grasslands and barelands on woodland and dambo pixels. Prediction of future LULCC was done using the land change modeller (LCM) in TerrSet. It was observed that in the period between 1984 and 2016, woodlands decreased by 17.9% while dambos increased by 4.9%. The two classes were predicted to lose 26.4% and 2.0%, respectively, by 2050. Conversion to cropland was the highest contributor to woodland loss, accounting for 57.5% of total loss by 2016, and projected to reach 67.6% by 2050. Similarly, establishment of cropland was shown to result into 53.5% (2016) and 58.9% (2050) of loss of dambos. Expansion of croplands caused a decline in woodlands and dambos. Therefore, sustainable agriculture should be adopted.

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Section: Discussionsupporting

confidence: 91%

Land use induced land cover changes and future scenarios in extent of Miombo woodland and Dambo ecosystems in the Copperbelt province of Zambia

et al. 2021

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“…For evidence of wetland conversion we currently need to rely on case-study reports. Remote-sensing based observations of wetland conversion mostly address local to regional areas (e.g., 39 - 44 ). The meta-regression adds to the case-studies analysis because of its global consistency.…”

Section: Discussionmentioning

confidence: 99%

Drivers of Wetland Conversion: a Global Meta-Analysis

et al. 2013

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Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions.

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“…Land use dynamic degree (LUDD) can measure the rate of land-use change, compare the differences in specific areas, and predict the landscape pattern trend [39,63]. LUDD consist of the single land use dynamic degree (K) and the overall land use dynamic degree (K S ) [64]. The single land use dynamic degree K refers to the change rate of a certain type of land use during a specific period, reflecting the speed and magnitude of the change.…”

Section: Analysis Of Shanghai's Landscape Patternmentioning

confidence: 99%

Optimal Grain Size Based Landscape Pattern Analysis for Shanghai Using Landsat Images from 1998 to 2017

Wang¹,

Li²,

Zhang³

et al. 2021

Pol. J. Environ. Stud.

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While they are an effective tool for studying landscape patterns and describing land-use change, landscape metrics are sensitive to variation in spatial grain sizes. It is therefore crucially important to select an optimal grain size for characterizing urban landscape patterns. Due to accelerated urbanization, Shanghai, the economic capital of China, has seen drastic changes in landscape patterns in recent decades and it would be interesting to take Shanghai as an example for examining the grain effect of landscape patterns. In this study, from Shanghai's land use maps derived from Landsat images of 1998, 2007, and 2017 via random forest classification, a selection of landscape metrics was measured with 14 grain sizes ranging from 30 m to 460 m. Both the conventional first scale domain method and the information loss evaluation model were adopted to comprehensively determine an optimal grain size for characterizing Shanghai's landscape pattern. After that, the land use dynamic degree model was used to explore the change in Shanghai's landscape pattern under the optimal grain size. Results demonstrate that (1) the responses of landscape metrics varied with grain size, which could be divided into three categories, namely irregular trend, decreasing trend, and no clear change; that (2) the optimal spatial grain size for landscape pattern analysis was 60 m; and that (3) the degree of landscape aggregation decreased, whereas that of landscape diversity and fragmentation increased. This study shows a clear grain effect of landscape patterns and can provide useful insights for urban landscape planning.

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Research on Land Use Change in Beijing Hanshiqiao Wetland Nature Reserve Using Remote Sensing and GIS

Cited by 19 publications

References 7 publications

Land use induced land cover changes and future scenarios in extent of Miombo woodland and Dambo ecosystems in the Copperbelt province of Zambia

Land use induced land cover changes and future scenarios in extent of Miombo woodland and Dambo ecosystems in the Copperbelt province of Zambia

Drivers of Wetland Conversion: a Global Meta-Analysis

Optimal Grain Size Based Landscape Pattern Analysis for Shanghai Using Landsat Images from 1998 to 2017

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