Rules to write mathematics to clarify metrics such as the land use dynamic degrees

Pontius, Robert Gilmore; Huang, Jinliang; Jiang, Wenle; Khallaghi, Sam; Lin, Youtao; Liu, Jiyuan; Quan, Bin; Su, Ye

doi:10.1007/s10980-017-0584-x

Cited by 31 publications

(15 citation statements)

References 27 publications

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“…Three indices commonly used were adopted to measure land-use dynamic and urban expansion patterns, including single land use dynamic degree (K) and comprehensive land-use dynamic degree (S) [ 10 , 21 , 22 ], and annual intensity of urban expansion intensity (SI) [ 5 , 23 , 24 , 25 ]. Table 2 presents the notation and equations for these indices.…”

Section: Methodsmentioning

confidence: 99%

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Measuring Land Change in Coastal Zone around a Rapidly Urbanized Bay

Huang

et al. 2018

IJERPH

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Urban development is a major cause for eco-degradation in many coastal regions. Understanding urbanization dynamics and underlying driving factors is crucial for urban planning and management. Land-use dynamic degree indices and intensity analysis were used to measure land changes occurred in 1990, 2002, 2009, and 2017 in the coastal zone around Quanzhou bay, which is a rapidly urbanized bay in Southeast China. The comprehensive land-use dynamic degree and interval level intensity analysis both revealed that land change was accelerating across the three time intervals in a three-kilometer-wide zone along the coastal line (zone A), while land change was fastest during the second time interval 2002–2009 in a separate terrestrial area within coastal zone (zone B). Driven by urbanization, built-up gains and cropland losses were active for all time intervals in both zones. Mudflat losses were active except in the first time interval in zone A due to the intensive sea reclamation. The gain of mangrove was active while the loss of mangrove is dormant for all three intervals in zone A. Transition level analysis further revealed the similarities and differences in processes within patterns of land changes for both zones. The transition from cropland to built-up was systematically targeted and stationary while the transition from woodland to built-up was systematically avoiding transition in both zones. Built-up tended to target aquaculture for the second and third time intervals in zone A but avoid Aquaculture for all intervals in zone B. Land change in zone A was more significant than that in zone B during the second and third time intervals at three-level intensity. The application of intensity analysis can enhance our understanding of the patterns and processes in land changes and suitable land development plans in the Quanzhou bay area. This type of investigation is useful to provide information for developing sound land use policy to achieve urban sustainability in similar coastal areas.

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Section: Methodsmentioning

confidence: 99%

“…Most of these studies focused their objectives on the patterns of land changes and the underlying causes. However, some of their metrics combine various patterns of change into one measurement, which can make interpretation challenging [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Measuring Land Change in Coastal Zone around a Rapidly Urbanized Bay

Huang

et al. 2018

IJERPH

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“…The changing speed and changing amplitude are analyzed by calculating landscape type transfer matrixes in order that changing trend can be characterized from spatial and temporal aspects. The variation of amplitude is expressed in the area change of landscape, while the changing speed is embodied in the single dynamic degree of LULC and integrated dynamic degree of land use change [15,16]. The single dynamic degree of land use change (SDDLUC) describes the changing degree rate of a specific land use type of the area.…”

Section: Methodsmentioning

confidence: 99%

Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach

et al. 2018

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In this work, we present a processing chain for landscape pattern and ecological security status assessment and prediction based on cellular automata Markov (CA-Markov) and pressure status response pattern (PSRP) models using remotely sensed data (RSD) captured in 1986, 1996, 2006, 2016, and RSD simulated in 2026 over Zhengzhou city, Henan province, China. Three major findings can be withdrawn through the experiments. First, there is a significant changing of landscape type area, especially for building land. The area of building land is up to more than 5%, from 1986 to 2016. Secondly, the heterogeneity of landscape is increasing, and the diversity of landscape is becoming more and more diversifying and complex. Third, the changing trend of ecological security of Zhengzhou city shaped as decreasing and increasing gradually during the last 40 years. While the ecological security status, nowadays, appeared to a good trend by contrast of the previous stages. The predicted results with CAMarkov model show that the level of ecological security is still in moderate and has a trend of moving toward to the center in 2026.

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“…Intensity analysis is a quantitative framework to analyze changes in terms of size and intensity at three levels based on transition matrix, namely, interval level, category level, and transition level [37][38][39]. The mathematical notation for intensity analysis is shown in Table 2.…”

Section: Intensity Analysismentioning

confidence: 99%

Changes in Ecosystems and Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area since the Reform and Opening Up in China

et al. 2021

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Ecosystem services provide important support for the sustainable development of humans; these services are provided by various ecosystems, but they have been severely influenced by anthropogenic activities globally in the past several decades. To respond to the Sustainable Development Goals of the United Nations, this study investigated the changes in ecosystem structure and estimated the associated ecosystem services value (ESV) since China’s reform and opening-up policy in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the most developed and populous areas of China. Our results showed that dramatic changes in ecosystem structure occurred in the GBA, characterized by unpresented construction land sprawl (an average of 148 km2/yr) and extensive farmland loss (an average of 111 km2/yr). The change size and rate of ecosystems from 2000 to 2010 was the biggest and fastest, followed by that from 1990 to 2000. The ESV of the study area showed an overall decreasing trend, declining from 464 billion yuan to 346 billion yuan. The ESV supported by forest ecosystems and water body ecosystems made dominant contributions to the total ESV, ranging from 92% to 95%. Strong spatial heterogeneity of the ESV of the GBA might be noted throughout the study period, with lower values in the central region and higher values in the surrounding region. To realize sustainable development in the GBA; this study strongly suggests that local governments, and the public, scientifically use various ecosystems and their services, focusing on vigorously protecting ecosystems with high and important ESVs, such as water body, wetland, forest, and farmland ecosystems.

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Rules to write mathematics to clarify metrics such as the land use dynamic degrees

Cited by 31 publications

References 27 publications

Measuring Land Change in Coastal Zone around a Rapidly Urbanized Bay

Measuring Land Change in Coastal Zone around a Rapidly Urbanized Bay

Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach

Changes in Ecosystems and Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area since the Reform and Opening Up in China

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