Identification of the key landscape metrics indicating regional temperature at different spatial scales and vegetation transpiration

Peng, Yu; Wang, Qinghui; Bai, Lan

doi:10.1016/j.ecolind.2020.106066

Cited by 25 publications

(2 citation statements)

References 63 publications

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“…Previous studies show that the causes affecting LST changes are divided into three categories [ 16 ]: (1) land cover factors, including land use/land cover (LUCC) [ 17 ], land use intensity [ 18 ], land use landscape pattern [ 19 , 20 ], and vegetation cover [ 21 ]; (2) socio-economic factors, including anthropogenic heat and air pollution [ 22 , 23 ], population density [ 24 ], industrial layout [ 25 ], and energy consumption [ 26 ]; and (3) natural elements, including elevation [ 27 ], slope [ 28 ], precipitation [ 29 ], and light [ 30 ]. Numerous studies have shown that conditions such as a smaller density and shape of landscape pattern patches, higher vegetation cover, and more rivers and precipitation have a cooling effect on LST [ 31 ]. Areas with a high intensity of human activity, high population density, and low elevation have a warming impact on LST [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China

Zhang

Kasimu

Liang

et al. 2022

IJERPH

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An in-depth study of the influence mechanism of oasis land surface temperature (LST) in arid regions is essential to promote the stable development of the ecological environment in dry areas. Based on MODIS, MYD11A2 long time series data from 2003 to 2020, the Mann–Kendall nonparametric test, the Sen slope, combined with the Hurst index, were used to analyze and predict the trend of LST changes in the urban agglomeration on the northern slopes of the Tianshan Mountains. This paper selected nine influencing factors of the slope, aspect, air temperature, normalized vegetation index (NDVI), precipitation (P), nighttime light index (NTL), patch density (PD), mean patch area (AREA_MN), and aggregation index (AI) to analyze the spatial heterogeneity of LST from global and local perspectives using the geodetector (GD) model and multi-scale geo-weighted regression (MGWR) model. The results showed that the average LSTs of the urban agglomeration on the northern slopes of the Tianshan Mountains in spring, summer, autumn, and winter were 31.53 °C, 47.29 °C, 22.38 °C, and −5.20 °C in the four seasons from 2003 to 2020, respectively. Except for autumn, the LST of all seasons showed an increasing trend, bare soil and grass land had a warming effect, and agricultural land had a cooling effect. The results of factor detection showed that air temperature, P, and NDVI were the dominant factors affecting the spatial variation of LST. The interaction detection results showed that the interaction between air temperature and NDVI was the most significant, and the two-factor interaction was more effective than the single-factor effect on LST. The MGWR model results showed that the effects of PD on LST were positively correlated, and the impact of AREA_MN and AI on LST were negatively correlated, indicating that the dense landscape of patches has a cooling effect on LST. Overall, this study provides information for managers to carry out more targeted ecological stability regulations in arid zone oases and facilitates the development of regulatory measures to maintain the cold island effect and improve the environment.

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Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China

Zhang

Kasimu

Liang

et al. 2022

IJERPH

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“…Meanwhile, LPIs also contain some information about the spatial structures, internal mechanisms, and action processes of landscape patches (Seppelt & Schröder, 2006). Because LPIs can reveal the potential links and ecological processes between organisms and substances within landscape patterns (Knoke et al, 2016), landscape indicators are also commonly used as quantitative measures to characterize the landscape pattern of ecosystems in studying species distribution, ecological processes, and habitat quality (Peng et al, 2020; Weisshuhn, 2019; Xie et al, 2020). Thus, enhancing the representational ability and accuracy of LPIs is essential for understanding, managing, and evaluating ecosystems, and carrying out ecological environmental protection management (Bundy et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Fusing multidimensional hierarchical information into finer spatial landscape metrics

Xiao

et al. 2021

Ecology and Evolution

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One of the core issues of ecology is to understand the effects of landscape patterns on ecological processes. For this, we need to accurately capture changes in the fine landscape structures to avoid losing information about spatial heterogeneity. The landscape pattern indicators (LPIs) can characterize the spatial structures and give some information about landscape patterns. However, researches on LPIs had mainly focused on the horizontal structure of landscape patterns, while few studies addressed vertical relationships between the levels of hierarchical landscape structures. Thus, the ignorance of the vertical hierarchical relationships may cause serious biases and reduce LPIs' representational ability and accuracy. The hierarchy theory about the landscape pattern structures could notably reduce the loss of hierarchical information, and the information entropy could quantitatively describe the vertical status of landscape units. Therefore, we established a new multidimensional fusion method of LPIs based on hierarchy theory and information entropy. Here, we created a general fusion formula for commonly used simple LPIs based on two‐grade land use data (whose land use classification system contains two grades/levels) and derived 3 fusion landscape pattern indicators (FLIs) with a case study. The results show that the information about fine spatial structure is captured by the fusion method. The regions with the most differences between the FLIs and the traditional LPIs are those with the largest vertical structure such as the ecological ecotones, where vertical structure was ignored before. The FLIs have a finer spatial representational ability and accuracy, not only retaining the main trend information of first‐grade land use data, but also containing the internal detail information of second‐grade land use data. Capturing finer spatial information of landscape patterns should encourage the application of fusion method, which should be suitable for more LPIs or more dimensional data. And the increased accuracy of FLIs will improve ecological models that rely on finer spatial information.

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How can the landscape ecological security pattern be quantitatively optimized and effectively evaluated? An integrated analysis with the granularity inverse method and landscape indicators

Guan

Jiang

Cheng

2022

Environ Sci Pollut Res

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The optimization of the landscape ecological security pattern aims to construct a suitable ecological environment and promote the harmonious development between humans and nature. The optimization model of the ecological security pattern for the main urban area of Chongqing was constructed with the granularity inverse method, minimum cumulative resistance model, and spatial network analysis method. We used ecological nodes to optimize the landscape ecological security pattern by organically combining the landscape quantity and spatial structure. The results were as follows: (1) The optimal granularity for selecting the ecological source in the study area was 500 m. There were 220 ecological sources with a total area of 188691.03 hm 2 and a minimum area of 75.15 hm 2 . (2) The ecological buffer zone, protection and utilization zone, key development zone, coordinated control zone, and restricted development zone accounted for 57.78%, 20.87%, 12.36%, 6.48% and 2.50%, respectively, of the total area. (3) The constructed of the landscape ecological security pattern contained 70 ecological corridors with a total length of 415.89 km. The longest and shortest ecological corridors had lengths of 20.33 km and 1153.23 m, respectively. There were 17 ecological nodes of corridor-resistance and 27 ecological nodes of corridor-corridor. (4) 41 ecological node service areas were constructed, with a total area of approximately 236.0723 hm 2 , accounting for 0.04% of the total study area, and the largest and smallest ecological node areas were 6.0744 hm 2 and 0.0057 hm 2 , respectively. (5) The optimized result of the landscape ecological security pattern converted 209.1384 hm 2 of nonecological land into ecological land.

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Identification of the key landscape metrics indicating regional temperature at different spatial scales and vegetation transpiration

Cited by 25 publications

References 63 publications

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China

Fusing multidimensional hierarchical information into finer spatial landscape metrics

How can the landscape ecological security pattern be quantitatively optimized and effectively evaluated? An integrated analysis with the granularity inverse method and landscape indicators

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