This page has only limited features, please log in for full access.
Remotely sensed land surface temperature (LST) distribution has played a valuable role in land surface processes studies from local to global scales. However, it is still difficult to acquire concurrently high spatiotemporal resolution LST data due to the trade-off between spatial and temporal resolutions in thermal remote sensing. To address this problem, various methods have been proposed to enhance the resolutions of LST data, and substantial progress in this field has been achieved in recent years. Therefore, this study reviewed the current status of resolution enhancement methods for LST data. First, three groups of enhancement methods—spatial resolution enhancement, temporal resolution enhancement, and simultaneous spatiotemporal resolution enhancement—were comprehensively investigated and analyzed. Then, the quality assessment strategies for LST resolution enhancement methods and their advantages and disadvantages were specifically discussed. Finally, key directions for future studies in this field were suggested, i.e., synergy between process-driven and data-driven methods, cross-comparison among different methods, and improvement in localization strategy.
Qi Mao; Jian Peng; Yanglin Wang. Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives. Remote Sensing 2021, 13, 1306 .
AMA StyleQi Mao, Jian Peng, Yanglin Wang. Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives. Remote Sensing. 2021; 13 (7):1306.
Chicago/Turabian StyleQi Mao; Jian Peng; Yanglin Wang. 2021. "Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives." Remote Sensing 13, no. 7: 1306.
Wind erosion is the main form of soil erosion in arid and semi‐arid areas. It leads to soil loss and land degradation, which aggravates ecosystem vulnerability and threatens regional sustainable development. Exploring wind erosion and associating driving factors can provide useful information to reduce soil wind erosion and solve corresponding environmental problems. Southern Africa is characterized with severe soil wind erosion, which has brought a series of socioeconomic issues, such as food crises and poverty. This study used meteorological and remote sensing data, and the revised wind erosion equation (RWEQ) model to explore the spatio‐temporal dynamics of soil erosion in southern Africa from 1991 to 2015. The impact of climate change on soil wind erosion was also analyzed. The results showed that wind erosion fluctuated during the study period, which first showed a downward trend and then stabilized at a relatively low level after 2010. Soil wind erosion across 66.65% of the study area significantly decreased (p < 0.05) and near‐surface wind speed was the most important influencing factor. The decrease of wind speed can significantly reduce the soil wind erosion across 39.89% of the area. Temperature and precipitation were significantly related to soil wind erosion over 18.96% and 24.63% of the study area, respectively. Both can also indirectly affect soil wind erosion through their impacts on vegetation cover. This study will help decision‐makers to identify high‐risk areas for soil erosion in southern Africa and to take countermeasures effectively.
Chaonan Zhao; Hanbing Zhang; Man Wang; Hong Jiang; Jian Peng; Yanglin Wang. Impacts of climate change on wind erosion in Southern Africa between 1991 and 2015. Land Degradation & Development 2021, 32, 2169 -2182.
AMA StyleChaonan Zhao, Hanbing Zhang, Man Wang, Hong Jiang, Jian Peng, Yanglin Wang. Impacts of climate change on wind erosion in Southern Africa between 1991 and 2015. Land Degradation & Development. 2021; 32 (6):2169-2182.
Chicago/Turabian StyleChaonan Zhao; Hanbing Zhang; Man Wang; Hong Jiang; Jian Peng; Yanglin Wang. 2021. "Impacts of climate change on wind erosion in Southern Africa between 1991 and 2015." Land Degradation & Development 32, no. 6: 2169-2182.
Unprecedented rapid urbanization in China during the past several decades has been accompanied by extensive urban landscape renewal, which has increased the urban thermal environmental risk. However, landscape change is a sufficient but not necessary condition for land surface temperature (LST) variation. Many studies have merely highlighted the correlation between landscape pattern and LST, while neglecting to comprehensively present the spatiotemporal diversification of LST change under urban landscape renewal. Taking the main city of Shenzhen as a case study area, this study tracked the landscape renewal and LST variation for the period 1987–2015 using 49 Landsat images. A decision tree algorithm suitable for fast landscape type interpretation was developed to map the landscape renewal. Analytical tools that identified hot-cold spots, the gravity center, and transect of LST movement were adopted to identify LST changes. The results showed that the spatial variation of LST was not completely consistent with landscape change. The transformation from Green landscape to Grey landscape usually increased the LST within a median of 0.2 °C, while the reverse transformation did not obviously decrease the LST (the median was nearly 0 °C). The median of LST change from Blue landscape to Grey landscape was 1.0 °C, corresponding to 0.5 °C in the reverse transformation. The imbalance of LST change between the loss and gain of Green or Blue landscape indicates the importance of protecting natural space, where the benefits in terms of temperature mitigation cannot be completely substituted by reverse transformation.
Yanxu Liu; Jian Peng; Yanglin Wang. Diversification of Land Surface Temperature Change under Urban Landscape Renewal: A Case Study in the Main City of Shenzhen, China. Remote Sensing 2017, 9, 919 .
AMA StyleYanxu Liu, Jian Peng, Yanglin Wang. Diversification of Land Surface Temperature Change under Urban Landscape Renewal: A Case Study in the Main City of Shenzhen, China. Remote Sensing. 2017; 9 (9):919.
Chicago/Turabian StyleYanxu Liu; Jian Peng; Yanglin Wang. 2017. "Diversification of Land Surface Temperature Change under Urban Landscape Renewal: A Case Study in the Main City of Shenzhen, China." Remote Sensing 9, no. 9: 919.
Use of the normalized difference vegetation index (NDVI) to build long-term vegetation trends is one of the most effective techniques for identifying global environmental change. Trend identification can be achieved by ordinary least squares (OLS) analysis or the Theil–Sen (TS) procedure with a Mann–Kendall (MK) significance test, and these linear regression approaches have been widely used. However, vegetation changes are not linear, and thus the response of vegetation to global climate change may follow non-linear trends. In this article, a polynomial trend-fitting method, which uses stepwise regression and expands on previous research, is presented. With an improved fitting ability, this procedure may reveal trends that were concealed by linear fitting methods. Globally, the traditional TS-MK method reveals significant greening trends for 37.27% of vegetated land, and significant browning trends for 7.98%. Using the polynomial analysis, 34.62% of pixels were fitted by high-order trends. The significant greening trends covered up to 30% of cultivated land, thus indicating that cultivated vegetation may be increasing faster than natural vegetation. Significant vegetation browning mostly occurred in sparse vegetation areas, which suggests that vegetation growth may be more sensitive to climate change in arid regions. Our results show that use of polynomial analysis can help further elucidate global NDVI trends.
Yanxu Liu; Yanglin Wang; Yueyue Du; Mingyue Zhao; Jian Peng. The application of polynomial analyses to detect global vegetation dynamics during 1982–2012. International Journal of Remote Sensing 2016, 37, 1568 -1584.
AMA StyleYanxu Liu, Yanglin Wang, Yueyue Du, Mingyue Zhao, Jian Peng. The application of polynomial analyses to detect global vegetation dynamics during 1982–2012. International Journal of Remote Sensing. 2016; 37 (7):1568-1584.
Chicago/Turabian StyleYanxu Liu; Yanglin Wang; Yueyue Du; Mingyue Zhao; Jian Peng. 2016. "The application of polynomial analyses to detect global vegetation dynamics during 1982–2012." International Journal of Remote Sensing 37, no. 7: 1568-1584.
Urban-rural development and transformation is profoundly changing the socioeconomic system as well as the natural environment. The study uses the AHP (Analytic Hierarchy Process) method to construct a top-down index of human activity based around five dimensions (population, land, industry, society, and environment) to evaluate the spatial characteristics in the region east of the Hu Huanyong line, China, in 1994 and 2010. Then, we investigate the spatial-temporal pattern using the methods of hotspot analysis, local Moran’s I index and Pearson correlation coefficient. The calculation showed that: (1) northeast China was experiencing an economic recession during study period, and the implementation of revitalization plan have not controlled the recession trend yet; (2) Pearson correlation analysis showed that the improvement of population quality promote the development of industry and society systems significantly during study period; and (3) negative correlation between Population Development Index (PDI) change and Population Transformation Index (PTI) change (along with the Society Transformation Index (STI) change and Industry Transformation Index (ITI) change) reflected that east of the Hu Huanyong line, China was in a “demographic dividend” period. Then, with the help of SOFM neural network algorithm, we divided the study area into six types of region, and found that municipalities, provincial capitals, Yangtze River Delta region and cities on the North China Plain owned the greatest development, while cities in southwest and northeast China showed relatively poor development during study period.
Zhichao Hu; Yanglin Wang; Yansui Liu; Hualou Long; Jian Peng. Spatio-Temporal Patterns of Urban-Rural Development and Transformation in East of the “Hu Huanyong Line”, China. ISPRS International Journal of Geo-Information 2016, 5, 24 .
AMA StyleZhichao Hu, Yanglin Wang, Yansui Liu, Hualou Long, Jian Peng. Spatio-Temporal Patterns of Urban-Rural Development and Transformation in East of the “Hu Huanyong Line”, China. ISPRS International Journal of Geo-Information. 2016; 5 (3):24.
Chicago/Turabian StyleZhichao Hu; Yanglin Wang; Yansui Liu; Hualou Long; Jian Peng. 2016. "Spatio-Temporal Patterns of Urban-Rural Development and Transformation in East of the “Hu Huanyong Line”, China." ISPRS International Journal of Geo-Information 5, no. 3: 24.
Quantifying the long-term trends of changes in terrestrial vegetation on a large scale is an effective method for detecting the effects of global environmental change. In view of the trend towards overall restoration and local degradation of terrestrial vegetation in China, it is necessary to pay attention to the spatial processes of vegetative restoration or degradation, as well as to clarify the temporal and spatial characteristics of vegetative growth in greater geographical detail. However, traditional linear regression analysis has some drawbacks when describing ecological processes. Combining nonparametric linear regression analysis with high-order nonlinear fitting, the temporal and spatial characteristics of terrestrial vegetative growth in China during 1982–2012 were detected using the third generation of Global Inventory Modeling and Mapping Studies (GIMMS3g) dataset. The results showed that high-order curves could be effective. The region joining Ordos City and Shaanxi Gansu Ningxia on the Loess Plateau may have experienced restoration–degradation–restoration processes of vegetative growth. In the Daloushan Mountains, degradation–restoration processes of vegetative growth may have occurred, and the occurrence of several hidden vegetative growth processes was located in different regions of eastern China. Changes in cultivated vegetation were inconsistent with changes in other vegetation types. In southern China and some high-altitude areas, temperature was the primary driver of vegetative growth on an interannual scale, while in the north, the effect of rainfall was more significant. Nevertheless, the influence of climate on vegetation activity in large urban areas was weak. The trend types of degradation–restoration processes in several regions were inconsistent with the implements of regional land development and protection strategy. Thus, the role of human activity cannot be ignored. In future studies, it will be still necessary to quantify the effects of human management on spatial patterns, develop trend-fitting methods, and explore more refined methods of analyzing the driving forces affecting large-scale changes in vegetative growth.
Yanxu Liu; Xianfeng Liu; Li Shuangshuang; Shuangshuang Li; Jian Peng; Yanglin Wang. Analyzing nonlinear variations in terrestrial vegetation in China during 1982–2012. Environmental Monitoring and Assessment 2015, 187, 1 -14.
AMA StyleYanxu Liu, Xianfeng Liu, Li Shuangshuang, Shuangshuang Li, Jian Peng, Yanglin Wang. Analyzing nonlinear variations in terrestrial vegetation in China during 1982–2012. Environmental Monitoring and Assessment. 2015; 187 (11):1-14.
Chicago/Turabian StyleYanxu Liu; Xianfeng Liu; Li Shuangshuang; Shuangshuang Li; Jian Peng; Yanglin Wang. 2015. "Analyzing nonlinear variations in terrestrial vegetation in China during 1982–2012." Environmental Monitoring and Assessment 187, no. 11: 1-14.
Vegetation is one of the most important components of the terrestrial ecosystem and, thus, monitoring the spatial and temporal dynamics of vegetation has become the key to exploring the basic process of the terrestrial ecosystem. Vegetation change studies have focused on the relationship between climatic factors and vegetation dynamics. However, correlations among the climatic factors always disturb the results. In addition, the impact of anthropogenic activities on vegetation dynamics was indeterminate. Here, vegetation dynamics in 14 provinces in Eastern China over a 10-year period was quantified to determine the driving mechanisms relating to climate and anthropogenic factors using partial correlation analysis. The results showed that from 1999 to 2008, the vegetation density increased in the whole, with spatial variations. The vegetation improvement was concentrated in the Yangtze River Delta, with the vegetation degradation concentrated in the other developed areas, such as Beijing-Tianjin-Hebei Region and the Pearl River Delta. The annual NDVI changes were mainly driven by temperature in Northeast China and the Pearl River Delta, and by precipitation in the Bohai Rim; while in the Yangtze River Delta, the driving forces of temperature and precipitation almost equaled each other. Furthermore, the impact of anthropogenic activities on vegetation dynamics had accumulative effects in the time series, and had a phase effect on the vegetation change trend.
Jian Peng; You Li; Lu Tian; Yanxu Liu; Yanglin Wang. Vegetation Dynamics and Associated Driving Forces in Eastern China during 1999–2008. Remote Sensing 2015, 7, 13641 -13663.
AMA StyleJian Peng, You Li, Lu Tian, Yanxu Liu, Yanglin Wang. Vegetation Dynamics and Associated Driving Forces in Eastern China during 1999–2008. Remote Sensing. 2015; 7 (10):13641-13663.
Chicago/Turabian StyleJian Peng; You Li; Lu Tian; Yanxu Liu; Yanglin Wang. 2015. "Vegetation Dynamics and Associated Driving Forces in Eastern China during 1999–2008." Remote Sensing 7, no. 10: 13641-13663.
Identifying historical trends in the integrated frequencies of various climate extremes is meaningful in climatic hazard research. However, the variation trends in regional climate extremes still need to be described by more effective indices, correlations among multiple climatic extremes and different regions need to be quantified, and the urban heat island backgrounds and thermal bioclimate conditions in which people live need to be noted. In this study, the threats of heat wave, heavy rain, strong wind, and Universal Thermal Climate Index (UTCI) stress were identified both by units of days using the 90th percentile threshold, and by an unscaled magnitude index derived from kernel density functions for Guangzhou and Shenzhen, China, in 1960–2013. The results show that both metropolises experienced an increase in heat wave threat and a decrease in strong wind threat, and the change amplitudes were higher for Guangzhou than Shenzhen. The correlation of heat wave threat between the two metropolises was significant, while the other correlations depended on the city and index. The heat wave threat was correlated with the UTCI stress in Guangzhou, while both heat wave threat and UTCI stress were correlated with strong wind threat in Shenzhen. The UTCI stress indicated that bioclimate conditions for human habitat have not deteriorated, especially in Shenzhen. In the daily-level results, the heat waves had close relationship between the two adjacent cities, and people suffered from hazard events were usually in high weighted indices of extremes.
Yanxu Liu; Shuangshuang Li; Yanglin Wang; Tian Zhang; Jian Peng; Tianyi Li. Identification of multiple climatic extremes in metropolis: a comparison of Guangzhou and Shenzhen, China. Natural Hazards 2015, 79, 939 -953.
AMA StyleYanxu Liu, Shuangshuang Li, Yanglin Wang, Tian Zhang, Jian Peng, Tianyi Li. Identification of multiple climatic extremes in metropolis: a comparison of Guangzhou and Shenzhen, China. Natural Hazards. 2015; 79 (2):939-953.
Chicago/Turabian StyleYanxu Liu; Shuangshuang Li; Yanglin Wang; Tian Zhang; Jian Peng; Tianyi Li. 2015. "Identification of multiple climatic extremes in metropolis: a comparison of Guangzhou and Shenzhen, China." Natural Hazards 79, no. 2: 939-953.
Changes in biodiversity owing to vegetation degradation resulting from widespread urbanization demands serious attention. However, the connection between vegetation degradation and urbanization appears to be complex and nonlinear, and deserves a series of long-term observations. On the basis of the Normalized Difference Vegetation Index (NDVI) and the image’s digital number (DN) in nighttime stable light data (NTL), we delineated the spatiotemporal relations between urbanization and vegetation degradation of different metropolises by using a simplified NTL calibration method and Theil-Sen regression. The results showed clear and noticeable spatiotemporal differences. On spatial relations, rapidly urbanized cities were found to have a high probability of vegetation degradation, but in reality, not all of them experience sharp vegetation degradation. On temporal characteristics, the degradation degree was found to vary during different periods, which may depend on different stages of urbanization and climate history. These results verify that under the scenario of a vegetation restoration effort combined with increasing demand for a high-quality urban environment, the urbanization process will not necessarily result in vegetation degradation on a large scale. The positive effects of urban vegetation restoration should be emphasized since there has been an increase in demand for improved urban environmental quality. However, slight vegetation degradation is still observed when NDVI in an urbanized area is compared with NDVI in the outside buffer. It is worthwhile to pay attention to landscape sustainability and reduce the negative urbanization effects by urban landscape planning.
Yanxu Liu; Yanglin Wang; Jian Peng; Yueyue Du; Xianfeng Liu; Shuangshuang Li; Donghai Zhang. Correlations between Urbanization and Vegetation Degradation across the World’s Metropolises Using DMSP/OLS Nighttime Light Data. Remote Sensing 2015, 7, 2067 -2088.
AMA StyleYanxu Liu, Yanglin Wang, Jian Peng, Yueyue Du, Xianfeng Liu, Shuangshuang Li, Donghai Zhang. Correlations between Urbanization and Vegetation Degradation across the World’s Metropolises Using DMSP/OLS Nighttime Light Data. Remote Sensing. 2015; 7 (2):2067-2088.
Chicago/Turabian StyleYanxu Liu; Yanglin Wang; Jian Peng; Yueyue Du; Xianfeng Liu; Shuangshuang Li; Donghai Zhang. 2015. "Correlations between Urbanization and Vegetation Degradation across the World’s Metropolises Using DMSP/OLS Nighttime Light Data." Remote Sensing 7, no. 2: 2067-2088.
At landscape scale, the normalized difference vegetation index (NDVI) can be used to indicate the vegetation’s dynamic characteristics and has been widely employed to develop correlated and dependent relationships with the climatic and environmental factors. However, studies show that NDVI-environment relationships always emerge with complex features such as nonlinearity, scale dependency, and nonstationarity, especially in highly heterogeneous areas. In this study, we used geographically weighted regression (GWR), a local modeling technique to estimate regression models with spatially varying relationships, to investigate the spatially nonstationary relationships between NDVI and climatic factors at multiple scales in North China. The results indicate that all GWR models with appropriate bandwidth represented significant improvements of model performance over the ordinary least squares (OLS) models. The spatial relationships between NDVI and climatic factors varied significantly over space and were more significant and sensitive in the ecogeographical transition zone. Clear spatial patterns of slope parameters and local coefficient of determination (R 2) were found from the results of the GWR models. Moreover, the spatial patterns of the local R 2 of NDVI-precipitation are much clearer than the R 2 of NDVI-temperature in the semi-arid and subhumid areas, which mean that precipitation has more significant influence on vegetation in these areas. In conclusion, the study revealed detailed site information on the variable relationships in different parts of the study area, especially in the ecogeographical transition zone, and the GWR model can improve model ability to address spatial, nonstationary, and scale-dependent problems in landscape ecology.
Zhiqiang Zhao; Jiangbo Gao; Yanglin Wang; Jianguo Liu; Shuangcheng Li. Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression. Theoretical and Applied Climatology 2014, 120, 507 -519.
AMA StyleZhiqiang Zhao, Jiangbo Gao, Yanglin Wang, Jianguo Liu, Shuangcheng Li. Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression. Theoretical and Applied Climatology. 2014; 120 (3-4):507-519.
Chicago/Turabian StyleZhiqiang Zhao; Jiangbo Gao; Yanglin Wang; Jianguo Liu; Shuangcheng Li. 2014. "Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression." Theoretical and Applied Climatology 120, no. 3-4: 507-519.
In this paper, we proposed a framework for evaluating the performance of ecosystem strategies prepared for enhancing vulnerability reduction in the face of hazards due to climate change. The framework highlights the positive effects of human activities in the coupled human and natural system (CHANS) by introducing adaptive capacity as an evaluation criterion. A built-in regional vulnerability to a certain hazard was generated based upon interaction of three dimensions of vulnerability: exposure, sensitivity and adaptive capacity. We illustrated the application of this framework in the temperate farming-grazing transitional zone in the middle Inner Mongolia of the northern China, where drought hazard is the key threat to the CHANS. Specific indices were produced to translate such climate variance and social-economic differences into specific indicators. The results showed that the most exposed regions are the inner land areas, while counties located in the eastern part are potentially the most adaptive ones. Ordos City and Bayannur City are most frequently influenced by multiple climate variances, showing highest sensitivity. Analysis also indicated that differences in the ability to adapt to changes are the main causes of spatial differences. After depiction of the spatial differentiations and analysis of the reasons, climate zones were divided to depict the differences in facing to the drought threats. The climate zones were shown to be similar to vulnerability zones based on the quantitative structure of indexes drafted by a triangular map. Further analysis of the composition of the vulnerability index showed that the evaluation criteria were effective in validating the spatial differentiation but potentially ineffective because of their limited time scope. This research will be a demonstration of how to combine the three dimensions by quantitative methods and will thus provide a guide for government to vulnerability reduction management.
Xiaoqian Liu; Yanglin Wang; Jian Peng; Ademola K. Braimoh; He Yin. Assessing vulnerability to drought based on exposure, sensitivity and adaptive capacity: A case study in middle Inner Mongolia of China. Chinese Geographical Science 2012, 23, 13 -25.
AMA StyleXiaoqian Liu, Yanglin Wang, Jian Peng, Ademola K. Braimoh, He Yin. Assessing vulnerability to drought based on exposure, sensitivity and adaptive capacity: A case study in middle Inner Mongolia of China. Chinese Geographical Science. 2012; 23 (1):13-25.
Chicago/Turabian StyleXiaoqian Liu; Yanglin Wang; Jian Peng; Ademola K. Braimoh; He Yin. 2012. "Assessing vulnerability to drought based on exposure, sensitivity and adaptive capacity: A case study in middle Inner Mongolia of China." Chinese Geographical Science 23, no. 1: 13-25.
Vegetation coverage and surface temperature are important parameters in describing the characteristics of land cover, which in combination can provide information on vegetation and soil moisture conditions at the surface. This paper aims to estimate spatial and temporal patterns of soil moisture in the Loess Plateau, China. Using Terra/MODIS images for each 10-day period in 2004 covering the semi-arid North Shaanxi Loess Plateau, a simplified land surface dryness index (Temperature–Vegetation Dryness Index, TVDI) developed by Sandholt [Sandholt, I., Rasmussena, K, Andersenb, J., 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment 79, 213–224.] was used to determine the relationship between surface temperature and vegetation index. From the analysis, it can be inferred that the trend in seasonal change of TVDI is high values in the dry season (spring or summer) and low values in the rainy season (autumn or winter). Moreover, the land surface moisture of each watershed had its seasonal characteristics. The relationship between TVDI and land cover types indicated that water-retention in forest and shrub areas was better than cropland and rangeland in relatively wet conditions, and rangeland was better than forest and shrub areas in dry conditions.
Zhengguo Li; Yanglin Wang; Qingbo Zhou; Jiansheng Wu; Jian Peng; Hsiaofei Chang. Spatiotemporal variability of land surface moisture based on vegetation and temperature characteristics in Northern Shaanxi Loess Plateau, China. Journal of Arid Environments 2008, 72, 974 -985.
AMA StyleZhengguo Li, Yanglin Wang, Qingbo Zhou, Jiansheng Wu, Jian Peng, Hsiaofei Chang. Spatiotemporal variability of land surface moisture based on vegetation and temperature characteristics in Northern Shaanxi Loess Plateau, China. Journal of Arid Environments. 2008; 72 (6):974-985.
Chicago/Turabian StyleZhengguo Li; Yanglin Wang; Qingbo Zhou; Jiansheng Wu; Jian Peng; Hsiaofei Chang. 2008. "Spatiotemporal variability of land surface moisture based on vegetation and temperature characteristics in Northern Shaanxi Loess Plateau, China." Journal of Arid Environments 72, no. 6: 974-985.
Using remotely sensed data, landscape pattern analysis based on landscape metrics has been one of the major topics of landscape ecology, and more attention has been focused on the effects of spatial scale and the accuracy of remotely sensed data on landscape metrics. However, few studies have been conducted to assess the change of landscape metrics under the influence of land‐use categorization. In this paper, we took the Bao'an district of Shenzhen city as the study area, to analyse how land‐use categorization would influence changes in 24 landscape metrics. The results showed a significant influence, and based on the characteristics of the response curves of landscape metrics associated with the change in land‐use categorization in regression analysis, and the predictability of these relations, the 24 landscape metrics fell into three groups. (1) Type I included 12 landscape metrics, and showed a strong predictability with changing of land‐use categorization with simple function relations in regression analysis. (2) Type II included seven indices, and exhibited complicated behaviours against changing of land‐use categorization. The response curves of these metrics, which were not easy to predict, consisted of two subsections and could not be described by a single function. (3) Type III included five indices, and showed unpredictable behaviours against the change of the land‐use categorization. Their response curves could not be described by a certain function. This study highlights the need for the analysis of effects of land‐use categorization on landscape metrics so as to clearly quantify landscape patterns, and provides insights into the selection of landscape metrics for comparative research on a given area under different land‐use categorizations.
J. Peng; Y. Wang; M. Ye; J. Wu; Y. Zhang. Effects of land‐use categorization on landscape metrics: A case study in urban landscape of Shenzhen, China. International Journal of Remote Sensing 2007, 28, 4877 -4895.
AMA StyleJ. Peng, Y. Wang, M. Ye, J. Wu, Y. Zhang. Effects of land‐use categorization on landscape metrics: A case study in urban landscape of Shenzhen, China. International Journal of Remote Sensing. 2007; 28 (21):4877-4895.
Chicago/Turabian StyleJ. Peng; Y. Wang; M. Ye; J. Wu; Y. Zhang. 2007. "Effects of land‐use categorization on landscape metrics: A case study in urban landscape of Shenzhen, China." International Journal of Remote Sensing 28, no. 21: 4877-4895.
As an important component of sustainable development in mountain areas, evaluation for sustainable land use is always one of the hotpots of researches on sustainable development. Traditional evaluation for sustainable land use mainly focuses on the sustainability of land use model and biological production on temporal scale, and overlooks the effects of land use patterns on the sustainability, while landscape ecology can be a good help to realize the spatial analysis of sustainable land use. In this study, a synthetic evaluation indexes system for sustainable land use was constructed through the application of landscape metrics. Taking Yongsheng County of Yunnan Province, China as a case study, a series of quantitative evaluation were conducted in 1996, 1999 and 2001, to monitor the temporal dynamics of regional land use sustainability. Two indicators, contributing amount of indexes, and obstacle amount of indexes, were also set up to ascertain the significance of all the evaluation indexes to the evaluation results. The results showed that, in the study phases, the land use sustainability of the whole county had been low with a stable but great spatial difference, and great changes took place in regional land use system in 1999 with the deviation from the aim of sustainable land use. It also showed that, the most important indexes contributing for the land use sustainability in the study period, were the indexes of population density and land use degree, followed by the index of landscape diversity and cropping index. And the most important indexes counteracting the land use sustainability were the indexes of per unit area total production value of industry and agriculture, per unit area yield of cereal crops, landscape fragmentation, followed by the indexes of per unit area yield of economic crops and fertilizer consume per unit area.
Jian Peng; Yanglin Wang; Jiansheng Wu; Qing Chang; Yuan Zhang. RETRACTED ARTICLE: Evaluation for sustainable land use in mountain areas of Northwestern Yunnan Province, China. Environmental Monitoring and Assessment 2007, 133, 407 -415.
AMA StyleJian Peng, Yanglin Wang, Jiansheng Wu, Qing Chang, Yuan Zhang. RETRACTED ARTICLE: Evaluation for sustainable land use in mountain areas of Northwestern Yunnan Province, China. Environmental Monitoring and Assessment. 2007; 133 (1-3):407-415.
Chicago/Turabian StyleJian Peng; Yanglin Wang; Jiansheng Wu; Qing Chang; Yuan Zhang. 2007. "RETRACTED ARTICLE: Evaluation for sustainable land use in mountain areas of Northwestern Yunnan Province, China." Environmental Monitoring and Assessment 133, no. 1-3: 407-415.