Search

Under the stress of the global change, rocky desertification has become a serious environmental problem in the Karst Mountain area. At present, few remote sensing monitoring research works on rocky desertification based on feature space model have been conducted and reported. In this study, the Albedo-LST feature space remote sensing monitoring index based on point-point model has been proposed, and subsequently the spatio–temporal evolution pattern and driving mechanism of rocky desertification in Dafang district from 1986 to 2019 were analyzed. The results show that: (1) The point-point Albedo-LST feature space model of rocky desertification has good applicability with the overall accuracy of 90.79%; (2) From 1986 to 2019, the rocky desertification in Dafang district first showed an increasing trend (1986–2005) and then a decreasing trend (2005–2019); (3) The comprehensive evolution frequency of rocky desertification during 2001–2005 was the largest with 7.51% a−1, which was related to the implementation of the Grain for Green Project; (4) The single factor with the largest contribution rates to rocky desertification are land use type, landform, and temperature. The interactive factors with the largest explanatory power are temperature ∩ land use type and landform ∩ land use type. The research results can provide decision support for the prevention and control of rocky desertification in Southwest China.

Since the Ming Dynasty, the flood disaster has been one of the important natural disasters affecting our country. However, relatively few studies on the evolution law of flood disaster at large spatial–temporal scales have been reported. Based on the datasets of flood stations from 1470 to 2000, this paper quantitatively analyzes and discusses the spatial and temporal evolution pattrens of floods in China in the past 500 years by using regional gravity center model, wavelet analysis, Daniel index, and M–K test, and predicts the trend of flood disasters in the future. The results show that: (1) Since 1470, there has been an increasing trend of flood intensity index in China;(2) From 1470 to 2000, there were two high centers of flood disaster in 1560 and 1925, respectively, and one low center appeared in 1745; (3) Flood risk changes from easy occurrence in the eastern coastal area to uniform spatial distribution over the whole study region; (4) In the past 500 years, the graver center of flood disaster is located in Baokang County, Hubei Province, and the gravity center of flood disaster shows a trend of moving to northwest both at time scales of 50-year and 100 year; (5) The flood disaster in the studied area will still show an overall upward trend in the future.The research can provide decision support for the precise prevention and control of flood disasters in China.

What the dominant influencing factors are and how changes have occurred over different historical periods in Yellow River Basin are unclear. This study aims to retrieve the annual soil erosion modulus of the Yellow River Basin and utilizes the LMDI model and Geodetector model to identify the dominant driving factors of soil erosion evolution in different historical periods and regions at a pixel scale and landscape scale. Results showed that: (1) The serious (intensive, extreme intensive, and severe) erosion areas were mainly distributed in the Loess Plateau, while the slight and mild erosion areas were mainly distributed in the Hetao Plain, Ordos Plateau, and Guanzhong Plain; (2) During 1981-2019a, the gravity center of soil erosion moved from southwest to northeast, which indicated that the increment and increasing rate of soil erosion in the northeast of the Yellow River Basin were higher than that of the southwestern areas; (3) Increased soil erosion due to the R factor (ISER) was the most widely distributed, accounting for 79.21% of the area where soil erosion increased, while decreased soil erosion due to the C factor and R factor was the most widely distributed, accounting for 49.15% of the area where soil erosion decreased; (4) Before 2000a, precipitation, vegetation, and soil types were the dominant factors affecting the evolution pattern of soil erosion. Through the efforts of human intervention, such as returning farmland to forest or grassland, the explanatory power of land use change in the evolution pattern of soil erosion has increased.

The Qinghai–Tibet plateau (QTP), as the “roof of the world” and the “Asian Water Tower”, provides important ecological resources for China and other Asian countries. The changing trend of ecological assets and their dominant influencing factors in different sub-regions and periods are not yet clear. In order to reveal the differences in driving mechanisms among sub-regions under the context of global changes, this study quantitatively analyzed the ecological assets and their spatial and temporal evolution patterns during 2000–2015 by using the value equivalent method. Then, the Geodetector was introduced to reveal and clarify the dominant factors of ecological asset changes in different ecological sub-regions. The results show the following. (1) From 2000 to 2010, the total value of ecological assets in Nakchu County was the highest, followed by Kangding County, while that in 2015 was the highest in Kangding County, followed by Nakchu County. (2) During 2000–2015, the average value of ecological assets of the Qinghai–Tibet plateau gradually decreased from east to west, while the average ecological asset value in the southern Qinghai–Tibet plateau was lower. (3) The QTP showed the highest value in 2005 with an increasing trend from 2000 to 2005, followed by a subsequent decrease from 2005 to 2015. (4) Between 2000 and 2015, the area of the stable zone (slight or no change) of ecological assets was the largest, followed by that of the decreasing zone. (5) During all the study period, the spatio-temporal evolution of ecological assets in different ecological sub-regions was mainly affected by natural factors, which were the main driving variables rather than human activities. These results could provide important support for decisions regarding the protection of ecosystems and resources in the Qinghai–Tibet plateau.

The Hengduan mountain area, located in the upper reaches of the Yangtze River of China, is an important ecological barrier that significantly impacts the climate and ecological environment of the surrounding region and western China as a whole. This paper introduces the gravity center model used to analyze the spatial-temporal variation patterns of vegetation Net Primary Productivity (NPP) from 2000 to 2015, which were determined by the use of MOD17A3 NPP products. Additionally, the dominant driving factors of the spatial—temporal changes of vegetation NPP of the Hengduan Mountain area were quantitatively determined with a geographical detector over 2000–2015. The results revealed that: (1) From 2000 to 2015, there was an increasing trend of vegetation NPP in the Hengduan mountain area. Throughout the whole study region, the vegetation NPP with a mean value of 611.37 gC·m−2·a−1 indicated a decreasing trend from southeast to northwest in terms of spatial distribution. (2) The gravity centers of vegetation NPP in 2000–2015 were mainly concentrated in Zhongdian County. During the study period, the gravity center of vegetation NPP moved northward, which indicated that the increment and increasing rate of vegetation NPP in the northern parts were greater than that of the southern areas. (3) The vegetation NPP showed a moderately positive correlation with temperature, accumulated temperature (>10°C), and sunshine, while there was an overall negative relationship between NPP and precipitation. (4) The dominant factors and interactive dominant factors changed in different sub-regions over different segments of the study period. The dominant factors of most sub-regions in Hengduan mountain were natural factors, and the climate change factors played an increasingly greater role over the 16 years of the study period.

This study optimized the slope and slope length factor (LS) and crop management factor (P) of the RUSLE model and then introduced the gravity centre model to analyze the spatial–temporal variation patterns of soil erosion in Yellow River Basin from a new perspective. Results showed that: (1) The improved model of RUSLE with optimized factors of LS and P had better applicability in Yellow River Basin; (2) The average erosion intensity was 2777.5 t/a, which belonged to moderate erosion. The soil erosion intensity of the Yellow River Basin showed an overall trend of increasing firstly (1990–2005) and then decreasing (2005–2015). (3) During 1990–2015, the gravity centre of soil erosion moved to the southwest, indicating that the increment and increasing rate of soil erosion in the southwest parts of the Yellow River Basin were greater than that in the northeast parts. (4) The intensity of soil erosion aggravated with the increasing slope. The sandy soil, chestnut soil, light-grey calcium soil and fluvo aquic soil had severe erosion intensity due to the regional climate and their own physical–chemical structure. The woodland and shrubbery land were more susceptible to soil erosion.

Naiman Banner is one of the most typical semi‐arid vulnerable ecological zones that is characterized by vegetation degradation and severe desertification. Previous studies mostly utilized a single feature space or only the linear correlation model to monitor desertification. In this study, the optimal monitoring model that fully considers the multiple feature spaces and the nonlinear relationship between surface parameters in Naiman Banner was determined, and then the spatial–temporal evolution patterns of the desertification from 1989 to 2017 were analyzed. The results showed that: (a) The albedo–normalized difference vegetation index point‐to‐point model was applicable to areas with high‐vegetation coverage, whereas the albedo–modified soil‐adjusted vegetation index (MSAVI) linear model had better applicability in areas with relatively low vegetation coverage. Considering the surface cover, the desertification monitoring index based on albedo‐MSAVI linear model had the best applicability. (b) During 1989–2017, the total desertification area percentage of Naiman County decreased from 90.85% in 1989 to 63.35% in 2017, showing a significant downward trend. (c) During the past 30 years, the desertification process was significantly influenced by human activities, such as ‘Three‐North’ Shelterbelt, Returning Farmland to Forestry, and Returning Farmland to Grassland (d) The desertification levels of unused land and grassland with low coverage was relatively high, whereas the desertification degree of cultivated and forest lands was low. These results can be used as an effective reference for the remote sensing monitoring and evaluation of desertification and can provide data and decision support for the ecological restoration of the region.

The current desertification feature space models are almost linear and ignore the complicated and nonlinear relations that exist among variables when monitoring desertification. Herein, point-to-point and point-to-line models have been proposed by completely considering the nonlinear relations between the Albedo-Modified Soil Adjusted Vegetation Index (MSAVI) and the effects of soil background. Further, the applicability of these models for monitoring different levels of desertification information was compared and analyzed. The point-to-line feature space model exhibited a larger inversion accuracy (93.8%) for Naiman Banner with respect to albedo-MSAVI when compared with that exhibited by the point-to-point model (88.9%). In addition, the monitoring accuracy is observed to slightly differ for different levels of desertification, and slight and mild desertification exhibit the best inversion accuracy in case of both point-to-point and point-to-line models. Furthermore, the point-to-line model exhibits better applicability in case of intensive (92.7%) and severe (93.3%) desertification when compared with those exhibited by the point-to-point model (87.5% and 88.9%, respectively). The results obtained in this study can provide improved data and decision support for preventing and managing land degradation.

Based on the datasets of MODIS NDVI and SPOT VETGETATION NDVI from 1999 to 2018, this study has introduced the gravity centre model and time-lag analysis model to explore the spatial–temporal change patterns of the NDVI in China in different sub-regions and its response to climate factors. Results showed that: (1) In 1999–2018, there was an increasing trend in vegetation coverage in China on the whole, with a larger increase occurring in central south and northeast China. (2) The NDVIs of six land use types showed a similar change trend with temperature and precipitation with different time lags. (3) The maximum correlation coefficients between the NDVIs of different land types and climate factors appeared at Lag 0–1 (current month and previous one month) and Lag 0–2 (current month, previous one and two month), while the correlation coefficients were the lowest at Lag 3 (previous three month). (4) During the past twenty years, the gravity centres of the NDVI, temperature and precipitation moved northward as a whole with similar migration trajectories. These results could provide important decision supports for vegetation restorations and ecological protections in different sub-regions of China.

The Yellow River Delta, with the most typical new wetland system in warm temperate zone of China, is suffering from increasingly serious salinization. The purpose of this study is to utilize five typical surface parameters, including Albedo (the surface Albedo), NDVI (vegetation index), SI (salinity index),WI (humidity index), and (Iron oxide index), to construct 10 different feature spaces and, then, propose two different kinds of monitoring models (point-to-point model and point to line model) of soil salinization. The results showed that the inversion accuracy of the feature space detection index based on the point-to-point model was the highest with R2=0.86. However, the inversion accuracy of Albedo-NDVI feature space detection index based on the point-to-point model is the lowest with R2=0.72. This is due to the fact that NDVI is not sensitive enough to indicate the status of vegetation grown in the region with low (disturbance of soil background) and high (influenced by the saturation effect) vegetation coverage. The chemical weathering is also a primary cause of soil salinization, during which Fe2O3 is formed by the reaction of oxygen present in the atmosphere with primary Fe2+ minerals in the soil .Therefore, the feature space detection index based on the point-to-point model has a stronger applicability to monitor the information of soil salinization in the Yellow River Delta. This above point-to-point detection model can be utilized as a better approach to provide data and decision support for the development of agriculture, construction of reservoirs, and protection of natural ecological system in the Yellow River Delta.

The vegetation ecosystem of the Qinghai-Tibet Plateau in China, considered to be the “natural laboratory” of climate change in the world, has undergone profound changes under the stress of global change. Herein, we analyzed and discussed the spatial-temporal change patterns and the driving mechanisms of net primary productivity (NPP) in the Qinghai-Tibet Plateau from 2000 to 2015 based on the gravity center and correlation coefficient models. Subsequently, we quantitatively distinguished the relative effects of climate change (such as precipitation, temperature and evapotranspiration) and human activities (such as grazing and ecological construction) on the NPP changes using scenario analysis and Miami model based on the MOD17A3 and meteorological data. The average annual NPP in the Qinghai-Tibet Plateau showed a decreasing trend from the southeast to the northwest during 2000–2015. With respect to the inter-annual changes, the average annual NPP exhibited a fluctuating upward trend from 2000 to 2015, with a steep increase observed in 2005 and a high fluctuation observed from 2005 to 2015. In the Qinghai-Tibet Plateau, the regions with the increase in NPP (change rate higher than 10%) were mainly concentrated in the Three-River Source Region, the northern Hengduan Mountains, the middle and lower reaches of the Yarlung Zangbo River, and the eastern parts of the North Tibet Plateau, whereas the regions with the decrease in NPP (change rate lower than −10%) were mainly concentrated in the upper reaches of the Yarlung Zangbo River and the Ali Plateau. The gravity center of NPP in the Qinghai-Tibet Plateau has moved southwestward during 2000–2015, indicating that the increment and growth rate of NPP in the southwestern part is greater than those of NPP in the northeastern part. Further, a significant correlation was observed between NPP and climate factors in the Qinghai-Tibet Plateau. The regions exhibiting a significant correlation between NPP and precipitation were mainly located in the central and eastern Qinghai-Tibet Plateau, and the regions exhibiting a significant correlation between NPP and temperature were mainly located in the southern and eastern Qinghai-Tibet Plateau. Furthermore, the relative effects of climate change and human activities on the NPP changes in the Qinghai-Tibet Plateau exhibited significant spatial differences in three types of zones, i.e., the climate change-dominant zone, the human activity-dominant zone, and the climate change and human activity interaction zone. These research results can provide theoretical and methodological supports to reveal the driving mechanisms of the regional ecosystems to the global change in the Qinghai-Tibet Plateau.

Drought features prominently among natural disasters in Chinese history. Analysing the laws that govern the spatial and temporal evolution of drought could support decision-making for drought prevention and control. However, few studies have investigated the long-term evolution rules of drought events on different spatial and temporal scales. In this paper, the spatio-temporal change patterns of drought in China were analysed with geostatistical methods based on the 1470–2000a drought datasets in China, and then the possible future drought trend was predicted. Results showed that (1) the drought risk in the northern region was the highest during the past 500 years. And the drought intensity index showed an overall increasing trend with a detail pattern of weakening->strengthening-> weakening-> strengthening; (2) the drought condition in the north was severe than that in the south, but the drought trend in the south was significantly aggravated. (3) The drought gravity centres were mainly distributed in the north, but they showed a tendency to move southward. (4) From 1470 to 2000, the study area showed a significant drought enhancement, which was predicted to show an increasing trend of drought after 2000.

Drought is a prominent disaster in Chinese history. Analysing the spatial and temporal evolution laws of drought could provide decision supports for drought prevention and control. However, fewer studies were applied to investigate the long-term evolution rules of drought events on different spatial and temporal scales. In this paper, the spatio-temporal changes patterns of drought in China were analysed with geostatistical methods based on the 1470–2000a drought datasets in China, and then the possible future drought trend was predicted. Results showed that :(1) The drought risk in the northern region was the highest during the past 500 years. And, the drought intensity index showed an overall increasing trend with detail pattern of weakening->strengthening-> weakening-> strengthening; (2) The drought condition in the north was server than that in the south, but the drought trend in the south was significantly aggravated. (3) The drought gravity centres were mainly distributed in the north, but it showed a tendency to move southward. (4) From 1470 to 2000, the study area showed a significant drought enhancements, which was predicted to show an increasing trend of drought after 2000.

Under the stress of global climate change, soil wind erosion has become a major environmental issue in the Three-River Source Region (TRSR) of China. However, few large-scale studies have been conducted on soil wind erosion owing to the lack of investigational data or complex parameters. Moreover, the uncertainty and randomness in the weight determination process cannot be avoided using the traditional method. Thus, a cloud-analytic hierarchy process (cloud-AHP) model was proposed to construct a wind erosion intensity index model for the TRSR based on seven typical land surface parameters. The following results were obtained. (1) The cloud-AHP model can better eliminate the randomness and uncertainty in the weight determination process. (2) The proposed evaluation method of wind erosion intensity has better applicability in the TRSR with overall accuracy of 93%. (3) The overall wind erosion intensity in this region is moderate. The wind erosion intensity was the largest in the Yangtze River (0.55, moderate erosion) and smallest in the source region of the Lancang River (0.50, mild erosion). (4) Significant differences are observed in the influences of various vegetation types on wind erosion intensity. Bare land exhibits the highest wind erosion intensity, whereas a coniferous forest exhibits the smallest. Moreover, grassland is a key control zone of soil and water conservation because it has the largest spatial heterogeneity of internal erosion intensity. These results can provide data and technical support for preventing and controlling soil erosion and protecting the environment in the region.

There were dramatic changes in the ecological vulnerability (EV) of the China-Pakistan Economic Corridor (CPEC) region due to the effects of climate change and human activity. Obtaining field observation and statistical data for the evaluation of the EV of the CPEC region is difficult due to its transnational status. This study proposes a novel remote sensing ecological vulnerability index (RSEVI) based on the Moderate Resolution Imaging Spectroradiometer (MODIS), STRM3, DMSP-OLS, and NPP-VIIRS products. The RSEVI index was applied to the CPEC region to investigate the spatiotemporal changes in EV and the influencing factors during 2000–2019. The results showed that: (1) the large-scale application of the RSEVI index showed good applicability to the CPEC region, with a precision of 89.75%; (2) the average RSEVI value for the CPEC region was 0.83, thereby falling into a category of “intensive vulnerability”; (3) a stable trend in RSEVI was observed for the entire CPEC region during the study period, except for the Indus Basin where there was a significant change; (4) the increasing rate of the RSEVI in the northeastern parts exceeded that of other parts during 2000–2019; (5) precipitation, temperature, and vegetation coverage showed negative relationships with RSEVI, whereas RSEVI showed a positive relationship with slope. The RSEVI results indicated that ice and bare land were the most vulnerable, whereas cropland was the least vulnerable; (6) there were differences in the dominant factor and dominant interactive factor among different sub-regions. The results of this study could provide important decision support for the protection of the ecological environment and for economic development.

Under the background of global climate change, the natural environment of the southwest karst mountain region is undergoing profound changes. Considering the differences in the relative importance of each index in the vulnerability evaluation system in different periods, this study presents the dynamic weight determination method and then establishes the dynamic weight table of the ecological vulnerability evaluation index in the southwest karst mountain region. At the same time, net primary productivity (NPP) is introduced to help determine the ecological vulnerability classification threshold in different periods, and then the spatial–temporal change pattern and driving mechanism of ecological vulnerability in the past 15 years are analyzed. Results show the following: (1) Dynamic weight determination method has good application in the study of ecological vulnerability in long-term series, and the threshold of vulnerability classification based on NPP can ensure the comparability of ecological vulnerability evaluation results in different periods; (2) From 2000 to 2015, the southwest karst mountain region generally belonged to the moderate vulnerability category, and a decreasing trend is observed from the Sichuan–Yunnan–Guizhou border area to its surrounding region in a spatial distribution pattern; (3) In the past 15 years, the ecological vulnerability of the southwest karst mountain region increased first and then decreased; (4) The spatial–temporal change pattern of ecological vulnerability in the southwest karst mountain region is significantly affected by GDP of different industries, precipitation, soil erosion, rocky desertification. These results would be in particular useful for planning the conservation strategies or assessments of the conservation priorities. In addition, the method employed could provide a technical support and references for the ecological assessment and restoration of other similar karst mountain ecosystem zones in China or worldwide.

With the development of Unmanned Aerial Vehicles (UAVs) remote sensing technique, some researchers began to apply the UAV imagery to investigate Yellow River's dike hazards. There has been an urgent need for establishing automatic and accurate registration techniques of UAV remote sensing imagery. High-precision image registration will enhance the reliability of the investigation. As the Yellow River is very wide, it is very difficult to find enough Ground Control Points (GCP) and the limited GCPs are usually distributed unevenly. Therefore, the traditional point-based image registration approach cannot satisfy the requirements of imagery registration. However, the river basin contains linear features richly. This paper proposes a line-based registration approach for UAV remote sensing imagery of the wide-spanning river basin. This method regards the segment straight line as the registration primitives. After detect the primitives of input images, we estimate the parameters of transformation model between reference-image and registering-image with Modified Iterated Hough Transform (MIHT). Experiments using the UAV imagery of Ning-Meng section of Yellow River conclude that this is a robust method for registration for UAV remote sensing imagery of wide-spanning river basin.