Is land degradation worsening in Northern China? Quantitative evidence and enlightenment from satellites

Land degradation has become an urgent environmental issue globally. The complexity of land degradation hinders its quantification and monitoring, which is needed to realize the sustainable development of land resources. This study constructed a comprehensive index—land degradation status index (LDSI), which integrates in fractional vegetation cover (FVC), net primary productivity (NPP), albedo, and modified temperature vegetation drought index (MTVDI) based on spatial principal component analysis (SPCA). Land degradation was then identified by the dynamics of land degradation status during 2001–2018. This study investigated the spatio‐temporal process and driving mechanism of land degradation in Northern China. The result indicates that: (1) LDSI had a better monitoring performance compared with normalized difference vegetation index (NDVI), (2) although some degraded land had been effectively rehabilitated (17.11%), a highly clustered spatial distribution of land degradation status remained, challenging to break, (3) localized land degradation had expanded (1.063%), mainly in semiarid (0.481%), dry sub‐humid (0.289%), and humid (0.187%) regions, and (4) differences in climate, environmental backgrounds, and human activities were driven land degradation status and process. This study also assessed the effectiveness of ecological projects implemented by the Chinese government. The in‐depth understanding of the change regularity and influencing mechanism in land degradation status and process can provide a scientific basis for formulating ecological policies based on local conditions.

[1]  G. Zhao,et al.  Fuzzy Logic Modeling of Land Degradation in a Loess Plateau Watershed, China , 2022, Remote. Sens..

[2]  Zhongyi Sun,et al.  Attribution analysis of land degradation in Hainan Island based on geographical detector , 2022, Ecological Indicators.

[3]  Liangzhi Li,et al.  Spatiotemporal evolution of desertification based on integrated remote sensing indices in Duolun County, Inner Mongolia , 2022, Ecol. Informatics.

[4]  Yunyan Du,et al.  The spatiotemporal response of China's vegetation greenness to human socio-economic activities. , 2021, Journal of environmental management.

[5]  A. Bao,et al.  Monitoring land degradation and assessing its drivers to support sustainable development goal 15.3 in Central Asia. , 2021, The Science of the total environment.

[6]  Ye Liu,et al.  Analyzing ecological environment change and associated driving factors in China based on NDVI time series data , 2021 .

[7]  J. Lei,et al.  Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020 , 2021 .

[8]  R. Prăvălie.  Exploring the multiple land degradation pathways across the planet , 2021 .

[9]  Xi-sheng Hu,et al.  A New Remote Sensing Index for Assessing the Spatial Heterogeneity in Urban Eco-Environmental Quality Associated Road Network , 2021, Land.

[10]  A. Bagherzadeh,et al.  Spatio-temporal variation characteristics of NDVI and its response to climate on the Loess Plateau from 1985 to 2015 , 2021, CATENA.

[11]  A. Darvishi Boloorani,et al.  Land degradation modeling of dust storm sources using MODIS and meteorological time series data , 2021, Journal of Arid Environments.

[12]  Kaiyong Wang,et al.  Spatial-temporal patterns and influencing factors of ecological land degradation-restoration in Guangdong-Hong Kong-Macao Greater Bay Area. , 2021, The Science of the total environment.

[13]  Yi Luo,et al.  Impact of urban expansion on vegetation: The case of China (2000-2018). , 2021, Journal of environmental management.

[14]  Jianping Wang,et al.  Driving factors of desertification in Qaidam Basin, China: An 18-year analysis using the geographic detector model , 2021 .

[15]  Qingwu Hu,et al.  Spatio-Temporal Changes of Vegetation Net Primary Productivity and Its Driving Factors on the Qinghai-Tibetan Plateau from 2001 to 2017 , 2021, Remote. Sens..

[16]  J. Tomasella,et al.  Land degradation mapping in the MATOPIBA region (Brazil) using remote sensing data and decision-tree analysis , 2021 .

[17]  Yue Zhang,et al.  Quantifying the influences of natural and human factors on the water footprint of afforestation in desert regions of northern China. , 2021, The Science of the total environment.

[18]  Hao Li,et al.  Understanding global land degradation processes interacted with complex biophysics and socioeconomics from the perspective of the Normalized Difference Vegetation Index (1982–2015) , 2021 .

[19]  I. Săvulescu,et al.  Arable lands under the pressure of multiple land degradation processes. A global perspective. , 2021, Environmental research.

[20]  Liang Zhou,et al.  Spatiotemporal changes of land desertification sensitivity in northwest China from 2000 to 2017 , 2021, Journal of Geographical Sciences.

[21]  S. Liang,et al.  The Global Land Surface Satellite (GLASS) Product Suite , 2020, Bulletin of the American Meteorological Society.

[22]  Yaonan Zhang,et al.  Land Degradation and Development Processes and Their Response to Climate Change and Human Activity in China from 1982 to 2015 , 2021, Remote. Sens..

[23]  Fei Xie,et al.  Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary? , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[24]  Yanjun Shen,et al.  Revegetation projects significantly improved ecosystem service values in the agro-pastoral ecotone of northern China in recent 20 years. , 2021, The Science of the total environment.

[25]  L. Poggio,et al.  The advantages and limitations of global datasets to assess carbon stocks as proxy for land degradation in an Ethiopian case study , 2021 .

[26]  T. Lei,et al.  Effects of natural and anthropogenic factors and their interactions on dust events in Northern China , 2021 .

[27]  Qiang Liu,et al.  Estimation of Land Surface Albedo from MODIS and VIIRS Data: A Multi-Sensor Strategy Based on the Direct Estimation Algorithm and Statistical-Based Temporal Filter , 2020, Remote. Sens..

[28]  M. Schaepman,et al.  Changes in grassland cover and in its spatial heterogeneity indicate degradation on the Qinghai-Tibetan Plateau , 2020, Ecological Indicators.

[29]  Liang Zhou,et al.  Temperature Vegetation Precipitation Dryness Index (TVPDI)-based dryness-wetness monitoring in China , 2020 .

[30]  Yunzhi Zhang,et al.  Land desertification and its influencing factors in Kazakhstan , 2020 .

[31]  Jun Wang,et al.  Unfolding the effectiveness of ecological restoration programs in combating land degradation: Achievements, causes, and implications. , 2020, The Science of the total environment.

[32]  M. Hoffman,et al.  Application of Landsat-derived vegetation trends over South Africa: Potential for monitoring land degradation and restoration , 2020, Ecological Indicators.

[33]  Paolo Mazzetti,et al.  Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[34]  Qingquan Li,et al.  Spatiotemporal characteristics of land degradation in the Fuxian Lake Basin, China: Past and future , 2020, Land Degradation & Development.

[35]  Flavio Borfecchia,et al.  Remote sensing for monitoring and mapping Land Productivity in Italy: A rapid assessment methodology , 2020 .

[36]  Sinkyu Kang,et al.  Assessing the degree of land degradation and rehabilitation in the Northeast Asia dryland region using net primary productivity and water use efficiency , 2020, Land Degradation & Development.

[37]  Qi Liu,et al.  Monitoring drought using composite drought indices based on remote sensing. , 2020, The Science of the total environment.

[38]  Fei Yang,et al.  Dynamic monitoring of desertification in Naiman Banner based on feature space models with typical surface parameters derived from LANDSAT images , 2020, Land Degradation & Development.

[39]  Qun'ou Jiang,et al.  Analysis of critical land degradation and development processes and their driving mechanism in the Heihe River Basin. , 2020, The Science of the total environment.

[40]  Liang Zhou,et al.  Regional-scale assessment of environmental vulnerability in an arid inland basin , 2020 .

[41]  Liang Zhou,et al.  Assessing environmental interference in northern China using a spatial distance model: From the perspective of geographic detection. , 2019, The Science of the total environment.

[42]  Nanshan You,et al.  Spatial pattern and temporal trend of land degradation in the Heihe River Basin of China using local net primary production scaling , 2019, Land Degradation & Development.

[43]  Stephen D. Prince,et al.  Challenges for remote sensing of the Sustainable Development Goal SDG 15.3.1 productivity indicator , 2019 .

[44]  A. Bao,et al.  Assessing land degradation and quantifying its drivers in the Amudarya River delta , 2019 .

[45]  O. Bruzzone,et al.  Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring , 2019 .

[46]  Graciela Metternicht,et al.  Taking Land Degradation Neutrality from concept to practice: Early reflections on LDN target setting and planning , 2019, Environmental Science & Policy.

[47]  Vladimir R. Wingate,et al.  Mapping precipitation-corrected NDVI trends across Namibia. , 2019, The Science of the total environment.

[48]  D. Chu.  Fractional vegetation cover , 2019, Advanced Remote Sensing.

[49]  S. Liang,et al.  Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017 , 2019, Earth System Science Data.

[50]  Wade T. Crow,et al.  Evaluating the Operational Application of SMAP for Global Agricultural Drought Monitoring , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[51]  Lan Yao,et al.  Human-induced changes of surface albedo in Northern China from 1992-2012 , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[52]  Batunacun,et al.  Identifying drivers of land degradation in Xilingol, China, between 1975 and 2015 , 2019, Land Use Policy.

[53]  T. McVicar,et al.  Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017 , 2019, Remote Sensing of Environment.

[54]  C. Yan,et al.  Dynamic monitoring of aeolian desertification based on multiple indicators in Horqin Sandy Land, China. , 2019, The Science of the total environment.

[55]  Duanyang Xu,et al.  Assessing the spatial-temporal pattern and evolution of areas sensitive to land desertification in North China , 2019, Ecological Indicators.

[56]  V. Brovkin,et al.  China and India lead in greening of the world through land-use management , 2019, Nature Sustainability.

[57]  Hanspeter Liniger,et al.  Land degradation assessment in the Argentinean Puna: Comparing expert knowledge with satellite-derived information , 2019, Environmental Science & Policy.

[58]  V. Masson‐Delmotte,et al.  Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems , 2019 .

[59]  Ge Li,et al.  Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau , 2018, Remote. Sens..

[60]  H. Balzter,et al.  Land degradation continues despite greening in the Nigeria-Niger border region , 2018, Global Ecology and Conservation.

[61]  Bingfang Wu,et al.  Land degradation and restoration in the arid and semiarid zones of China: Quantified evidence and implications from satellites , 2018, Land Degradation & Development.

[62]  A. Belal,et al.  Land Degradation , 2018, World Soils Book Series.

[63]  Tsegaye Tadesse,et al.  Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in Northeastern Brazil , 2018, Remote Sensing of Environment.

[64]  N. Crossman,et al.  China’s response to a national land-system sustainability emergency , 2018, Nature.

[65]  Qingsheng Liu,et al.  Soil moisture variations at different topographic domains and land use types in the semi-arid Loess Plateau, China , 2018, CATENA.

[66]  Xisheng Hu,et al.  A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: A case from Fuzhou City, China , 2018, Ecological Indicators.

[67]  Reiji Kimura,et al.  Global distribution of degraded land area based on dust erodibility determined from satellite data , 2018 .

[68]  K. Yoshino,et al.  Environmental vulnerability evaluation using a spatial principal components approach in the Daxing’anling region, China , 2017 .

[69]  Qiang Guo,et al.  Satellite Monitoring the Spatial-Temporal Dynamics of Desertification in Response to Climate Change and Human Activities across the Ordos Plateau, China , 2017, Remote. Sens..

[70]  Hongbo Shao,et al.  Albedo indicating land degradation around the Badain Jaran Desert for better land resources utilization. , 2017, The Science of the total environment.

[71]  F. Liu,et al.  Semi-natural areas of Tarim Basin in northwest China: Linkage to desertification. , 2016, The Science of the total environment.

[72]  Tonglin Zhang,et al.  A measure of spatial stratified heterogeneity , 2016 .

[73]  Chunyang He,et al.  Are the drylands in northern China sustainable? A perspective from ecological footprint dynamics from 1990 to 2010. , 2016, The Science of the total environment.

[74]  C. Peng,et al.  Multiple afforestation programs accelerate the greenness in the 'Three North' region of China from 1982 to 2013 , 2016 .

[75]  Zhouyuan Li,et al.  Ecological restoration and its effects on a regional climate: the source region of the Yellow River, China. , 2015, Environmental science & technology.

[76]  Lihua Yuan,et al.  Spatio-temporal analysis of vegetation variation in the Yellow River Basin , 2015 .

[77]  Wei Zhou,et al.  Quantitative assessment of the individual contribution of climate and human factors to desertification in northwest China using net primary productivity as an indicator , 2015 .

[78]  Duanyang Xu,et al.  The dynamics of desertification in the farming-pastoral region of North China over the past 10 years and their relationship to climate change and human activity , 2014 .

[79]  Elias Symeonakis,et al.  Remote Sensing Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions , 2022 .

[80]  Feng Zhang,et al.  Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data , 2014 .

[81]  A. Kerkhoff,et al.  Convergence of terrestrial plant production across global climate gradients , 2014, Nature.

[82]  Philippe Ciais,et al.  Rain-Use-Efficiency: What it Tells us about the Conflicting Sahel Greening and Sahelian Paradox , 2014, Remote. Sens..

[83]  Xavier Ceamanos,et al.  Dynamic mapping of snow-free vegetation and bare soil albedos at global 1km scale from 10-year analysis of MODIS satellite products , 2014 .

[84]  Shaofeng Jia,et al.  Water conservancy projects in China: Achievements, challenges and way forward , 2013 .

[85]  Quan Sun,et al.  ractional vegetation cover estimation in arid and semi-arid environments using J-1 satellite hyperspectral data , 2012 .

[86]  F. van den Bergh,et al.  Limits to detectability of land degradation by trend analysis of vegetation index data , 2012 .

[87]  Tao Chen,et al.  Temporal-spatial variability of desertification in an agro-pastoral transitional zone of northern Shaanxi Province, China , 2012 .

[88]  Shixiong Cao,et al.  Excessive reliance on afforestation in China's arid and semi-arid regions: Lessons in ecological restoration , 2011 .

[89]  Delphis F. Levia,et al.  Soil moisture: A central and unifying theme in physical geography , 2011 .

[90]  K. Davis,et al.  Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data , 2010 .

[91]  Wei Guo,et al.  Real‐time weekly global green vegetation fraction derived from advanced very high resolution radiometer‐based NOAA operational global vegetation index (GVI) system , 2010 .

[92]  Xiaotong Zhang,et al.  Review on Estimation of Land Surface Radiation and Energy Budgets From Ground Measurement, Remote Sensing and Model Simulations , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[93]  T. McVicar,et al.  Parsimoniously modelling perennial vegetation suitability and identifying priority areas to support China's re-vegetation program in the Loess Plateau: Matching model complexity to data availability , 2010 .

[94]  Xiaoying Zheng,et al.  Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China , 2010, Int. J. Geogr. Inf. Sci..

[95]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[96]  Christian Töttrup,et al.  Regional desertification: A global synthesis , 2008 .

[97]  J. Puigdefabregas,et al.  Monitoring land degradation risk using ASTER data: The non-evaporative fraction as an indicator of ecosystem function , 2008 .

[98]  F. Pugnaire,et al.  Rooting depth and soil moisture control Mediterranean woody seedling survival during drought , 2007 .

[99]  Li Baoguo,et al.  Agricultural causes of desertification risk in Minqin, China. , 2006, Journal of environmental management.

[100]  Nadine Gobron,et al.  Coupling diffuse sky radiation and surface albedo , 2005 .

[101]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[102]  I. Sandholt,et al.  A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .

[103]  P. Sen.  Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[104]  H. Theil.  A Rank-Invariant Method of Linear and Polynomial Regression Analysis , 1992 .

[105]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[106]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .