Abstract
China is a large country by area. In situ monitoring of the environment cannot meet the demand of the society. For large areas, remote sensing is the only viable technology for environmental monitoring of the entire area. Although satellite observation capabilities as well as remotely sensed data acquired on board of satellites from both within and outside China are widely available, research is rare that targets the entire territory of China for environmental monitoring. In this paper, the process of environmental change has been categorized into changes in driving forces, environmental change, materials transport and transformation, concentration and abundance change, exposure and infection change of human and ecosystems, and impacts. The potential in monitoring changes in these various aspects is assessed. The progress of environmental change monitoring over the entire territory of China is reviewed. It is suggested that at the methodological level, remote sensing should not only be applied to observation and experiments as well as understanding the change mechanism, but also be coupled with environmental simulation and forecasting so as to support environmental policy making. At the application level, remote sensing should be used beyond its traditional application fields to include species diversity, biological invasion, public health, air and water quality monitoring. Finally, at the technical level, systematic research should be devoted to the improvement of operational and automatic use of remotely sensed data.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Leemans R, Asrar G, Busalacchi A, et al. Developing a common strategy for integrative global environmental change research and outreach: The Earth System Science Partnership (ESSP) Strategy Paper. Curr Opin Env Sust, 2009, 1: 4–13
Ci L J. Desertification of extra-arid desert (in Chinese). Chin Sci Bull (Chin Ver), 2011, 56: 2616–2626
Cracknell A P, Varotsos C A. New aspects of global climate- dynamics research and remote sensing. Int J Remote Sens, 2011, 32: 579–600
Li J F, Wang M H, Ho Y S. Trends in research on global climate change: A science citation index expanded-based analysis. Glob Planet Change, 2011, 77: 13–20
FAO. Global Forest Land-Use Change from 1990 to 2005. 2011
FAO. State of the World’s Forests, Rome, Italy, 2011. 179
Nemani R R, Keeling C D, Hashimoto H, et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 2003, 300: 1560–1563
Behrenfeld M J, O’Malley R T, Siegel D A, et al. Climate-driven trends in contemporary ocean productivity. Nature, 2006, 444: 752–755
Rodell M, Velicogna I, Famiglietti J S. Satellite-based estimates of groundwater depletion in India. Nature, 2009, 460: 999–U80
Gong P, Miao X, Ge S K, et al. Water table level in relation to EO-1 ALI and Landsat ETM+ data over a mountainous meadow in California. Can J Remote Sens, 2004, 32: 691–696
Turner B L, Lambin E F, Reenberg A. The emergence of land change science for global environmental change and sustainability. Proc Natl Acad Sci USA, 2007, 102: 20666–20671
Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens, 2007, 28: 823–870
Tucker C J, Townshend J R G, Goff T E. African land-cover classification using satellite data. Science, 1985, 227: 369–375
Pu R L, Li Z Q, Gong P, et al. Development and analysis of a 12-year daily 1-km forest fire data across the North America from NOAA/AVHRR data. Remote Sens Environ, 2007, 108: 198–208
Clinton N, Gong P, Jin Z Y, et al. Meta-prediction of Bromus tectorum invasion in Central Utah, U.S.A. Photogramm Eng Remote Sens, 2009, 75: 689–701
Herold M, Mayaux P, Woodcock C E, et al. Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sens Environ, 2008, 112: 2538–2556
Gong P, Sheng Y W, Biging G S. 3D model-based tree measurement from high resolution aerial imagery. Photogramm Eng Remote Sens, 2002, 68: 1203–1212
Chen Q, Gong P, Baldocchi D, et al. Filtering airborne laser scanning data with morphological methods. Photogr Eng Remote Sens, 2007, 73: 175–185
Sun G Q, Ranson K J, Guo Z, et al. Forest biomass mapping from lidar and radar synergies. Remote Sens Environ, 2011, 115: 2906–2916
Lo C P, Welch R. Chinese urban-population estimates. Ann Assoc A, 1977, 67: 246–253
Hansen M C, DeFries R, Townshend J. Towards an operational MODIS continuous field of percent tree cover algorithm: Examples using AVHRR and MODIS data. Remote Sens Environ, 2002, 83: 303–319
Gong P, Pu R L, Miller J R. Coniferous forest leaf area index estimation along a transect in Oregon using Compact Airborne Spectrographic Imager data. Photogr Eng Rem S, 1995, 61: 1107–1117
Michishita R, Gong P, Xu B. Spectral mixture analysis for bi-sensor wetland mapping using Landsat TM and Terra MODIS data. Int J Remote Sens, 2012, 33: 3373–3401
Li X W, Gao F, Wang J D. A priori knowledge accumulation and its application to linear BRDF model inversion. J Geophys Res, 2001, 106: 11925–11935
Liang S L, Strahler A H. An analytic brdf model of canopy radiative-transfer and its inversion. IEEE T Geosci Remote, 1993, 31: 1081–1092
Shi J C, Jackson T, Tao J, et al. Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E. Remote Sens Environ, 2008, 112: 4285–4300
Liang S L. Quantitative Remote Sensing of Land Surfaces. New York: John Wiley and Sons, Inc, 2004. 534
Qin J, Liang S L, Liu R G, et al. A weak constraint based data assimilation for estimating surface turbulent fluxes. IEEE Geosci Remote Sens, 2007, 4: 649–653
Lu D S, Mausel P, Brondizio E, et al. Change detection techniques. Int J Remote Sens, 2004, 25: 2365–2407
Liu D S, Cai S S. A spatial-temporal modeling approach to reconstructing land-cover change trajectories from multi-temporal satellite imagery. Ann Assoc A, 2011, doi: 10.1080/00045608.2011.596357
Liu J Y, Liu M L, Tian H Q, et al. Spatial and temporal patterns of China’s cropland during 1990–2000: An analysis based on Landsat TM data. Remote Sens Environ, 2005, 98: 442–456
Liu J Y, Deng X Z. Progress of the research methodologies on the temporal and spatial process of LUCC. Chin Sci Bull, 2010, 55: 1354–1362
Liu J Y, Zhan J Y, Deng X Z. The Spatio-temporal patterns and driving forces of urban land expansion in China during the economic reform era. AMBIO, 2005, 34: 450–455
Piao S L, Fang J Y, Ciais P. The carbon balance of terrestrial ecosystems in China. Nature, 2009, 458: 1009–u82
Feng X, Liu G, Chen J M, et al. Net primary productivity of China’s terrestrial ecosystems from a process model driven by remote sensing. J Environ Manage, 2007, 85: 563–573
Bai Z G, Dent D. Recent land degradation and improvement in China. AMBIO, 2009, 38: 150–156
Gong P, Niu Z G, Cheng X, et al. China’s wetland change (1990–2000) determined by remote sensing. Sci China Earth Sci, 2010, 53: 1036–1042
Sun J L. Dynamic monitoring and yield estimation of crops by mainly using the remote sensing technique in China. Photogramm Eng Remote Sens, 2010, 66: 645–650
Xiao X M, Liu J Y, Zhuang D F, et al. Uncertainties in estimates of cropland area in China: A comparison between an AVHRR-derived dataset and a Landsat TM-derived dataset. Glob Planet Change, 2003, 37: 297–306
Zhuang D F, Liu M L, Deng X Z. Spatialization model of population based on dataset of land use and land cover change in China. Chin Geogr Sci, 2002, 12: 114–119
Ma R H, Duan H T, Hu C M, et al. A half-century of changes in China’s lakes: Global warming or human influence? Geophys Res Lett, 2010, 37: L24106
Shang S L, Lee Z P, Wei G M. Characterization of MODIS-derived euphotic zone depth: Results for the China Sea. Remote Sens Environ, 2011, 115: 180–186
Fernandez J E. Resource consumption of new urban construction in China. J Ind Ecol, 2007, 11: 99–115
Yang X C, Hou Y L, Chen B D. Observed surface warming induced by urbanization in east China. J Geophys Res, 2011, 116: D14113
Zhao Y, McElroy M B, Xing J, et al. Multiple effects and uncertainties of emission control policies in China: Implications for public health, soil acidification, and global temperature. Sci Total Environ, 2011, 409: 5177–5187
Zhang F Y, Wang W Y, Lv J M. Time-series studies on air pollution and daily outpatient visits for allergic rhinitis in Beijing, China. Sci Total Environ, 2011, 409: 2486–2492
Zhang J, Pu L J, Peng B Z, et al. The impact of urban land expansion on soil quality in rapidly urbanizing regions in China: Kunshan as a case study. Environ Geochem Health, 2011, 33: 125–135
Siciliano G. Urbanization strategies, rural development and land use changes in China: A multiple-level integrated assessment. Land Use Policy, 2012, 29: 165–178
Wang L, Li C C, Ying Q, et al. China’s urban expansion from 1990 to 2010 determined with satellite remote sensing. Chin Sci Bull, 2012, 57: 2802–2812
Niu Z G, Zhang H Y, Wang X W, et al. Mapping wetland changes in China between 1978 and 2008. Chin Sci Bull, 2012, 57: 2813–2823
Zheng Y M, Zhang H Y, Niu Z G, et al. Protection efficacy of national wetland reserves in China. Chin Sci Bull, 2012, 57: 207–230
Yu C Q, Gong P, Yin Y Y. China’s water crisis needs more than words. Nature, 2011, 470: 307
Lu H, Shi J C. Reconstruction and analysis of temporal and spatial variations in surface soil moisture in China using remote sensing. Chin Sci Bull, 2012, 57: 2824–2834
Liu S, Gong P. Change of surface cover greenness in China between 2000 and 2010. Chin Sci Bull, 2012, 57: 2835–2845
Gong P, Pu R L, Biging G S, et al. Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE T Geosci Remote, 2003, 41: 1355–1362
Deng F, Chen J M, Plummer S, et al. Algorithm for global leaf area index retrieval using satellite imagery. IEEE T Geosci Remote, 2006, 44: 2219–2229
Zhu G L, Ju W M, Chen J M, et al. Foliage clumping index over China’s landmass retrieved from the MODIS BRDF parameters product. IEEE T Geosci Remote, 2011, doi:10.11091TGRS.2011.2172213
Liu Y B, Ju W M, Chen J M, et al. Spatial and temporal variations of forest LAI over China during 2010–2010. Chin Sci Bull, 2012, 57: 2846–2856
Wang Z F, Chen L F, Tao J H, et al. Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method. Remote Sens Environ, 2010, 114: 50–63
Cheng T H, Gu X F, Xie D H, et al. Simultaneous retrieval of aerosol optical properties over the Pearl River Delta, China using multi-angular, multi-spectral, and polarized measurements. Remote Sens Environ, 2011, 115: 1643–1652
Wang X L, Mannaerts C M, Yang S T, et al. Evaluation of soil nitrogen emissions from riparian zones coupling simple process-oriented models with remote sensing data. Sci Total Environ, 2010, 408: 3310–3318
Li Z Q, Chen H, Cribb M, et al. Preface to special section on east Asian studies of tropospheric aerosols: An international regional experiment (EAST-AIRE). J Geophys Res, 2007, 112: D22S00
Wang S W, Streets D G, Zhang Q, et al. Satellite detection and model verification of NO(x) emissions from power plants in Northern China. Environ Res Lett, 2010, 5: 044007
Wang Y, Zhang Y, Hao J, et al. Seasonal and spatial variability of surface ozone over China: Contributions from background and domestic pollution. Atmos Chem Phys, 2011, 11: 3511–3525
Zhang Q, He K B, Geng G N, et al. Satellite remote sensing of changes in NOx emissions over China during 1996–2010. Chin Sci Bull, 2012, 57: 2857–2864
Shen L L, Wang Y X. Changes in tropospheric ozone levels over the Three Representative Regions of China observed from space by Tropospheric Emission Spectrometer (TES), 2005–2010. Chin Sci Bull, 2012, 57: 2865–2871
Shi L, Zhao S Q, Tang Z Y, et al. The changes in China’s forests: An analysis using the forest identity. PLoS One, 2011, 6: e20778
Xu G H, Gong P, Shao L Q, et al, Four prioritized research area of global change research that need to be strengthened in China (in Chinese). Review of Global Change Research, First Vol, Beijing: Higher Education Press, 2010. 1–11
Xu G H, Ju H B, He B, et al. 21st century Chinese earth science development: based on China, towards the world (in Chinese). Sci Technol Daily, 2010-8-1
Behrenfeld M J. Abandoning Sverdrup’s critical depth hypothesis on phytoplankton blooms. Ecology, 2010, 91: 977–989
Myneni R B, Keeling C D, Tucker C J, et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 1997, 386: 698–702
Samanta A, Costa M H, Nunes E L, et al. Comment on “drought-induced reduction in global terrestrial net primary production from 2000 through 2009”. Science, 2011, 333: 1093
Liang L, Xu B, Chen Y L, et al. Combining spatial-temporal and phylogenetic analysis approaches for improved understanding on global H5N1 transmission. PLoS One, 2010, 5: e13575
White M A, de Beurs K M, Didan K, et al. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Glob Change Biol, 2009, 15: 2335–2359
Garrity S R, Bohrer G, Maurer K D, et al. A comparison of multiple phenology data sources for estimating seasonal transitions in deciduous forest carbon exchange. Agr Forest Meteorol, 2011, 151: 1741–1752
Jones M O, Jones L A, Kimball J S, et al. Satellite passive microwave remote sensing for monitoring global land surface phenology. Remote Sens Environ, 2011, 115: 1102–1114
Zhao M S, Running S W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 2010, 329: 940–943
Sellers P J, Meeson B W, Hall F G, et al. Remote-sensing of the land-surface for studies of global change—models, algorithms, experiments. Remote Sens Environ, 1995, 51: 3–26
Piao S L, Ciais P, Friedlingstein P, et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 2008, 451: 49–U3
Fang L Q, de Vlas S J, Liang S, et al. Environmental factors contributing to the spread of avian influenza in mainland China. PLoS One, 2008, 3: e2268
Spear R C, Seto E Y W, Carlton E J, et al. The challenge of effective surveillance in moving from low transmission to elimination of schistosomiasis in China. Int J Parasitol, 2011, 41: 1243–1247
Zhou X N, Yang G J, Yang K, et al. Potential impact of climate change on schistosomiasis transmission in China. Am J Trop Med Hyg, 2008, 78: 188–194
Yang G J, Gao Q, Zhou S S, et al. Mapping and predicting malaria transmission in the People’s Republic of China, using integrated biology-driven and statistical models. Geospatial Health, 2010, 5: 11–22
Zhang J F, Mauzerall D L, Zhu T, et al. Environmental health in China: Progress towards clean air and safe water. Lancet, 2010, 375: 1110–1119
Gong P, Liang S L, Carlton E, et al. Urbanization and health in China. Lancet, 2012, 379: 843–852
Smith K R, McCracken J P, Weber M W, et al. Effect of reduction in household air pollution on childhood pneumonia in Guatemala (RESPIRE): A randomised controlled trial. Lancet, 2011, 378: 1717–1726
Fan M S, Shen J B, Yuan L X, et al. Improving crop productivity and resource use efficiency to ensure food security and environmental quality in China. J Environ Bot, 2012, 63: 13–24
Lv Y H, Fu B J, Wei W. Major ecosystems in China, dynamics and challenges for sustainable management. Environ Manage, 2011, 48: 13–27
Scherler D, Bookhagen B, Strecker M R. Spatially variable response of Himalayan glaciers to climate change affected by debris cover. Nat Geosci, 2011, 4: 156–159
Author information
Authors and Affiliations
Corresponding author
Additional information
This article is published with open access at Springerlink.com
Rights and permissions
This article is published under an open access license. Please check the 'Copyright Information' section either on this page or in the PDF for details of this license and what re-use is permitted. If your intended use exceeds what is permitted by the license or if you are unable to locate the licence and re-use information, please contact the Rights and Permissions team.
About this article
Cite this article
Gong, P. Remote sensing of environmental change over China: A review. Chin. Sci. Bull. 57, 2793–2801 (2012). https://doi.org/10.1007/s11434-012-5268-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11434-012-5268-y