Ye H.-C.,CAS Institute of Remote Sensing |
Ye H.-C.,Hainan Key Laboratory of Earth Observation |
Huang Y.-F.,China Agricultural University |
Chen P.-F.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research |
And 5 more authors.
Journal of Integrative Agriculture | Year: 2016
Understanding the effects of land use changes on the spatiotemporal variation of soil organic carbon (SOC) can provide guidance for low carbon and sustainable agriculture. In this paper, based on the large-scale datasets of soil surveys in 1982 and 2009 for Pinggu District - an urban-rural ecotone of Beijing, China, the effects of land use and land use changes on both temporal variation and spatial variation of SOC were analyzed. Results showed that from 1982 to 2009 in Pinggu District, the following land use change mainly occurred: Grain cropland converted to orchard or vegetable land, and grassland converted to forestland. The SOC content decreased in region where the land use type changed to grain cropland (e.g., vegetable land to grain cropland decreased by 0.7 g kg-1; orchard to grain cropland decreased by 0.2 g kg-1). In contrast, the SOC content increased in region where the land use type changed to either orchard (excluding forestland) or forestland (e.g., grain cropland to orchard and forestland increased by 2.7 and 2.4 g kg-1, respectively; grassland to orchard and forestland increased by 4.8 and 4.9 g kg-1, respectively). The organic carbon accumulation capacity per unit mass of the soil increased in the following order: grain cropland soil
Zhang B.,CAS Institute of Remote Sensing |
Zhang B.,Hainan Key Laboratory of Earth Observation |
Zhang B.,University of Chinese Academy of Sciences |
Zhang L.,CAS Institute of Remote Sensing |
And 7 more authors.
Remote Sensing | Year: 2016
Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and temporal adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. This provided observations at fine temporal (8-d) and medium spatial (30 m) resolutions. Based on field-sampled aboveground biomass (AGB), synthetic NDVI and support vector machine (SVM) techniques were integrated to develop an AGB estimation model (SVM-AGB) for Xilinhot in Inner Mongolia, China. Compared with model generated from MODIS-NDVI (R2 = 0.73, root-mean-square error (RMSE) = 30.61 g/m2), the SVM-AGB model we developed can not only ensure the accuracy of estimation (R2 = 0.77, RMSE = 17.22 g/m2), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management. © 2015 by the authors; licensee MDPI, Basel, Switzerland.
Guo H.-D.,CAS Institute of Remote Sensing |
Guo H.-D.,Hainan Key Laboratory of Earth Observation |
Zhang L.,CAS Institute of Remote Sensing |
Zhang L.,Hainan Key Laboratory of Earth Observation |
And 2 more authors.
Advances in Climate Change Research | Year: 2015
Earth observation technology has provided highly useful information in global climate change research over the past few decades and greatly promoted its development, especially through providing biological, physical, and chemical parameters on a global scale. Earth observation data has the 4V features (volume, variety, veracity, and velocity) of big data that are suitable for climate change research. Moreover, the large amount of data available from scientific satellites plays an important role. This study reviews the advances of climate change studies based on Earth observation big data and provides examples of case studies that utilize Earth observation big data in climate change research, such as synchronous satellite-aerial-ground observation experiments, which provide extremely large and abundant datasets; Earth observational sensitive factors (e.g., glaciers, lakes, vegetation, radiation, and urbanization); and global environmental change information and simulation systems. With the era of global environment change dawning, Earth observation big data will underpin the Future Earth program with a huge volume of various types of data and will play an important role in academia and decisionmaking. Inevitably, Earth observation big data will encounter opportunities and challenges brought about by global climate change. © 2015 National Climate Center (China Meteorological Administration). Production and hosting by Elsevier B.V.