Key Laboratory of Resources Remote Sensing and Digital Agriculture
Key Laboratory of Resources Remote Sensing and Digital Agriculture
Shen M.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
Shen M.,Beijing Normal University |
Chen J.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
Chen J.,Beijing Normal University |
And 3 more authors.
International Journal of Remote Sensing | Year: 2010
The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are vegetation indices widely used in remote sensing of above-ground biomass. Because both indexes are based on spectral features of plant canopy, NDVI and EVI may suffer reduced accuracy in estimating above-ground biomass when flower signals are mixed in the plant canopy. This paper addresses how flowers influence the estimation of above-ground biomass using NDVI and EVI for an alpine meadow with mixed yellow flowers of Halerpestes tricuspis (Ranunculaceae). Field spectral measurements were used in combination with simulated reflectance spectra with precisely controlled flower coverage by applying a linear spectral mixture model. Using the reflectance spectrum for the in-situ canopy with H. tricuspis flowers, we found no significant correlation between above-ground biomass and EVI (p = 0.17) or between above-ground biomass and NDVI (p=0.78). However, both NDVI and EVI showed very good prediction of above-ground biomass with low root mean square errors (RMSE = 43 gm-2 for NDVI and RMSE = 43 g m-2 for EVI, both p< 0.01) when all the flowers were removed from the canopies. Simulation analysis based on the in-situ measurements further indicated that high variation in flower coverage among different quadrats could produce more noise in the relationship between above-ground biomass and NDVI, or EVI, which results in an evident decline in the accuracy of above-ground biomass estimation. Therefore, the study suggests that attention should be paid both to the flower fraction and the heterogeneity of flower distribution in the above-ground biomass estimation via NDVI and EVI. © 2010 Taylor & Francis.
Yin H.,Peking University |
Li Z.,Peking University |
Li Z.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
Wang Y.,Peking University |
Cai F.,Institute of Atmospheric Environment
Acta Geographica Sinica | Year: 2011
Desertification is one of the serious threats to the environment in arid and semi-arid northern China. In order to understand inter-annual vegetation dynamics, vegetation indicators have been widely used in desertification assessment. In this study, rain use efficiency (RUE) derived from hyper-temporal remote sensing images has been used for desertification assessment. Based on time-series analysis, this study focused on how the desertification developed in Inner Mongolia and how the desertification reversed in the extremely arid environment. Results showed that during the past 11 years, there was no significant desertification development in Inner Mongolia. Parts of area showed a significant increase trend of RUE, especially in the eastern part of Ordos Plateau and southern Daqing Mountain, as well as the region from the Greater Hinggan Mountains to northern Yanshan Mountains. It is indicated that the ecological conditions in these areas have tended to be much better than before. The reason may be that the vegetation protection policies adopted in northern China have exerted a positive effect on the local environment. The results also showed that there was a significant relationship between rainfall and vegetation restoration, areas with more precipitation tend to be more easily restored, especially in the areas with more 300 mm precipitation. In addition, the research on desertification reversion showed that the desert edge region in western Inner Mongolia have changed intensively, and desertification reverse assessment needs to be further examined.
Suramaythangkoor T.,King Mongkut's University of Technology Thonburi |
Li Z.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
Li Z.,Chinese Academy of Agricultural Sciences
Renewable and Sustainable Energy Reviews | Year: 2012
Cane trash could viably substitute fossil fuels in heat and power generation projects to avoid air pollution from open burning and reduce greenhouse gas (GHG) emission. It is competitive with bituminous and other agro-industrial biomass. Using cane trash for heat generation project could provide a higher reliability and return on investment than power generation project. The heat generation project could be viable (Financial Internal Rate of Return, FIRR = 36-81%) without feedstock subsidy. With current investment and support conditions, the capacity of 5 MW option of power generation project is the most viable (FIRR = 13.6-15.3%); but 30 MW, 1 MW and 10 MW options require feedstock subsidy 450-1100 Baht/t-cane trash to strengthen financial viability. Furthermore, the revenue from carbon credit sales could compensate the revenue from current energy price adder and increases 0.5-1.0% FIRR of power generation project. Using cane trash for 1 MW power generation could reduce GHG emission 637-861 t CO 2eq and avoid air pollutant emissions of 3.35 kg nitrogen oxides (NO x), 0.41 kg sulfur oxides (SO x) and 2.05 kg volatile organic compounds (VOC). Also, 1 t steam generation from cane trash could avoid pollutant emissions of 0.6 kg NO x, 0.07 kg SO x, and 0.37 kg VOC. The potential of cane trash to cause fouling/slagging as well as erosion are not significantly different from other biomass, but chlorinated organic compounds and NO x could be higher than bituminous and current biomass feedstock at sugar mill (bagasse and rice husk). © 2012 Elsevier Ltd. All right reserved.
Dong J.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research |
Dong J.,University of Chinese Academy of Sciences |
Dong J.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
Tao F.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research |
And 2 more authors.
Environmental Earth Sciences | Year: 2011
Extensive studies have investigated the relationships between climate change and vegetation dynamics. However, the geographic controls on vegetation dynamics are rarely studied. In this study, the geographic controls on the trends and variation of vegetation greenness in middle and eastern Inner Mongolia, China (mid-eastern Inner Mongolia) were investigated. The SPOT VEGETATION 10-day period synthesis archive of normalized difference vegetation index (NDVI) from 1999 to 2007 was used for this study. First, the maximum value compositing (MVC) method was applied to derive monthly maximum NDVI (MNDVI), and then yearly mean NDVI (YMNDVI) was calculated by averaging the MNDVIs. The greenness rate of change (GRC) and the coefficient of variation (CV) were used to monitor the trends and variation in YMNDVI at each raster grid for different vegetation types, which were determined from a land use dataset at a scale of 1:100,000, interpreted from Landsat TM images in 2000. The possible effects of geographic factors including elevation, slope and aspect on GRC and CV for three main vegetation types (cropland, forest and steppe) were analyzed. The results indicate that the average NDVI values during the 9-year study period for steppe, forest and cropland were 0.26, 0.41 and 0.32, respectively; while the GRC was 0.008, 0.042 and 0.033 per decade, respectively; and CVs were 10.2, 4.8 and 7.1%, respectively. Cropland and steppe shared a similar trend in NDVI variation, with both decreasing initially and then increasing over the study period. The forest YMNDVI increased throughout the study period. The GRCs of the forest also increased, although GRCs for cropland and steppe decreased with increasing elevation. The GRCs of cropland and steppe increased with increasing slope, but the forest GRCs were not as closely related to slope. All three vegetation types exhibited the same effects in that the GRC was larger on north-facing (shady) slopes than south-facing slopes due to differences in water conditions. The CVs of the three vegetation types showed different features to the GRC. The CVs for all three vegetation types were not affected by aspect. The CVs for forest and cropland showed minor effects with changes in elevation and slope, but the CV for steppe decreased with increasing slope, and increased with increasing elevations to 1,200 m, before decreasing at higher elevations. Our findings suggest that the role of geographic factors in controlling GRC should also be considered alongside climate factors. © 2010 Springer-Verlag.
Zhang G.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research |
Zhang G.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
Dong J.,University of Oklahoma |
Xiao X.,University of Oklahoma |
And 3 more authors.
Ecological Engineering | Year: 2012
Horqin Sandy Land is a major source of sandstorms in Northern China, especially the Beijing-Tianjin-Tangshan Region. A series of ecological restoration projects including the 'Grain for Green Project', the 'Beijing and Tianjin Sandstorm Source Controlling Project', and the 'Three-North Shelterbelt Project' were implemented in this region. This study assesses the effectiveness of ecological restoration projects within Tongliao City, the main body of Horqin Sandy Land. The different treatment effects of various sand dunes were assessed and compared based on Normalized Difference Vegetation Index (NDVI) from SPOT VEGETATION Ten Daily Synthesis Archive from 1999 to 2007 and the desert distribution map of China in 2000. The results showed that: (1) the fixed and semi-fixed sand dunes were the main sand dune types, which accounted for 70% of the entire sand dune area in 2000; followed by shifting sand dunes and the semi-shifting sand dunes. (2) The ecological restoration projects resulted in improvements of different sand dune types, the improved area covered 76% of the sand dune area, mainly in the southern parts of the study area. The vegetation cover of the sand dunes in Naiman Banner, Hure Banner and the south of Horqin Left Back Banner increased significantly. While mild improvement occurred in the central sand dunes of the study area. (3) The area with degraded vegetation accounted for approximately 10% of sand dune area, mainly located in the southeast of Jarud Banner and the west of Horqin Left Middle Banner. Most of these areas showed mild and insignificant degradation except for a small area of moderate degradation. (4) The types of sand dunes in degraded status were mainly the fixed and semi-fixed sand dunes, followed by the semi-shifting sand dunes and saline-alkali land. The lower the dune fixity (e.g. shifting or semi-shifting versus semi-fixed or fixed) and the more likely to contribute to sand-storms, the greater the effectiveness of restoration projects. Finally, some implications for the sustainable development of the ecological restoration projects are discussed. © 2011 Elsevier B.V.
Hu Z.M.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research |
Hu Z.M.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
Li S.G.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research |
Dong J.W.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
And 2 more authors.
Rangeland Journal | Year: 2012
The spatial annual patterns of aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of the rangelands of the Inner Mongolia Autonomous Region of China, a region in which several projects for ecosystem restoration had been implemented, are described for the years 1998-2007. Remotely sensed normalised difference vegetation index and ANPP data, measured in situ, were integrated to allow the prediction of ANPP and PUE in each 1km 2 of the 12 prefectures of Inner Mongolia. Furthermore, the temporal dynamics of PUE and ANPP residuals, as indicators of ecosystem deterioration and recovery, were investigated for the region and each prefecture. In general, both ANPP and PUE were positively correlated with mean annual precipitation, i.e. ANPP and PUE were higher in wet regions than in arid regions. Both PUE and ANPP residuals indicated that the state of the rangelands of the region were generally improving during the period of 2000-05, but declined by 2007 to that found in 1999. Among the four main grassland-dominated prefectures, the recovery in the state of the grasslands in the Erdos and Chifeng prefectures was highest, and Xilin Gol and Chifeng prefectures was 2 years earlier than Erdos and Hunlu Buir prefectures. The study demonstrated that the use of PUE or ANPP residuals has some limitations and it is proposed that both indices should be used together with relatively long-term datasets in order to maximise the reliability of the assessments. © Australian Rangeland Society 2012.
Liu X.-R.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research |
Dong Y.-S.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research |
Ren J.-Q.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
Ren J.-Q.,Chinese Academy of Agricultural Sciences |
Li S.-G.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research
Nutrient Cycling in Agroecosystems | Year: 2010
Soil net nitrogen mineralization (NNM) of four grasslands across the elevation and precipitation gradients was studied in situ in the upper 0-10 cm soil layer using the resin-core technique in Xilin River basin, Inner Mongolia, China during the growing season of 2006. The primary objectives were to examine variations of NNM among grassland types and the main influencing factors. These grasslands included Stipa baicalensis (SB), Aneulolepidum Chinense (AC), Stipa grandis (SG), and Stipa krylovii (SK) grassland. The results showed that the seasonal variation patterns of NNM were similar among the four grasslands, the rates of NNM and nitrification were highest from June to August, and lowest in September and October during the growing season. The rates of NNM and nitrification were affected significantly by the incubation time, and they were positively correlated with soil organic carbon content, total soil nitrogen (TN) content, soil temperature, and soil water content, but the rates of NNM and nitrification were negatively correlated with available N, and weakly correlated with soil pH and C:N ratio. The sequences of the daily mean rates of NNM and nitrification in the four grasslands during the growing season were AC > SG > SB > SK, and TN content maybe the main affecting factors which can be attributed to the land use type. © 2009 Springer Science+Business Media B.V.
Kaneko D.,Monitor Inc. |
Yang P.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
Kumakura T.,Nagaoka University of Technology
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2011
The forth IPCC report pointed out grain production as one of the highest vulnerability by which climate change is expected to impart the most severe effects. The recent concern about food scarcity motivates the development of the present system named Remote Sensing Environmental Monitor (RSEM) for crop yield monitoring. The authors have developed a photosynthesis model for rice production to address such issues. The system includes a photosynthetic sterility yield model using meteorological re-analysis data and precise land use and cover (LULC) classification of crop field in Asian countries. The validation concept is based on the fact that the carbon hydrate in grains has the same chemical formula as cellulose in grain vegetation. The both photosynthesis and sterility models are validated by carbon partitioning method associated with particular rice species in Japan and China. © 2011 IEEE.
Jiang Z.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
Jiang Z.,Chinese Academy of Agricultural Sciences |
Chen Z.,Key Laboratory of Resources Remote Sensing and Digital Agriculture |
Chen Z.,Chinese Academy of Agricultural Sciences |
And 4 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2011
Crop growth models have been applied extensively in the regional crop yield prediction and estimation. It is very important to select the most sensitive model parameters for the model optimization and better model output. The Extend Fourier Amplitude Sensitivity Test (EFAST) was used to analyze the sensitivity of CERES-Wheat model parameters in a study region in Luoyang, Henan province. The sensitivity of crop and field management parameters were analyzed. The results show that these parameters including the interval between successive leaf tip appearances, days at optimum vernalizing temperature required to complete vernalization, percentage reduction in development rate in a photoperiod 10 hour shorter than the threshold relative to that at the threshold, standard kernel size under optimum conditions, kernel number per unit canopy weight at anthesis are the key sensitive parameters which should be firstly selected for the model localization. The optimal parameters selected for application of model in regional scale are planting date, planting density, fertilization date, planting depth and irrigation date. The research showed that the global sensitivity analysis in EFAST is effective for parameter selection in the crop growth model optimization to improve its performance at regional scale.
Yan Z.,Chinese Academy of Agricultural Sciences |
Peng Y.,Key Laboratory of Resources Remote Sensing and Digital Agriculture
Proceedings - 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011 | Year: 2011
With global warming and increasing of extreme climate evens, climate change may impose positive or negative effects on crop growth and yield. The traditional crop productivity simulations based on crop models are normally site-specific. In this study, the spatial crop model is developed by integrating Geographical Information System (GIS) with Erosion Productivity Impact Calculator (EPIC) model to simulate regional crop growth and yield. Data are exchanged using ASCII or binary data format between GIS and EPIC model without a common user interface. The GIS-based EPIC model is applied to simulate the average corn and wheat yield of 1980s in North China. Compared with the statistical yields, the crops yield of Shandong and Beijing is underestimated by the GIS-based EPIC model, especially for Beijing. However, the errors are mostly less than 10%, except that in Beijing and Shandong. Thus, the simulation accuracy of the GIS-based EPIC model is acceptable. The simulation accuracy can be improved by using the detailed field management information, such as irrigation, fertilization and tillage. © 2011 IEEE.