The degradation of forest and soil contribute significantly to carbon emission to the atmosphere leading to the build-up of carbon dioxide in atmosphere and contributing to global warming. Consequences of climate chan...The degradation of forest and soil contribute significantly to carbon emission to the atmosphere leading to the build-up of carbon dioxide in atmosphere and contributing to global warming. Consequences of climate change are not only the rise in global temperatures, but also changes in the precipitation patterns, which could affect agricultural production, food security, human health and long-term ecosystem properties balance. The deforestation and land degradation are major sources of GHG （greenhouse gas） emissions. International negotiations and dialogues on REDD＋ mechanism are held for both national and local level mitigation policies formulation for the reduction of carbon emission from land use, land use change and forestry sector. The reduction of emissions from fossil fuel combustion and avoidance of deforestation and forest/land degradation constitute lasting and long-term solutions for mitigating climate change. There is an urgent need of relevant and efficient methods of measuring forest and soil carbon through application of the latest geospatial technologies, i.e., GIS （geographic information system）, Remote Sensing and LiDAR （Light Detection and Ranging）. These technologies can support the precise measurement of carbon stocks, as well as, offer cost effective and interoperable data generation methods. The REDD＋ mechanism is being promoted worldwide mainly to reduce the diminishing of forest in developing countries. Such an approach must consider use rights, sustainable management of forests, ensuring and safe-guarding the benefit sharing mechanism and good governance, along with the legal framework and local livelihood concerns.展开更多
Abstract： Loess-paleosol sequences preserve records of climatic change during the Quaternary, which is important for paleoclimate study. In this study, a loess-palaeosol sequence from the Chumbur- Kosa （CK） site in...Abstract： Loess-paleosol sequences preserve records of climatic change during the Quaternary, which is important for paleoclimate study. In this study, a loess-palaeosol sequence from the Chumbur- Kosa （CK） site in the Sea of Azov region was investigated to reconstruct climatic variability during the Marine Isotope Stage （MIS）11- MIS 1, using proxies of grain size （GS）, magnetic susceptibility （xlf and Xfd（%））, carbonate content （CaCO3%） and soil color The results enabled formulation of a detailed description of the climatic characteristics related to each individual layer. The sequence indicates that the paleoclimate shifted progressively towards increasingly cooler, somewhat drier conditions. The CK section may thus be ideal for reconstructing climatie eondifions during the Middle and Late Pleistocene in the Sea of Azov region. However, the ）Of value of paleosol $2 in the CK profile indicates different characteristics from the other paleosol layers, dilution of carbonate resulting from carbonate leaching in L2 may be the main reason for the decrease in magnetic susceptibility. Furthermore, through simple analysis part of the environmental evolution process in the Sea of Azov region and Serbia during Middle and Late Pleistocene cycles. The climate cycle expressed by Xfd（%） and Xlf variations show similar patterns, with rapidly alternating cold and warm intervals. Nevertheless, although the two areas had different climatic regimes, geographical settings, and loess source areas, both exhibited similar climate change trends since the MIS 11.展开更多
Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kapp...Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kappa coefficients of agreement have been devised as thematic accuracy metrics. However, such spatially averaged measures about accuracy neither offer hints about spatial variation in misclassification, nor are useful for quantifying error margins in derivatives, such as the areal extents of different land cover types and the land cover change statistics. Such limitations originate from the deficiency that spatial dependency is not accommodated in the conventional methods for error analysis. Geostatistics provides a good framework for uncertainty characterization in land cover information. Methods for predicting and propagating misclassification will be described on the basis of indicator samples and covariates, such as spectrally derived posteriori probabilities. An experiment using simulated datasets was carried out to quantify the error in land cover change derived from postclassification comparison. It was found that significant biases result from applying joint probability rules assuming temporal independence between misclassifications across time, thus emphasizing the need for the stochastic simulation in error modeling. Further investigations, incorporating indicators and probabilistic data for mapping and propagating misclassification, are anticipated.展开更多
文摘The degradation of forest and soil contribute significantly to carbon emission to the atmosphere leading to the build-up of carbon dioxide in atmosphere and contributing to global warming. Consequences of climate change are not only the rise in global temperatures, but also changes in the precipitation patterns, which could affect agricultural production, food security, human health and long-term ecosystem properties balance. The deforestation and land degradation are major sources of GHG （greenhouse gas） emissions. International negotiations and dialogues on REDD＋ mechanism are held for both national and local level mitigation policies formulation for the reduction of carbon emission from land use, land use change and forestry sector. The reduction of emissions from fossil fuel combustion and avoidance of deforestation and forest/land degradation constitute lasting and long-term solutions for mitigating climate change. There is an urgent need of relevant and efficient methods of measuring forest and soil carbon through application of the latest geospatial technologies, i.e., GIS （geographic information system）, Remote Sensing and LiDAR （Light Detection and Ranging）. These technologies can support the precise measurement of carbon stocks, as well as, offer cost effective and interoperable data generation methods. The REDD＋ mechanism is being promoted worldwide mainly to reduce the diminishing of forest in developing countries. Such an approach must consider use rights, sustainable management of forests, ensuring and safe-guarding the benefit sharing mechanism and good governance, along with the legal framework and local livelihood concerns.
基金supported by the National Natural Science Foundation of China(Grant No.41271024)International Cooperation and Exchanges Project(The record of landscape changes in Eurasian arid and semi-arid regions by loess-paleosol sequence of southern Russian on the million scales and its comparative study with Chinese loess(Grant No.No.41411130204)
文摘Abstract： Loess-paleosol sequences preserve records of climatic change during the Quaternary, which is important for paleoclimate study. In this study, a loess-palaeosol sequence from the Chumbur- Kosa （CK） site in the Sea of Azov region was investigated to reconstruct climatic variability during the Marine Isotope Stage （MIS）11- MIS 1, using proxies of grain size （GS）, magnetic susceptibility （xlf and Xfd（%））, carbonate content （CaCO3%） and soil color The results enabled formulation of a detailed description of the climatic characteristics related to each individual layer. The sequence indicates that the paleoclimate shifted progressively towards increasingly cooler, somewhat drier conditions. The CK section may thus be ideal for reconstructing climatie eondifions during the Middle and Late Pleistocene in the Sea of Azov region. However, the ）Of value of paleosol $2 in the CK profile indicates different characteristics from the other paleosol layers, dilution of carbonate resulting from carbonate leaching in L2 may be the main reason for the decrease in magnetic susceptibility. Furthermore, through simple analysis part of the environmental evolution process in the Sea of Azov region and Serbia during Middle and Late Pleistocene cycles. The climate cycle expressed by Xfd（%） and Xlf variations show similar patterns, with rapidly alternating cold and warm intervals. Nevertheless, although the two areas had different climatic regimes, geographical settings, and loess source areas, both exhibited similar climate change trends since the MIS 11.
基金Supported by the National 973 Program of China (No. 2006CB701302)the Hubei Department of Science and Technology (No. 2007ABA276)
文摘Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kappa coefficients of agreement have been devised as thematic accuracy metrics. However, such spatially averaged measures about accuracy neither offer hints about spatial variation in misclassification, nor are useful for quantifying error margins in derivatives, such as the areal extents of different land cover types and the land cover change statistics. Such limitations originate from the deficiency that spatial dependency is not accommodated in the conventional methods for error analysis. Geostatistics provides a good framework for uncertainty characterization in land cover information. Methods for predicting and propagating misclassification will be described on the basis of indicator samples and covariates, such as spectrally derived posteriori probabilities. An experiment using simulated datasets was carried out to quantify the error in land cover change derived from postclassification comparison. It was found that significant biases result from applying joint probability rules assuming temporal independence between misclassifications across time, thus emphasizing the need for the stochastic simulation in error modeling. Further investigations, incorporating indicators and probabilistic data for mapping and propagating misclassification, are anticipated.
