A new land cover classification system was established for the Three Gorges Reservoir Region(TGRR) after considering the continuity of inundation and the natural characteristics of land cover. The potential evapotrans...A new land cover classification system was established for the Three Gorges Reservoir Region(TGRR) after considering the continuity of inundation and the natural characteristics of land cover. The potential evapotranspiration(PET) was predicted using a modified Penman-Monteith(P-M) model. The region's ratio of precipitation to evapotranspiration was calculated as the humidity index(HI). The data obtained was used to analyze climatic responses to land cover conversions from the perspectives of evapotranspiration and humidity variations. The results show that, from 1997 to 2009, the average annual PET increased in the early years and decreased later. In terms of overall spatial distribution, a significant reciprocal relationship appeared between annual PET and annual HI. In 1997,the annual PET was higher in the lower reaches than in the upper reaches of the TGRR, but the areas with high PET shifted substantially westward by 2003. The annual PET continued to increase in 2006, but the areas with high PET shrank by 2009. In contrast, the annual HI showed varying degrees of localized spatial variability. Over the three periods, the dominantforms of land cover conversions occurred from evergreen cover to seasonal green cover, from seasonal green cover to evergreen cover, and from seasonal green cover to seasonally inundated areas, respectively. These accounted for 48.0%, 38.4%, and 23.8% of the total areas of converted land covers in the three periods, respectively. During the period between 1997 and 2003, the main forms of land cover conversions resulted in both positive and negative growths in the average annual PET, while all of them pushed down the average annual HI. From 2003 to 2006, the reservoir region experienced neither a decrease in the annual PET nor an increase in the annual HI. The period between 2006 and 2009 saw a consistent downward trend in the annual PET and a consistent upward trend in the annual HI.展开更多
Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatia...Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.展开更多
基金partially supported and funded by Chongqing Research Program of Basic Research and Frontier Technology (Grant No. cstc2017jcyj B0317)Chongqing University Innovation Team Building Plan (Grant No. CXTDX201601017)Science and Technology Project of Chongqing Municipal Education Commission (Grant No. KJ1738462)
文摘A new land cover classification system was established for the Three Gorges Reservoir Region(TGRR) after considering the continuity of inundation and the natural characteristics of land cover. The potential evapotranspiration(PET) was predicted using a modified Penman-Monteith(P-M) model. The region's ratio of precipitation to evapotranspiration was calculated as the humidity index(HI). The data obtained was used to analyze climatic responses to land cover conversions from the perspectives of evapotranspiration and humidity variations. The results show that, from 1997 to 2009, the average annual PET increased in the early years and decreased later. In terms of overall spatial distribution, a significant reciprocal relationship appeared between annual PET and annual HI. In 1997,the annual PET was higher in the lower reaches than in the upper reaches of the TGRR, but the areas with high PET shifted substantially westward by 2003. The annual PET continued to increase in 2006, but the areas with high PET shrank by 2009. In contrast, the annual HI showed varying degrees of localized spatial variability. Over the three periods, the dominantforms of land cover conversions occurred from evergreen cover to seasonal green cover, from seasonal green cover to evergreen cover, and from seasonal green cover to seasonally inundated areas, respectively. These accounted for 48.0%, 38.4%, and 23.8% of the total areas of converted land covers in the three periods, respectively. During the period between 1997 and 2003, the main forms of land cover conversions resulted in both positive and negative growths in the average annual PET, while all of them pushed down the average annual HI. From 2003 to 2006, the reservoir region experienced neither a decrease in the annual PET nor an increase in the annual HI. The period between 2006 and 2009 saw a consistent downward trend in the annual PET and a consistent upward trend in the annual HI.
基金funded by the Natural Science and Engineering Research Council (NSERC) of Canada (No. RGPIN-2014-04100)
文摘Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.
