基于土地利用/覆被分类系统估算 碳储量的差异——以海南岛森林为例

土地利用/覆被分类系统是碳蓄积研究的依据,然而各种碳蓄积研究所采用的土地利用/覆被分类系统不尽相同。根据1993年海南林业资源二类调查资料,我们按照USGS土地利用/覆被、LCCS土地覆被和中国科学院土地资源三种分类系统所定义的类别进行分类并计算了各自的碳储量和碳密度,为碳储量的进一步精确估算和土地覆被分类系统研制提供重要的科学依据。结果发现,（1）不同的土地利用/覆被分类系统所对应的总碳储量以及平均碳密度都有了明显的差别。林业调查资料的植被分类、FAO 土地覆被分类系统(LCCS)、USGS 土地利用/土地覆被分类系统以及我国学者常用的中国科学院土地资源分类系统的碳蓄积量(Tg C)分别是28.98、28.71、21.04和21.04;碳密度(t C/ha)分别是31.24、30.95、22.68和22.68。（2）土地利用分类系统和土地覆被分类系统之间的结果具有较大差异,其碳储量相差7.67～7.94 Tg C,碳密度则相差8.27～8.56 t C/ha;差距在26.47%～37.74%之间。与其他学者研究结果比较发现,土地利用/覆被分类系统造成的碳蓄积差异的变化方向是不定的,取决于具体的分类系统和材积-生物量函数。不同土地利用/覆被分类系统对于植被划分的不同,导致了材积-生物量回归方程和类别面积的差异是造成碳蓄积和碳密度估算差异的根本原因。目前常用的土地利用/覆被分类系统在估算碳蓄积中存在一定问题,不适合于高精度的碳蓄积计算。体现地表植被生物量差异、植被叶型和外貌特征、种类及树龄差异等内涵的土地利用/覆被分类系统利于陆地碳循环研究的深化。     

基于3S技术的金沙江干热河谷区LUCC研究——以云南省元谋县为例

以云南省元谋县为例,集成3S技术,对多期Landsat-TM数据进行土地利用/土地覆被信息提取及建立数据库,采用单一土地利用/土地覆被动态度和综合土地利用/土地覆被动态度指标,分析金沙江干热河谷区土地利用/土地覆被结构变化规律,提出基于生态环境建设的金沙江干热河谷区土地利用优化对策。并结合DEM数据分析分析金沙江干热河谷区土地利用/土地覆被垂直分异及其变化规律,结果表明：1.草地和林地是土地利用/土地覆被的主体,但林地以疏林地为主,草地以低、中覆盖度草地为主,具有相当大的敏感性和脆弱性;2.土地利用较为粗放,如水浇地占耕地、人工草地占草地及城乡建设用地占总用地面积的比例等相对较低;3.土地利用/土地覆被结构发生了较快的变化,耕地、草地、水域、未利用土地面积有所减少,而林地、城乡建设用地面积增长较快;4.低山区和中低山区为以林草地为主、耕地亦有一定比例的类农林牧交错带,是干热河谷区生态环境最脆弱的地带。     

A review of historical reconstruction methods of land use/land cover

Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relatively standardized information from historical sources. Based on available data sources and a retrospective research, in this paper we review the approaches and methods of the digital reconstruction and analyze their advantages and possible constraints in the following aspects: (1) Historical documents contain qualitative or semi-quantitative information about past land use, which also usually include land-cover data, but preparation of archival documents is very time-consuming. (2) Historical maps and pictures offer visual and spatial quantitative land-cover information. (3) Natural archive has significant advantages as a method for reconstructing past vegetation and has its unique possibilities especially when historical records are missing or lacking, but it has great limits of rebuilding certain land-cover types. (4) Historical reconstruction models have been gradually developed from empirical models to mechanistic ones. The method does not only reconstruct the quantity of land use/cover in historical periods, but it also reproduces the spatial distribution. Yet there are still few historical land-cover datasets with high spatial resolution. (5) Reconstruction method based on multiple-source data and multidisciplinary research could build historical land-cover from multiple perspectives, complement the missing data, verify reconstruction results and thus improve reconstruction accuracy. However, there are challenges that make the method still in the exploratory stage. This method can be a long-term development goal for the historical land-cover reconstruction. Researchers should focus on rebuilding historical land-cover dataset with high spatial resolution by developing new models so that the study results could be effectively applied in simulations of climatic and ecological effects.     

土地利用/覆被历史重建方法(英文)

Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relatively standardized information from historical sources. Based on available data sources and a retrospective research, in this paper we review the approaches and methods of the digital reconstruction and analyze their advantages and possible constraints in the following aspects:(1) Historical documents contain qualitative or semi-quantitative information about past land use, which also usually include land-cover data, but preparation of archival documents is very time-consuming.(2) Historical maps and pictures offer visual and spatial quantitative land-cover information.(3) Natural archive has significant advantages as a method for reconstructing past vegetation and has its unique possibilities especially when historical records are missing or lacking, but it has great limits of rebuilding certain land-cover types.(4) Historical reconstruction models have been gradually developed from empirical models to mechanistic ones. The method does not only reconstruct the quantity of land use/cover in historical periods, but it also reproduces the spatial distribution. Yet there are still few historical land-cover datasets with high spatial resolution.(5) Reconstruction method based on multiple-source data and multidisciplinary research could build historical land-cover from multiple perspectives, complement the missing data, verify reconstruction results and thus improve reconstruction accuracy. However, there are challenges that make the method still in the exploratory stage. This method can be a long-term development goal for the historical land-cover reconstruction. Researchers should focus on rebuilding historical land-cover dataset with high spatial resolution by developing new models so that the study results could be effectively applied in simulations of climatic and ecological effects.     

Integrating land-cover data with different ontologies: identifying change from inconsistency

Spatially coincident land-cover information frequently varies due to technological and political variations. This is especially problematic for time-series analyses. We present an approach using expert expressions of how the semantics of different datasets relate to integrating temporal time series land-cover information where the classification classes have fundamentally changed. We use land-cover mapping in the UK (LCMGB and LCM2000) as example data sets because of the extensive object-based meta-data in the LCM2000. Inconsistencies between the two datasets can arise from random, gross and systematic error and from an actual change in land cover. Locales of possible land-cover change are inferred by comparing characterizations derived from the semantic relations and meta-data. Field visits showed errors of omission to be 21% and errors of commission to be 28%, despite the accuracy limitations of the land-cover information when compared with the field survey component of the Countryside Survey 2000.     

Large-scale land cover mapping with the integration of multi-source information based on the Dempster–Shafer theory

Land cover type is a crucial parameter that is required for various land surface models that simulate water and carbon cycles, ecosystem dynamics, and climate change. Many land use/land cover maps used in recent years have been derived from field investigations and remote-sensing observations. However, no land cover map that is derived from a single source (such as satellite observation) properly meets the needs of land surface simulation in China. This article presents a decision-fuse method to produce a higher-accuracy land cover map by combining multi-source local data based on the Dempster–Shafer (D–S) evidence theory. A practical evidence generation scheme was used to integrate multi-source land cover classification information. The basic probability values of the input data were obtained from literature reviews and expert knowledge. A Multi-source Integrated Chinese Land Cover (MICLCover) map was generated by combining multi-source land cover/land use classification maps including a 1:1,000,000 vegetation map, a 1:100,000 land use map for the year 2000, a 1:1,000,000 swamp-wetland map, a glacier map, and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001). The merit of this new map is that it uses a common classification system (the International Geosphere-Biosphere Programme (IGBP) land cover classification system), and it has a unified 1 km resolution. The accuracy of the new map was validated by a hybrid procedure. The validation results show great improvement in accuracy for the MICLCover map. The local-scale visual comparison validations for three regions show that the MICLCover map provides more spatial details on land cover at the local scale compared with other popular land cover products. The improvement in accuracy is true for all classes but particularly for cropland, urban, glacier, wetland, and water body classes. Validation by comparison with the China Forestry Scientific Data Center (CFSDC)–Forest Inventory Data (FID) data shows that overall forest accuracies in five provinces increased to between 42.19% and 88.65% for our MICLCover map, while those of the MODIS2001 map increased between 27.77% and 77.89%. The validation all over China shows that the overall accuracy of the MICLCover map is 71%, which is higher than the accuracies of other land cover maps. This map therefore can be used as an important input for land surface models of China. It has the potential to improve the modeling accuracy of land surface processes as well as to support other aspects of scientific land surface investigations in China.     

美国土地利用/土地覆盖变化的空间特征

1IntroductionInrecentyears,studyonglobalchangehasbecomethefocus,onwhichstresshasbeenlaidbytodayhumansociety.Inordertodealwithseriesofenvironmentalproblemssuchasdegradationoflandresources,globalwarming,destructionofozonosphereandsoon,andassurethestabilizationandsustainabledevelopmentofworldeconomy,IGBPhasestablishedthestudyframeforglobalchange.Withavastrangeofstudy,researchonglobalchangehasbroughtforwardagreatdealofimportantscientificquestionsrelatedtofeasibilityforhumanelivingoftheEarth.Inth…     

Spatial features of land use/land cover change in the United States

With the classification data covering American land-use/land-cover (LUCC) with 30 m resolution from the project of National Land Cover Data (NLCD), we normalized them and made their resolution changed into 1 km ×1 km, created the data of American land-use grade and analyzed the spatial distribution and features of American LUCC as well as the influence of population and altitude on the land-use grade in light of methods of sampling analysis and correlation study. Based on the analysis, we concluded that forestry and grassland, accounting for 71.24% of the whole country, has taken the main part of American land cover, and besides, construction and arable land has occupied 19.22% of the total land, the rest of land cover types, including water area, wetland and underdeveloped land, is 9.54% of the country's total. The developing potential of American land resources is enormous with less destroyed and disturbed ecological environment. Although, in some sense, the population and altitude influence the spatial variation of American land-use grade respectively, the influence of spatial variation of altitude and population density on that of land-use grade is not significanct.     

山地城市土地覆盖变化对地表温度的影响

针对山地城市复杂的城市地貌和下垫面类型,本文使用TM、DEM、ETM⁺等遥感影像资料,提取了重庆市土地利用覆盖类型;借助TM、MSS等遥感数据的红外波段,反演出1988年和2000年的地表温度。分析了重庆市近十年的土地覆盖变化及其对地表温度的影响,结果表明,在1988~2000年间,研究区土地覆盖变化明显,特别是城市土地覆盖面积有显著增加。土地覆盖类型的变化会改变地表温度的空间分布,尤其是城市土地的扩展会提高地表温度。对山地、丘陵、平坝、陡坡四种耕地的地表温度进行了深入分析与研究,结果表明:山地城市土地覆盖变化引起了植被覆盖度的变化,而植被覆盖度的变化又相应地影响了地表温度的变化,植被覆盖度每下降10%,地表温度上升0.49K。     

Thematic land-cover map assimilation and synthesis: the case of locating potential bioenergy feedstock in eastern Ontario,Canada

Insufficient spatial coverage of existing land-cover data is a common limitation to timely and effective spatial analysis. Achieving spatial completeness of land-cover data is the most challenging for large study areas which straddle ecological or administrative boundaries, and where individuals and agencies lack access to, and the means to process, raw data from which to derive spatially complete land-cover maps. In many cases, various sources of secondary data are available, so that land-cover map assimilation and synthesis can resolve this research problem. The following paper develops a reliable and repeatable framework for assimilating and synthesizing pre-classified data sets. Assimilation is achieved through data reformatting and map legend reconciliation in the context of a specific application. Individual maps are assessed for accuracy at various geographic scales and levels of thematic precision, with an emphasis on the ‘area of overlap’, in order to extract information that guides the synthesis process. The quality of the synthesized land-cover data set is evaluated using advanced accuracy assessment methods, including a measure describing the ‘magnitude of disagreement’. This method is applied to derive a seamless thematic map of the land cover of eastern Ontario from two disparate map series. The importance of assessing data quality throughout the process using multiple reference data sets is highlighted, and limitations of the method are discussed.     

How many predictors in species distribution models at the landscape scale? Land use versus LiDAR-derived canopy height

At the local spatial scale, land-use variables are often employed as predictors for ecological niche models (ENMs). Remote sensing can provide additional synoptic information describing vegetation structure in detail. However, there is limited knowledge on which environmental variables and how many of them should be used to calibrate ENMs. We used an information-theoretic approach to compare the performance of ENMs using different sets of predictors: (1) a full set of land-cover variables (seven, obtained from the LGN6 Dutch National Land Use Database); (2) a reduced set of land-cover variables (three); (3) remotely sensed laser data optimized to measure vegetation structure and canopy height (LiDAR, light detection and ranging); and (4) combinations of land cover and LiDAR. ENMs were built for a set of bird species in the Veluwe Natura 2000 site (the Netherlands); for each species, 26–214 records were available from standardized monitoring. Models were built using MaxEnt, and the best performing models were identified using the Akaike’s information criterion corrected for small sample size (AICc). For 78% of the bird species analysed, LiDAR data were included in the best AICc model. The model including LiDAR only was the best performing one in most cases, followed by the model including a reduced set of land-use variables. Models including many land-use variables tended to have limited support. The number of variables included in the best model increased for species with more presence records. For all species with 33 records or less, the best model included LiDAR only. Models with many land-use variables were only selected for species with >150 records. Test area under the curve (AUC) scores ranged between 0.72 and 0.92. Remote sensing data can thus provide regional information useful for modelling at the local and landscape scale, particularly when presence records are limited. ENMs can be optimized through the selection of the number and identity of environmental predictors. Few variables can be sufficient if presence records are limited in number. Synoptic remote sensing data provide a good measure of vegetation structure and may allow a better representation of the available habitat, being extremely useful in this case. Conversely, a larger number of predictors, including land-use variables, can be useful if a large number of presence records are available.     

Application of Mineral Exploration Models and GIS to Generate Mineral Potential Maps as Input for Optimum Land-Use Planning in the Philippines

The inherent problems of classifying or inventorying potential mineral resources (as opposed to known mineral resources) pose specific challenges. In this paper, the application of a conceptual mineral exploration model and GIS to generate mineral potential maps as input to land-use policy decision-making is illustrated. We implement the criteria provided by a conceptual exploration model for nickeliferous-laterites by using a GIS to classify the nickeliferous-laterite potential of an area in the northeastern part of the Philippines. The spatial data inputs to the GIS are geological map data, topographic map data, and stream sediment point data. Processing of these data yields derivative maps, which are used as indicators of nickeliferous-laterite potential. The indicator maps then are integrated to furnish a nickeliferous-laterite potential map. This map is compared with present land-use classification and policy in the area. The results indicate high potential for nickeliferous-laterite occurrence in the area, but the zones of potential are in places where mineral resources development is prohibited. The prohibition was imposed before the nickeliferous-laterite potential was assessed by this study. Mineral potential classification therefore is a critical input to land-use policy-making so that prospective land is not alienated from future mineral resource development.     

Comparison of land cover maps using fuzzy agreement

A generic problem associated with different land cover maps that cover the same geographical area is the use of different legend categories. There may be disagreement in many areas when comparing different land cover products even though the legend shows the same or very similar land cover class. To capture the uncertainty associated with both differences in the legend and the difficulty in classification when comparing two land cover maps, expert knowledge and a fuzzy logic framework are used to map the fuzzy agreement. The methodology is illustrated by comparing the Global Land Cover 2000 data set and the MODIS global land cover product. Overall accuracy measures are calculated, and the spatial fuzzy agreement between the two land cover products is provided. This approach can be used to improve the overall confidence in a land cover product, since areas of severe disagreement can be highlighted, and areas can be identified that require further attention and possible re-mapping.     

土地利用/土地覆被变化研究:寻求新的综合途径

土地利用/土地覆被变化是很复杂的现象,参与该项目的研究人员要避免“瞎子摸大象”那样的片面性,必须寻求新的综合研究途径。为此,不能简单地沿袭传统土地利用研究的思路和方法,需要不断提出新的研究论题;对土地利用变化驱动力必须有一种普遍的、综合的认识;需要将多个案例研究联结为一个可代表区域空间异质性的网络,需要作多空间尺度的研究,从而将地方尺度和区域尺度的土地覆被动态联系起来;需要发展新的研究方法,并将从农户调查到遥感数据的各种信息综合起来;尤其需要形成关于土地利用/土地覆被变化的综合科学理论框架     

基于遥感技术和GIS的小流域土地利用/覆被变化分析

根据贵州省平坝县克酬流域1973年MSS影像数据,1989、1995、2000年Landsat TM影像数据,利用RS和GIS信息获取技术、空间分析技术和数理统计方法,分析了该流域土地利用的数量变化和空间变化特征,明确了土地利用变化的主要类型和方向,并探讨了土地利用变化和地形因子的关系。结果显示,该流域土地利用以农田和灌草用地为主,1973~2000年林地和灌草地减少,农田、水域、村镇、交通用地增加,林地转化为灌草、灌草转化为农田及农田转化为村镇居民点和公路用地是研究区主要土地利用变化类型。研究区土地利用空间分布与海拔高程、坡度等地形因子有密切关系。1973~2000年农田、林地和灌草分布的海拔高程和坡度均呈增加趋势,这和上述土地利用类型间的转化规律相一致。     

An approach to spatially explicit reconstruction of historical forest in Northeast China

The spatially explicit reconstruction of historical land-cover datasets plays an important role in studying the climatic and ecological effects of land-use and land-cover change（LUCC）. Using potential natural vegetation（PNV） and satellite-based land use data, we determined the possible maximum distribution extent of forest cover in the absence of human disturbance. Subsequently, topography and climate factors were selected to assess the suitability of land for cultivation. Finally, a historical forest area allocation model was devised on the basis of the suitability of land for cultivation. As a case study, we used the historical forest area allocation model to reconstruct forest cover for 1780 and 1940 in Northeast China with a 10-km resolution. To validate the model, we compared satellite-based forest cover data with our reconstruction for 2000. A one-sample t-test of absolute bias showed that the two-tailed significance was 0.12, larger than the significant level 0.05, suggesting that the model has strong ability to capture the spatial distribution of forests. In addition, we calculated the relative difference of our reconstruction at the county scale for 1780 in Northeast China. The number of counties whose relative difference ranged from-30% to 30% is 99, accounting for 74.44% of all counties. These findings demonstrated that the provincial forest area could be transformed into forest cover maps well using the model.     

基于多期数据集的中亚五国土地利用/覆盖变化分析

针对目前中亚地区土地利用变化和分布格局方面的信息相对匮乏,现有资料较为陈旧且零散,无法满足中亚生态与环境变化研究需求的现状,利用全球的UMD, DISCover,GLC2000,GlobCover2005和GlobCover2009的5期土地覆被遥感数据集,提取中亚地区长时间序列土地覆被信息。并针对上述4个土地覆被分类系统无法进行直接对比和变化分析的问题,分别将其综合为4类土地覆被类型：耕地、自然植被、水体和其他,以分析近30 a中亚土地利用/土地覆被变化趋势。中亚土地利用类型多样,草地、裸地、农田、灌丛占绝对优势。自前苏联解体以后,20世纪90年代初至2000年期间,耕地面积大幅度减少,至2010年尽管有所恢复,但仍无法达到20世纪90年代初水平。而自然植被表现出了相反的趋势,这说明在此时间段内,由于社会政治制度的变化和市场经济的建立,耕地发生了较大规模的弃耕,弃耕地通常转换为草地、灌丛等自然植被。近10 a由于社会经济条件的变化,前苏联解体后所弃耕的土地又被收复和重新开发为耕地。90年代初至2000年期间,水体呈现先减少后增加的趋势。利用全球基于多期不同信息源获得的中亚土地覆被数据,尽管分类体系不统一,但均可较好地表征当时地表覆被状况。这在一定程度上弥补了中亚地区土地覆被数据不足的现状。通过对耕地、自然植被、水体及其他土地覆被类型进行大类合并,可基本体现中亚土地覆被的宏观特征和变化趋势。     

岷江上游半干旱河谷土地利用/土地覆盖研究

岷江上游半干旱河谷土地利用/土地覆盖结构受山地系统特征影响,以林地为主,土地利用类型分布呈垂直带性。人口增长、人民生活水平的提高以及经济政策的激发,导致耕地面积扩大,流域森林面积下降,可采资源消耗贻尽。森林面积减少、耕地面积增长是引起干旱河谷干旱面积范围扩大的重要因素。岷江上游半干旱河谷土地利用优化应以长江流域的持续发展为着眼点,突出大流域生态屏障功能;建立生态补偿机制,完善相关政策法规,以保证生态重建和土地利用结构调整工作的长期稳定性;提高土地利用方式的科技含量;在科学规划的指导下,先易后难,逐步实现生态建设与半干旱河谷的治理。     

东北地区历史时期森林空间格局网格化重建方法研究(英文)

The spatially explicit reconstruction of historical land-cover datasets plays an important role in studying the climatic and ecological effects of land-use and land-cover change(LUCC). Using potential natural vegetation(PNV) and satellite-based land use data, we determined the possible maximum distribution extent of forest cover in the absence of human disturbance. Subsequently, topography and climate factors were selected to assess the suitability of land for cultivation. Finally, a historical forest area allocation model was devised on the basis of the suitability of land for cultivation. As a case study, we used the historical forest area allocation model to reconstruct forest cover for 1780 and 1940 in Northeast China with a 10-km resolution. To validate the model, we compared satellite-based forest cover data with our reconstruction for 2000. A one-sample t-test of absolute bias showed that the two-tailed significance was 0.12, larger than the significant level 0.05, suggesting that the model has strong ability to capture the spatial distribution of forests. In addition, we calculated the relative difference of our reconstruction at the county scale for 1780 in Northeast China. The number of counties whose relative difference ranged from-30% to 30% is 99, accounting for 74.44% of all counties. These findings demonstrated that the provincial forest area could be transformed into forest cover maps well using the model.