Agricultural land-use in China: a comparison of area estimates from ground-based census and satellite-borne remote sensing

We compare estimates of total cropland area, paddy rice area, and irrigated cropland area in China from land cover maps derived from optical remote sensing in 1992–93 (1-km resolution NOAA AVHRR) and county-level agricultural census data for 1990. At national, regional, provincial, and county scales, the total cropland area estimated by remote sensing is 50–100% higher than reported in the agricultural census. For topographically flat North and Central China, there is a high correlation between county-level cropland area estimates by the two approaches. For other regions, the correlation between remote sensing and agricultural census cropland area is much weaker. Throughout China, there is only moderate to weak correlation between remote sensing-based and census-bases estimates of paddy rice area and total irrigated cropland area. It is likely that the census data underestimates and the remote sensing overestimates the actual cropland area. These uncertainties in agricultural land cover estimates will contribute to uncertainty in any large-scale biogeochemical analyses.     

A large‐area,spatially continuous assessment of land cover map error and its impact on downstream analyses

Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high‐resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel‐wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative “downstream” (map‐based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps’ spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ～45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National‐scale maps derived from higher‐resolution imagery were most accurate, followed by multi‐map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland‐adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%–500% greater than in input cropland maps, but ～40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land cover map users.     

A Simple Semi-Automatic Approach for Land Cover Classification from Multispectral Remote Sensing Imagery

Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.     

Mapping global cropland and field size

A new 1 km global IIASA‐IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo‐Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA‐IFPRI cropland product has been validated using very high‐resolution satellite imagery via Geo‐Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo‐Wiki. The first ever global field size map was produced at the same resolution as the IIASA‐IFPRI cropland map based on interpolation of field size data collected via a Geo‐Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.     

土地覆盖变化检测方法比较——以内蒙古草原区为例

随着对地观测技术的不断发展,遥感影像分辨率逐渐提高,促进了基于遥感影像的变化检测从传统像元级的检测向面向对象的检测转变。为了探究面向对象的变化检测方法在土地覆盖变化检测中的有效性和适用性,对面向对象的变化检测方法与常规的变化检测方法进行对比评价。以内蒙古鄂尔多斯和包头地区为试验区,选取2002年及2011年的Landsat TM/ETM+影像为数据源,比较了图像代数运算、图像变换、图像空间结构特征和面向对象的多种变化检测方法,对研究区两期土地覆盖进行了变化检测研究。结果表明:面向对象的变化检测方法在总体精度、kappa系数上都有明显的优越性,总体精度均在87.42%以上,尤其以面向对象的变化矢量分析方法精度最高,达91.56%。此外,主成分差异法也有较好的检测效果,总体精度为87.83%。对总体精度较高的3种方法在不同土地覆盖变化类型中检测效果的研究表明:对于研究区几种主要土地覆盖变化类型,面向对象的变化矢量分析法均有较理想的检测效果,平均精度为85%左右,且始终优于面向对象的光谱向量相似法,以居民地及旱地相关的变化类型最为明显;主成分差异法对不同土地覆盖变化类型检测效果差异很大,对其中4种变化类型的精度甚至达到了93%以上,但对于检测草地与裸地间转化精度很低,甚至只有8.69%;在与工矿用地有关的土地覆盖变化类型中,面向对象的变化矢量分析法的精度明显高于主成分差异法,而在与居民地有关的变化类型中,主成分差异法表现出一定优势。     

遥感主体图的准确度对景观生态学研究的影响

用各种案例系统地解释了遥感数据分类误差对景观指数误差的必然影响。一方面 ,遥感数据在各种时间和空间尺度上为景观生态学研究提供必需的土地类型数据 ;另一方面 ,遥感技术的灵活性和复杂性可以产生出各种质量的土地类型数据。但景观生态学方面的用户对土地类型数据基本上是没有选择地使用 ,甚至是不知好坏地使用 ,所以景观生态学的发现和结论具有不可避免的任意性。总结了在各种情况下景观指数的变动区间 ,指出了现实较低的遥感数据的分类准确度会引起更低的景观指数的准确度 ,当进行景观变化分析时 ,这种误差的放大效应将更加明显。当前 ,人们对除面积以外的景观指数的误差仍然束手无策 ,尽可能地提高遥感数据的分类准确度是唯一力所能及的办法。     

Soil carbon stocks,deforestation and land‐cover changes in the Western Ghats biodiversity hotspot (India)

Habitat loss and soil organic carbon (SOC) stock variations linked to land‐cover change were estimated over two decades in the most densely populated biodiversity hotspot in the world, in order to assess the possible influence of conservation practices on the protection of SOC. For a study area of 88 484 km², 70% of which lie inside the Western Ghats Biodiversity Hotspot (WGBH), land‐cover maps for two dates (1977, 1999) were built from various data sources including remote sensing images and ecological forest maps. SOC stocks were calculated from climatic parameters, altitude, physiography, rock type, soil type and land‐cover, with a modelling approach used in predictive learning and based on Multiple Additive Regression Tree. The model was trained on 361 soil profiles data, and applied to estimate SOC stocks from predictor variables using a Geographical Information System (GIS). Comparison of 1977 and 1999 land‐cover maps showed 628 km² of dense forests habitat loss (6%), corresponding to an annual deforestation rate of 0.44%. This was found consistent with other studies carried out in other parts of the WGBH, but not with FAO figures showing an increase in forest area. This could be explained by the different forest definitions used, based on ecological classification in the former, and on percentage tree cover in the latter. Unexpectedly, our results showed that despite ongoing deforestation, overall SOC stock was maintained (～0.43 Pg). But a closer examination of spatial differences showed that soil carbon losses in deforested areas were compensated by sequestration elsewhere, mainly in recent plantations and newly irrigated croplands. This suggests that more carbon sequestration in soils could be achieved in the future through appropriate wasteland management. It is also expected that increasing concerns about biodiversity loss will favour more conservation and reinforce the already prevailing protective measures, thus further maintaining C stocks.     

Comparing and Combining Landsat Satellite Imagery and Participatory Data to Assess Land-Use and Land-Cover Changes in a Coastal Village in Papua New Guinea

In regions lacking socio-economic data, pairing satellite imagery with participatory information is essential for accurate land-use/cover (LULC) change assessments. At the village scale in Papua New Guinea we compare swidden LULC classifications using remote sensing analyses alone and analyses that combine participatory information and remotely sensed data. These participatory remote sensing (PRS) methods include participatory land-use mapping, household surveys, and validation of image analysis in combination with remotely sensed data. The classifications of the swidden area made using only remote sensing analysis show swidden areas are, on average, two and a half times larger than land managers reported for 1999 and 2011. Classifications made using only remote sensing analysis are homogeneous and lack discrimination among swidden plots, fallow land, and non-swidden vegetation. The information derived from PRS methods allows us to amend the remote sensing analysis and as a result swidden areas are more similar to actual swidden area found when ground-truthing. We conclude that PRS methods are needed to understand swidden system LULC complexities.     

基于MODIS时序数据的黑龙江流域火烧迹地提取

火烧迹地信息是研究火灾的重要参数和基础数据,也是研究全球生态系统和碳循环扰动的重要依据之一。以受森林火灾影响较为严重的黑龙江流域为研究区,以MODIS时间序列数据为数据源建立了一个分为两阶段的火烧基地提取算法(即首先设定较为严格的提取条件对最有可能发生火灾的像元——核心像元进行提取,然后设定较为宽松的阈值提取距离核心像元一定范围内的火烧像元),对2000—2011年的火烧迹地信息进行了提取,生成了研究区长时间序列火烧迹地分布图,并对其时空分布特征进行了分析。选择黑龙江省为典型验证区对算法精度进行了验证,结果显示算法的整体精度较之以往的算法有了一定程度的提高。     

景观生态学原理在城市土地利用分类中的应用

根据城市相同土地利用类型具有相似景观格局特征的原理,探讨了融合景观格局特征指数和遥感技术的城市土地利用信息提取的新方法。以北京市五环内建城区为例,研究表明,在斑块类型水平和景观水平上,居住用地和非居住用地内景观斑块的大小、形状、边缘特征、空间连接度、核心区面积特征、多样性、均匀性等特征都有极显著的差异。进一步融合TM遥感影像和这些景观格局特征指数,提取了居民用地和非居民用地类型,总分类精度是79.7%,Kappa系数达到59.8%。研究揭示,景观生态学原理的引入,为传统的遥感技术应用提供了新的思路,在格局复杂的城市土地利用信息提取中有很大的应用发展潜力。     

敦煌市土地利用/覆盖变化特征及成因分析

利用ArcGIS9.2软件,结合野外调查,对敦煌市1987、1996和2007年3期Land-sat5-TM遥感影像进行解译,并分8个类型分析了土地利用/覆盖变化的特征及成因。结果表明:近20年来敦煌市呈现出植被覆盖程度不断降低、覆盖面积减小的趋势,其中耕作区、城建用地和无植被区面积净增,其他5类用地面积净减;8类用地类型之间互有转换,转移的主要方向是部分草地和无植被区转变为耕作区,水域湿地和灌木林地转变为草地,草地由高覆盖、中覆盖、低覆盖到无植被区逐次转变;除了耕作区边缘植被覆盖程度有所增加外,其他区域退化趋势明显;引起LUCC的自然因素中气温升高的影响较大,而人文因素中土地开垦、增加灌溉的影响较大。     

Assessing large area forest cover products derived from the same imaging source across Victoria,Australia

Forest cover products are an essential tool for land managers and policy makers. They are used at a variety of spatial scales to inform decision‐making and policy across a range of ecosystem drivers and services. This article compares three forest cover products (FCP), all of which were created using Landsat satellite imagery, but using different methodologies and covering different spatial extents that range from global to state. It also explores their use and utility across the state of Victoria, Australia. It asks the question, how interchangeable are the forest cover maps? FCP are also validated against a very high‐resolution reference data set. Overall accuracy was around 89% for the state and national FCP, and 84% for the global FCP. The global map produced the lowest estimate of total forest cover, while estimates obtained by the national and state FCP were similar across the study area. Spatially, differences, however, were apparent. The national forest cover map obtained higher estimates across most of Victoria except in the most arid region which is dominated by low open woodland. While the national and global scale forest cover maps were found to have good diagnostic ability for large area assessment and reporting, their use for land management is not optimal and can lead to gross error.     

Mapping Tension: Remote Sensing and the Production of a Statewide Land Cover Map

This article examines the process by which remotely sensed land cover maps work to both simplify and complicate landscapes. The central argument is not merely that the construction of land cover maps is complex, but that the points of complexity often arise through the process of trying to simplify. In other words, the forces of complexity are intimately connected to the forces of simplicity and vice versa. This article takes as a case study the production of WISCLAND, (Wisconsin Initiative for Statewide Cooperation on Landscape Analysis and Data), a statewide land cover map of Wisconsin derived from remote sensing data and GIS (Geographic Information Systems) technologies and proceeds by analysis of mapping methodology, practice, and representation. In addition to the development of a more nuanced critique of the use of land cover maps, it facilitates the possibility for a constructive dialogue between remote sensing practitioners and the critical GIS community.     

基于NOAA PAL数据集的地表蒸散遥感估算方法

基于NOAA AVHRR气象卫星长时间序列10 d合成的PAL数据集（分辨率8 km×8 km）以及地表能量平衡原理和“VI-T_s”方法,建立了地表蒸散的遥感估算方法,该方法不需要地面气象观测数据的支持,所需参数可直接从遥感数据反演或推算,并选择国际上著名的遥感蒸散模型——SEBS模型对新建模型进行了验证比较.结果表明：新建模型和SEBS模型模拟的地表蒸散值及其季节性变化趋势非常一致,说明新构建模型的模拟结果比较可靠,能够反映地表蒸散的实际情况.新建地表蒸散遥感估算模型可操作性强,为利用长时间序列的卫星遥感数据研究我国乃至全球地表蒸散的时空变化规律提供了一个新的途径.     

基于多源遥感信息融合的广东省土地利用分类方法——以雷州半岛为例

准确高效的获取土地利用信息对生态环境评价非常重要。广东省地处华南热带和亚热带季风气候区,经济作物种类繁多,土地覆盖破碎,为土地利用精确分类带来很大不确定性,而常年多云雨的天气也为有效光学影像的获取带来困难。为提高土地覆盖分类精度,以雷州半岛为实验区,综合应用Landsat-TM/ETM、多时相HJ光学影像,以及X波段Terra SAR数据,通过分析不同地物类型在光谱、极化以及多时相特征上的差别,对原始图像进行特征提取。在此基础上融合多源遥感信息的地物特征运用面向对象土地覆盖分类方法获取研究区高精度的土地利用信息。结果显示这一方法能有效提高土地覆盖利用信息获取精度,为研究生态环境变化提供更准确的数据支持。     

A new forest cover map of continental southeast Asia derived from SPOT‐VEGETATION satellite imagery

Question: Can recent satellite imagery of coarse spatial resolution support forest cover assessment and mapping at the regional level? Location: Continental southeast Asia. Methods: Forest cover mapping was based on digital classification of SPOT4‐VEGETATION satellite images of 1 km spatial resolution from the dry seasons 1998/1999 and 1999/2000. Following a geographical stratification, the spectral clusters were visually assigned to land cover classes. The forest classes were validated by an independent set of maps, derived from interpretation of satellite imagery of high spatial resolution (Landsat TM, 30 m). Forest area estimates from the regional forest cover map were compared to the forest figures of the FAO database. Results: The regional forest cover map displays 12 forest and land cover classes. The mapping of the region's deciduous and fragmented forest cover remained challenging. A high correlation was found between forest area estimates obtained from this map and from the Landsat TM derived maps. The regional and sub‐regional forest area estimates were close to those reported by FAO. Conclusion: SPOT4‐VEGETATION satellite imagery can be used for mapping consistently and uniformly the extent and distribution of the broad forest cover types at the regional scale. The new map can be considered as an update and improvement on existing regional forest cover maps.     

Remote sensing imagery for resource inventories in central Africa: The importance of detailed field data

The rate of rain forest clearing throughout central Africa is of national and international interest because it affects both the region's contribution to global warming and impacts the sustainable productive capacity of its natural resource base. The size and inaccessibility of much of central Africa makes remote sensing imagery the most suitable data source for regional land cover mapping and land transformation monitoring. Present image availability is poor. Most regional studies have had to rely on coarse resolution AVHRR 1 km data that fails to detect the small-scale agricultural clearings that are the primary cause of land cover change throughout the region. This study demonstrates that higher spatial resolution Landsat MSS imagery, which comprises the most available, geographically comprehensive and longest time series dataset, is too coarse to map land cover in low population density areas typical of most of central Africa. Furthermore, this study cautions that the use of high resolution imagery without detailed collateral field data on population density and land use practices while generating superficially plausible results, will most probably produce highly inaccurate estimates of land cover and land transformation. Policies for future regional remote sensing surveys of central Africa should focus on acquisition of higher spatial, spectral, and radiometric resolution imagery and must be accompanied by detailed, systematic field data collection.     

基于NDVI假彩色合成法的土地覆被变化监测

土地覆被变化监测对区域生态系统保护、环境变化研究具有重要的作用,研究旨在提供一种基于归一化植被指数(NDVI)的假彩色合成法的土地覆被变化监测方法。该研究以黄河三角洲为研究区,以3期 Landsat TM影像(成像时间分别为1987年5月7日,1998年5月5日,2009年5月3日)为数据源,在进行相对辐射校正的基础上,生成3期NDVI图像,然后分别以三期的NDVI图像作为红、绿和蓝波段生成假彩色合成图像。基于彩色合成原理,对黄河三角洲的1987-2009年间的土地覆被变化进行了分析。结果表明:(1) 假彩色合成图像上的灰白色区域表示其土地覆被状态稳定,三个时期的NDVI值均较大,黑色区域的土地覆被状态也较稳定,但是三个时期的NDVI值均较小,而青色、绿色、红色则反映相应地区的NDVI处在不稳定状态;(2)不同的颜色反映了不同的土地覆被变化方式,较为直观地反映了土地覆被的变化特点,尤其是自然植被与农田之间的转换;(3)限于NDVI的瞬时性,该方法需要与基于遥感影像分类的方法相结合,才能更好地监测土地覆被变化。     

长江三峡库区生态环境变化遥感研究进展

长江三峡库区是我国最重要的生态敏感区之一,受三峡水利工程和相关人类活动影响,其生态环境变化显著.揭示三峡库区生态系统结构、功能和生态过程的变化对于维护库区生态安全具有重要意义.生态环境遥感为此提供了关键途径,并在三峡库区生态环境研究中受到重视.现有的三峡库区生态环境变化遥感研究往往存在较大差异,难以有效反映生态环境变化和响应特征,加之生态本底和人类干扰活动的复杂性,三峡库区生态环境变化遥感研究仍面临诸多挑战.本文对库区环境变化遥感研究的发展阶段、研究尺度、遥感数据和方法等进行了系统总结,并从土地利用/覆盖变化、植被变化、水土安全、生态服务价值评价、生态系统健康与生态规划5个方面对库区生态环境变化遥感研究进展进行综述,在此基础上探讨了当前三峡库区生态环境变化遥感研究存在的问题,并提出未来研究中需重点关注的主要科学问题,以期为三峡库区生态环境管理和类似地区生态环境变化遥感研究提供参考.