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.     

Accuracy assessments of the GLOBCOVER dataset using global statistical inventories and FLUXNET site data

The spatio-temporal distribution of land cover provides fundamental data for global climate and environmental change research. In recent decades, five global land cover maps have been produced based on remote sensing data sources and methodologies. Related research have shown that the availability and quality of the first four global land cover datasets are poor at the regional or the continental scale for a variety of reasons. There is still no consensus on the accuracy of the latest global land cover map. Based on comparison of the land cover dataset with the statistical cropland data from FAO and the FLUXNET site data, this paper discusses the accuracy of the fifth global land cover map, namely, the GLOBCOVER dataset, at different spatial scales. At the global scale, the cropland area obtained from the GLOBCOVER dataset is greater than that of the FAO statistical data by 47.06–84.49%, and the land cover types of the GLOBCOVER dataset have a 65.02% consistency with that of the FLUXNET site data. At the continental scale, the difference between cropland areas obtained from the GLOBCOVER dataset and the statistical cropland area vary from ?43.42% to 502.36%; continents that have a more accurate cropland area compared to the FAO statistical data tend to be less consistent with the FLUXNET site data. In general, North America has a higher accuracy and Oceania has a lower accuracy. At the country scale, the accuracy estimates vary sharply over a wide range: between ?100.00% and 190670.37%. It is recommended that future studies should pay careful attention to the data validation step before using the GLOBCOVER dataset for any particular problem. Future studies are also required for the development of a universal land cover classification system and advanced algorithms for remote sensing classification of global land cover maps.     

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.     

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.     

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 Approach to Diversity Indices – Modeling and Mapping Plant Biodiversity of Nallihan (A3-Ankara/Turkey) Forest Ecosystem in Frame of Geographic Information Systems

Modeling and mapping possibilities of Shannon–Wiener, Simpson, and number of species (NS) indices were researched using geographic information systems (GIS) and remote sensing (RS) tools in Nallihan forest ecosystem of Turkey. The relationships between the indices and a number of independent variables such as topography, geology, soil, climate, normalized difference vegetation index (NDVI), and land cover were investigated to understand relationships between plant diversity and ecosystem. Georeferenced field data from the established 56 quadrats (50 × 20 m) were used to calculate the indices. Principle component analysis (PCA) and multiple regression were employed for data reduction and model development, respectively. Three diversity maps were produced using the developed models. Residual maps and logical interpretations in ecological point of view were used to test the validity of the models. Elevation and climatic factors formed the most important components that are effective determinants of plant species diversity, but geological formations, soil, land cover and land-use characteristics also influenced plant diversity. Considering the different responses of the models, Shannon–Wiener (SWI) and NS models were found suitable for rare cover types, while Simpson (SIMP) model might be appropriate for single dominant land covers in the study area.     

测量的区域土地覆盖格局研基于多尺度遥感究

 利用1km、4km和8km 3种空间分辨率的NOAA/AVHRR数字影像,对中国NECT样带西部地区进行了土地覆盖分类及其景观特征的比较研究。重点比较了几种空间分辨率遥感数据分类结果边界的一致性和空间差异,以及影像所记录的景观格局的差异。为进一步在不同尺度上研究景观变化过程以及尺度转换研究奠定了基础。研究表明：3种空间分辨率的遥感影像所反映的区域土地覆盖的宏观空间格局是一致的,但类型的边界、每一类型斑块的形状和数量均产生较大的差异;经过对反映景观空间结构的4种指标（分维数、破碎度、多样性、优势度）的比较显示出随着遥感影像空间分辨率的变化,影像所反映的景观结构发生了较大的变化。其中,各覆盖类型的分维数表现出最大差异,表征着空间分辨率的变化对斑块复杂程度的影响最大。     

Assessing the effect of EWDP on vegetation restoration by remote sensing in the lower reaches of Tarim River

In this study, effect of ecological water diversion on vegetation restoration in the lower reaches of Tarim River is assessed by coupling remote sensing techniques and a field-based survey. Land use/cover and fractional vegetation coverage (Fvc) maps derived from remote sensing images, ground validation data, and hydrological observation data are adopted to analyze the responses of Ecological Water Diversion Project (EWDP). The results indicate that, the EWDP has showed a positive effect on vegetation restoration in the lower part of Tarim Basin. During 2001 to 2013, transformation from unused land to nature vegetation (i.e. forest land, grassland and scrubland) was the major process of land use/cover change; the area of natural vegetation showed a 4.7% increase, and the area of unused land reduced by 6.8%. Landscape patch size was decreased, the degree of fragmentation and diversity of landscape was increased, and landscape structure in the study area became more complex. Moreover, vegetation coverage promoted from 2001 to 2013; average Fvc in 2013 was 1.5 times greater than that in 2001. The results can provide not only an accurate assessment for the EWDP, but also a visual insight for the water resources management practices in the study area, such that the sustainability for local ecosystem can be facilitated.     

测量的区域土地覆盖格局研基于多尺度遥感究

利用1km、4km和8km 3种空间分辨率的NOAA/AVHRR数字影像,对中国NECT样带西部地区进行了土地覆盖分类及其景观特征的比较研究。重点比较了几种空间分辨率遥感数据分类结果边界的一致性和空间差异,以及影像所记录的景观格局的差异。为进一步在不同尺度上研究景观变化过程以及尺度转换研究奠定了基础。研究表明：3种空间分辨率的遥感影像所反映的区域土地覆盖的宏观空间格局是一致的,但类型的边界、每一类型斑块的形状和数量均产生较大的差异;经过对反映景观空间结构的4种指标（分维数、破碎度、多样性、优势度）的比较显示出随着遥感影像空间分辨率的变化,影像所反映的景观结构发生了较大的变化。其中,各覆盖类型的分维数表现出最大差异,表征着空间分辨率的变化对斑块复杂程度的影响最大。     

Biotic factors and occurrence of Lutzomyia longipalpis in endemic area of visceral leishmaniasis, Mato Grosso do Sul, Brazil

The relationships between environmental exposure to risk agents and health conditions have been studied with the aid of remote sensing imagery, a tool particularly useful in the study of vegetation cover. This study aims to evaluate the influence of environmental variables on the spatial distribution of the abundance of Lutzomyia longipalpis and the reported canine and human visceral leishmaniasis (VL) cases at an urban area of Campo Grande, state of Mato Grosso do Sul. The sandfly captures were performed in 13 residences that were selected by raffle considering four residences or collection station for buffer. These buffers were generated from the central house with about 50, 100 and 200 m from it in an endemic area of VL. The abundance of sandflies and human and canine cases were georreferenced using the GIS software PCI Geomatica. The normalized difference vegetation index (NDVI) and percentage of land covered by vegetation were the environmental variables extracted from a remote sensing IKONOS-2 image. The average NDVI was considered as the complexity of habitat and the standard deviation as the heterogeneity of habitat. One thousand three hundred sixty-seven specimens were collected during the catch. We found a significant positive linear correlation between the abundance of sandflies and the percentage of vegetation cover and average NDVI. However, there was no significant association between habitat heterogeneity and the abundance of these flies.     

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。     

黔中喀斯特山地城市土地利用/覆被变化及其生态效应评价——以贵阳市花溪区为例

快速城市化发展对脆弱喀斯特山地城市生态环境造成严重威胁,系统监测评价城市土地利用/覆被格局变化及其生态效应,协调生态保护与城市发展的关系是新时期喀斯特山地城市生态文明示范城市建设的重要命题。以贵阳市花溪区为对象,以2013年和2018年Landsat ETM/OLI遥感影像为主要数据源,运用遥感和GIS技术,采用遥感生态指数（Remote Sensing Ecological Index,RSEI）模型,在系统分析研究区土地利用/覆被类型和生态环境质量时空动态变化的基础上,剖析土地利用/覆被变化的生态效应。结果表明：（1）2013-2018年花溪区土地利用/覆被格局发生明显变化,形成以林地、建设用地和耕地3种类型占优的格局态势,以耕地的大量减少（减少约15353.37 hm²）且90%转为建设用地或林地、灌木地为主要特征,并伴有局部林地退化（约2683.80 hm²）的现象;（2）5年间,花溪区生态环境质量呈下降趋势,RSEI从2013年的0.622下降到2018年的0.499,下降约20%,反映植被覆盖度和不透水建设用地的绿度指标和干度指标对花溪区生态环境质量的贡献最大;（3）土地利用/覆被与生态环境质量的分布和变化在空间上基本吻合;林地面积或林地与灌木地面积的增减对生态环境质量的变化具有显著影响,林地或林地与灌木地面积增加10%,可使生态质量好转面积增加约15%-20%,或减少生态质量恶化面积约4%;而林地的退化面积增加10%,可导致生态质量恶化面积增加约14%。研究可为喀斯特山地城市国土空间格局优化、城市生态环境改善、生态文明城市建设提供科学依据。     

Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors

Aim Traditional methodologies of mapping vegetation, as carried out by ecologists, consist primarily of field surveying or mapping from aerial photography. Previous applications of satellite imagery for this task (e.g. Landsat TM and SPOT HRV) have been unsuccessful, as such imagery proved to have insufficient spatial resolution for mapping vegetation. This paper reports on a study to assess the capabilities of the recently launched remote sensing satellite sensor Ikonos, with improved capabilities, for mapping and monitoring upland vegetation using traditional image classification methods. Location The location is Northumberland National Park, UK. Methods Traditional remote sensing classification methodologies were applied to the Ikonos data and the outputs compared to ground data sets. This enabled an assessment of the value of the improved spatial resolution of satellite imagery for mapping upland vegetation. Post‐classification methods were applied to remove noise and misclassified pixels and to create maps that were more in keeping with the information requirements of the NNPA for current management processes. Results The approach adopted herein for quick and inexpensive land cover mapping was found to be capable of higher accuracy than achieved with previous approaches, highlighting the benefits of remote sensing for providing land cover maps. Main conclusions Ikonos imagery proved to be a useful tool for mapping upland vegetation across large areas and at fine spatial resolution, providing accuracies comparable to traditional mapping methods of ground surveys and aerial photography.     

Multidimensional analysis of landscape dynamics in a Central African forest-savannah mosaic

Most landscape cover assessments for conservation programmes rely largely on remote sensing analyses. These analyses, however, neglect how people inhabiting protected zones perceive and structure land cover. Using socio-ecological systems (SES) analysis in a forest-savannah mosaic on the Congo Basin forest edge (Democratic Republic of Congo), we investigated how human practices, landscape perceptions and land use patterns affected forest cover. We conducted remote sensing analysis using a Sentinel-2 satellite image and 187 GPS landmarks, producing a land cover map with 11 classes. Our results yielded an 81.85% correspondence with additional 164 GPS landmarks, a robust score in tropical areas. We conducted 40 individual interviews, eighteen focus group discussion workshops and nine months of participant observation of human practices to identify 19 land units and elaborate a granular SES system structuring the landscape. Integrating local knowledge and practices with general ecological and agronomic processes, we developed a landscape dynamics model revealing progressive forest colonisation of savannahs. Our methods demonstrate that a forest-edge landscape cover evaluation through remote sensing and local knowledge can contribute to finer-grained assessment of land cover and ecosystem services. This assessment can assist conservation efforts by considering local populations' practices on and perceptions of landscape change.     

云南澜沧江流域土地利用和覆盖变化

由人类活动引起的土地利用和地被覆盖的变化是地球上环境变化的主要原因。上世纪90年代是云南省经济发展和环境变化的活跃时期。本文通过现代卫星遥感技术、地理信息系统和其它多学科手段相结合 ,系统调查评估了云南澜沧江流域 1990年至 1998年间土地利用和地被覆盖的变化 ,结果显示流域中林业用地面积从 1990年的 6 4 9%增长到 1998年的6 6 4 % ,但是森林有林地 (郁闭度 >30 %的天然林和人工林 )从 1990年的 5 2 6 %减少到 1998年的 35 3%。并且分析大规模商业性森林砍伐、基础建设、经济作物的种植、刀耕火种、薪材过度采集和砍伐建筑用材等引起了森林的结构的显著变化和退化 ,从而导致了生物多样性的下降。     

Extreme land-cover and biodiversity change as an outcome of land abandonment on a Mediterranean island (eastern Adriatic)

Understanding land abandonment and the resulting changes in land cover provides data for suitable reaction to habitat and species losses this process brings. This study aimed to define land-cover types and their biodiversity, record spatio-temporal changes and detect the trajectories and magnitude of these changes. The study was conducted on the island of Molat in the eastern Adriatic Sea. Land cover was defined for different years (1910, 1959, 1986, 2006) using remote sensing and recent vegetation sampling. The obtained land-cover maps were used for spatio-temporal analysis of land-cover change. Open vegetation units of rocky grassland, mesophilous grasslands and farm land dominated in 1910, covering 90% of the island. The observed changes occurred in trajectories through semi-open towards closed vegetation. In 2006, closed vegetation dominated, covering approximately 70% of the island area. The overall land-cover change was extreme, occurring over 90% of the island surface. Biodiversity analysis was related to the present land-cover types and change trajectories, and showed a considerable decline of species richness towards closed vegetation types. All observed changes were correlated to island depopulation and land abandonment. Re-population and encouragement of agro-pastoral activities should reduce habitat and species loss in the process of secondary succession.     

Forest cover classification and geoinformation modeling of forest regeneration dynamics (Based on the example of the southern part of near-Yenisei Siberia)

An automated conjugate classification of forest growing conditions and forest vegetation has been developed based on the example of the southern part of Yenisei Siberia; maps of potential forest growing conditions and forest-cover regeneration dynamics have been constructed on the basis of the automated recognition of remote sensing data and a system of conjugate analysis of dissimilar data in GIS. The proposed approach makes it possible to promptly create and update forest cover maps, which is particularly important for the vast taiga regions of Siberia.     

石羊河流域土地利用类型景观异质性

在GIS支持下,以TM/ETM遥感影像为数据源,利用人工解译方法得到石羊河流域14类景观,并结合数字高程模型(DEM),参考相关的地理基础图件,应用景观生态学理论和方法,采用景观空间格局指标对石羊河流域荒漠、绿洲为主的生态系统景观异质性做了分析。结果表明:耕地、林地、草地和沙地是主要的景观类型和结构成分,其相对频率平均值都在42%以上;在荒漠景观生态系统中,基质沙地的异质性是均质化方向上的异质性,而在绿洲景观生态系统中,基质林地、草地和耕地的异质性是异质化方向上的异质性;作为廊道的水系景观,其相对频率在荒漠和绿洲生态系统中都是较小的,且斑块和廊道异质性均表现为异质化方向上的异质性。研究结果对干旱内陆河流域土地有效利用及生态环境变化监测研究具有重要意义。     

基于RS和GIS的科尔沁沙地南缘章古台地区土地利用变化及其驱动力

利用遥感影像,借助于GIS技术以及数理统计方法,对章古台地区近20年间土地利用变化及驱动力进行了分析。结果表明:1988-2009年间,章古台地区土地利用构成以耕地和林地为主;土地利用类型变化以林地、耕地和建设用地面积的增加和草地、水域及未利用土地面积的减少为特征,其中,草地、建设用地和未利用土地的年变化率大于其他土地利用类型;土地利用程度综合指数由1988年的235.34增至2009年的251.65;土地类型间的转化以草地、未利用土地向林地、耕地转化和耕地与林地间转化为主,占总转化面积的95.58%。章古台地区土地利用变化受人口、政策和自然因素的综合影响,虽然自然因素是决定土地利用变化的重要因素,但在20年的时间尺度上,人口增长和政策引导则是加剧这种变化的主导因素。