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。     

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。     

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。     

北京海淀区植被覆盖的遥感动态研究

 植被覆盖度fg人(植被的垂直投影面积与单位面积之比)是一个十分重要的生态气候参数。为了有效地从遥感资料中提取植被覆盖度,发展了一套计算区域植被覆盖度的亚象元分解模型法。运用该方法对北京市海淀区1975、1991和1997年的植被覆盖度进行了计算,并在此基础上,求得研究区不同植被覆盖等级的变化转移矩阵,分析了海淀区22年来植被覆盖等级变化的空间过程和变化趋势。     

基于地形限制特征的泾河流域遥感地表覆被分类

由于在分类方法和空间分辨率等方面存在局限性,基于粗分辨率遥感数据的传统非监督分类结果在不同地物过渡带内往往误差较大。该文提出了基于地形限制特征的分类方法,在非监督分类的基础上,将非监督分类结果按照像元进行细分,并运用地形限制条件对细分后的像元进行二次判别分类。结果表明,分类精度明显提高,其中,农田和居民点分类精度的提高最为明显。这一方法使得完全同质的单元可以进行属性的变更,改善了像元空间分辨率差造成的误差;而地形限制特征的引入减少了传统非监督分类的不确定性,使模糊区域的分类有了较为明确的区分特征,提高了分类的精度。     

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.     

Mountain vegetation change quantification using surface landscape metrics in Lancang watershed,China

Land cover and vegetation change are among the most important aspects of environmental change. Vegetation change can be quantified by landscape pattern indices (LPI). Landscape indices are routinely calculated using planar land use/land cover (LU/LC) maps, obtained by the projection of a non-flat landscape surface into a two-dimensional Cartesian space. Especially in mountainous areas, quantification on planar maps can lead to underestimation of vegetation and land cover changes. Hoechstetter et al. (2008) developed a method to compute LPIs in a surface structure by calculating landscape patch surface area and surface perimeter from digital elevation models (DEM). As yet there have been no applications of these surface landscape indices on land use/land cover and vegetation change quantification. The objectives of this study are to (1) choose a LPI method (surface metrics pattern analysis or common planimetric metrics pattern analysis) for vegetation change quantification; and (2) employ the selected surface LPI method to assess vegetation pattern change in two mountainous areas of the Lancang watershed, Yunnan Province, China. The results show that the surface approach to estimate changes of class area (CA), mean patch area (MPA), and mean Euclidean Near-Neighbor distance (MENN) may obtain more accurate results for quantifying vegetation change in steep mountain areas. Forest fragmentation increased significantly over time in the two different mountainous study areas. The patches of two land cover classes, (i) agricultural land and (ii) low density forest and tall shrubs, became more aggregated in the northern (temperate) study area. In the southern (tropical) study area, rubber plantations increased considerably in size and became more aggregated.     

Ecosystem classification and inventory maps as surrogates for ground beetle assemblages in boreal forest

Aims We compare performance of ecosystem classification maps and provincial forest inventory data derived from air photography in reflecting ground beetle (Coleoptera: Carabidae) biodiversity patterns that are related to the forest canopy mosaic. Our biodiversity surrogacy model based on remotely sensed tree canopy cover is validated against field-collected ground data.Methods We used a systematic sampling grid of 198 sites, covering 84 km 2 of boreal mixedwood forest in northwestern Alberta, Canada. For every site, we determined tree basal area, characterized the ground beetle assemblage and obtained corresponding provincial forest inventory and ecosystem classification information. We used variation partitioning, ordination and misclassification matrices to compare beetle biodiversity patterns explained by alternative databases and to determine model biases originating from air photo-interpretation.Important findings Ecosystem classification data performed better than canopy cover derived from forest inventory maps in describing ground beetle biodiversity patterns. The biodiversity surrogacy models based on provincial forest inventory maps and field survey generally detected similar patterns but inaccuracies in air photo-interpretation of relative canopy cover led to differences between the two models. Compared to field survey data, air photo-interpretation tended to confuse two Picea species and two Populus species present and homogenize stand mixtures. This generated divergence in models of ecological association used to predict the relationship between ground beetle assemblages and tree canopy cover. Combination of relative canopy cover from provincial inventory with other geo-referenced land variables to produce the ecosystem classification maps improved biodiversity predictive power. The association observed between uncommon surrogates and uncommon ground beetle species emphasizes the benefits of detecting these surrogates as a part of landscape management. In order to complement conservation efforts established in protected areas, accurate, high resolution, wide ranging and spatially explicit knowledge of landscapes under management is primordial in order to apply effective biodiversity conservation strategies at the stand level as required in the extensively harvested portion of the boreal forest. In development of these strategies, an in-depth understanding of vegetation is key.     

Map Misclassification Can Cause Large Errors in Landscape Pattern Indices: Examples from Habitat Fragmentation

Although habitat fragmentation is one of the greatest threats to biodiversity worldwide, virtually no attention has been paid to the quantification of error in fragmentation statistics. Landscape pattern indices (LPIs), such as mean patch size and number of patches, are routinely used to quantify fragmentation and are often calculated using remote-sensing imagery that has been classified into different land-cover classes. No classified map is ever completely correct, so we asked if different maps with similar misclassification rates could result in widely different errors in pattern indices. We simulated landscapes with varying proportions of habitat and clumpiness (autocorrelation) and then simulated classification errors on the same maps. We simulated higher misclassification at patch edges (as is often observed), and then used a smoothing algorithm routinely used on images to correct salt-and-pepper classification error. We determined how well classification errors (and smoothing) corresponded to errors seen in four pattern indices. Maps with low misclassification rates often yielded errors in LPIs of much larger magnitude and substantial variability. Although smoothing usually improved classification error, it sometimes increased LPI error and reversed the direction of error in LPIs introduced by misclassification. Our results show that classification error is not always a good predictor of errors in LPIs, and some types of image postprocessing (for example, smoothing) might result in the underestimation of habitat fragmentation. Furthermore, our results suggest that there is potential for large errors in nearly every landscape pattern analysis ever published, because virtually none quantify the errors in LPIs themselves.     

Mapping tropical forest fractional cover from coarse spatial resolution remote sensing imagery

At regional to global scales the only feasible approach to mapping and monitoring forests is through the use of coarse spatial resolution remotely sensed imagery. Significant errors in mapping may arise as such imagery may be dominated by pixels of mixed land cover composition which cannot be accommodated by conventional mapping approaches. This may lead to incorrect assessments of forest extent and thereby processes such as deforestation which may propagate into studies of environmental change. A method to unmix the class composition of image pixels is presented and used to map tropical forest cover in part of the Mato Grosso, Brazil. This method is based on an artificial neural network and has advantages over other techniques used in remote sensing. Fraction images depicting the proportional class coverage in each pixel were produced and shown to correspond closely to the actual land cover. The predicted and actual forest cover were, for instance, strongly correlated (up to r = 0.85, significant at the 99% level of confidence) and the predicted extent of forest over the test site much closer to the actual extent than that derived from a conventional approach to mapping from remotely sensed imagery.     

基于中高分辨率遥感的植被覆盖度时相变换方法

植被覆盖度是衡量地表植被状况、指示生态环境变化的一个重要指标,也是许多学科的重要参数。传统的测量方法难以获取时间连续的面状数据,且耗时、耗力,很难大范围推广。遥感估算方法虽然可以弥补传统方法的不足,但由于云覆盖等天气条件的影响,获得同一时相覆盖整个研究区的遥感影像非常困难,时相的差异必然导致研究结果产生误差。针对植被覆盖度这一重要生态参数,结合低分辨率遥感数据的时间优势和中高分辨率遥感数据的空间优势,提出一种时相变换方法,将源于中高分辨率影像的植被覆盖度变换到研究需要的时相上。首先,利用像元二分模型计算MODIS尺度的时间序列植被覆盖度,并利用已经获得的SPOT影像计算其获取时相上的植被覆盖度;其次,利用土地利用图划分植被覆盖类型,并利用MODIS数据和土地利用数据之间的空间对应关系制作MODIS像元内各类植被覆盖的面积百分比数据;再次,利用面积百分比数据提取各类植被覆盖的纯像元,结合MODIS植被覆盖度时间序列,从而提取各类植被覆盖纯像元的植被覆盖度时间序列曲线;最后利用像元分解的方法提取MODIS像元内各类植被覆盖组分的植被覆盖度的变化规律,将其应用到该组分对应位置上SPOT像元的植被覆盖度上,从而将其变换到所需要的时相上。在密云水库上游进行试验,将覆盖研究区的10景SPOT5多光谱影像计算的植被覆盖度统一变换到7月上旬,结果显示:视觉效果上明显好转,且空间上连续一致;变换前后植被覆盖度的统计量对比结果也符合植被生长规律;利用外业样点数据与对应位置的植被覆盖度变换结果进行回归分析,结果发现各植被覆盖类型的R2均在0.8左右,表明变换结果与实测值非常接近,时相变换的效果较好,从而可以很好地促进相关研究精度的提高。     

基于高分遥感影像的滇西北村域景观格局演变——以大理市低丘缓坡山地开发区为例

人类的开发活动是造成土地覆盖和景观格局变化的主要原因.村域尺度上高强度的人类开发活动对土地覆盖及景观格局演变的影响规律研究尚不多见.本研究采用2009年的GeoEye-1数据和2014年的WorldView 3数据,利用ArcGIS和ENVI,基于面向对象和人机交互的方法解译影像,应用土地利用转移矩阵和景观指数定量研究大理市海东镇低丘缓坡山区改造过程中的土地覆盖变化和景观格局演变.结果表明: 2009年主要土地覆盖类型是林地、水田和旱地,占总面积的82.8%,2014年林地、推平未建地和水田占总面积的70.9%;研究期间,土地利用变化主要由林地、水田和旱地向推平未建地、建设用地转移,尤其是2014年推平未建地面积达531.57 hm²,其中,来自林地、旱地和水田的面积分别占42.8%、21.7%和14.2%.景观空间格局演变表现为斑块数量和密度增加,平均斑块面积变小;边缘指数和形状指数增加,斑块形状更加复杂;斑块破碎化,整体构成更加多样化.     

Leakiness: A new index for monitoring the health of arid and semiarid landscapes using remotely sensed vegetation cover and elevation data

The health of arid and semiarid lands needs to be monitored, particularly if they are used to produce food and fiber, and are prone to loss of vegetation cover and soil. Indicators of landscape health based on remotely sensed data could cost-effectively integrate structural and functional attributes of land surfaces across a range of scales. In this paper, we describe a new index for remotely monitoring changes in the health of land. The new index takes important aspects of landscape structure and function into account by focusing on the potential for landscapes to lose or ‘leak’ (not retain) soil sediments. We combined remotely sensed vegetation patchiness data with digital elevation model (DEM) data to derive a quantitative metric, the landscape leakiness index, LI. This index is strongly linked to landscape function by algorithms that reflect the way in which spatial configuration of vegetation cover and terrain affect soil loss. Linking LI to landscape function is an improvement on existing indicators that are based on qualitatively assessing remotely sensed changes in vegetation cover. Using archived Landsat imagery and Shuttle Radar Topography Mission DEMs, we found for example that LI indicated improvements in the condition or health of a rangeland paddock that was monitored from 1980 to 2002. This paddock is located in central Australia and its improved health is documented by photographs and field data. Although the full applicability of LI remains to be explored, we have demonstrated that it has the potential to serve as a useful ecological indicator for monitoring the health of arid and semiarid landscapes.     

Calculating landscape diversity with information-theory based indices: A GRASS GIS solution

The assessment of species diversity in relatively large areas has always been a challenging task for ecologists, mainly because of the intrinsic difficulty to judge the completeness of species lists and to undertake sufficient and appropriate sampling. Since the variability of remotely sensed signal is expected to be related to landscape diversity, it could be used as a good proxy of diversity at species level.It has been demonstrated that the relation between species and landscape diversity measured from remotely sensed data or land use maps varies with scale. However, Free and Open Source tools (allowing an access to the source code) for assessing landscape diversity at different spatial scales are still lacking today. In this paper, we aim at: i) providing a theoretical background of the mostly used diversity indices stemmed from information theory that are commonly applied to quantify landscape diversity from remotely sensed data and ii) proposing a free and robust Open Source tool (r.diversity) with its source code for calculating diversity indices (and allowing an easy potential implementation of new metrics by multiple contributors globally) at different spatial scales from remotely-sensed imagery or land use maps, running under the widely used Open Source program GRASS GIS.r.diversity can be a valuable tool for calculating landscape diversity in an Open Source space given the availability of multiple indices at multiple spatial scales with the possibility to create new indices directly reusing the code.We expect that the subject of this paper will stimulate discussions on the opportunities offered by Free and Open Source Software to calculate landscape diversity indices.     

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。     

Object-oriented methods for habitat mapping at multiple scales – Case studies from Northern Germany and Wye Downs, UK

This paper presents an application of object-oriented techniques for habitat classification based on remotely sensed images and ancillary data. The study reports the results of habitat mapping at multiple scales using Earth Observation (EO) data at various spatial resolutions and multi temporal acquisition dates. We investigate the role of object texture and context in classification as well as the value of integrating knowledge from ancillary data sources. Habitat maps were produced at regional and local scales in two case studies; Schleswig-Holstein, Germany and Wye Downs, United Kingdom. At the regional scale, the main task was the development of a consistent object-oriented classification scheme that is transferable to satellite images for other years. This is demonstrated for a time series of Landsat TM/ETM+ scenes. At the local scale, investigations focus on the development of appropriate object-oriented rule networks for the detailed mapping of habitats, e.g. dry grasslands and wetlands using very high resolution satellite and airborne scanner images. The results are evaluated using statistical accuracy assessment and visual comparison with traditional field-based habitat maps. Whereas the application of traditional pixel-based classification result in a pixelised (salt and pepper) representation of land cover, the object-based classification technique result in solid habitat objects allowing easy integration into a vector-GIS for further analysis. The level of detail obtained at the local scale is comparable to that achieved by visual interpretation of aerial photographs or field-based mapping and also retains spatially explicit, fine scale information such as scrub encroachment or ecotone patterns within habitats.     

Are CORINE land cover classes reliable proxies of plant species assemblages? A test in Mediterranean forest landscapes

In land cover mapping, the complexity of landscapes is fitted into classes that may limit the recognition of natural variability. In this study, we tested the power of land cover classes (defined on the CORINE land cover classification scheme, a standardized legend set by EU for land cover inventory) to separate different vascular plant assemblages in forest ecosystems. In order to separately identify the role of different sources of inconsistency between land cover classes and species composition, we compared three different inventory processes, based on (i) dominant tree species as observed in the field, (ii) visual interpretation of remotely sensed images and (iii) semi-automatic supervised classification of satellite images. Our results underline that classifying forest ecosystems on the basis of their canopy species produces an over-simplification of habitat variability. Consequently, land cover maps based on non-specialized classification schemes should not be regarded as good proxies for plant biodiversity. If land cover maps are intended to describe and manage landscapes and their associated biodiversity, it is necessary to improve their capacity to represent the complexity of ecosystems.     

区域城市扩张对森林景观破碎化的影响——以粤港澳大湾区为例

森林生境丧失与景观破碎化是引起生物多样性下降,生态系统功能降低的重要原因。量化森林景观破碎化的时空特征及其与城市扩张格局的关系是开展区域生态修复与功能提升的重要基础。本文以快速城市化的典型区域——粤港澳大湾区为研究对象,基于遥感解译的1980年、1990年、2000年、2010年和2018年土地覆盖/利用专题图,通过多尺度的景观格局分析和统计分析,定量解析森林景观破碎化的时空演变特征及其与城市扩张格局间的关系。研究结果显示：1）1980-2018年,大湾区林地覆盖面积缩减1,274 km²,林地转变为建设用地的面积占林地丧失总面积的比例从1980-1990年的11%增长至2010-2018年的42%,表明城市扩张已成为林地丧失的主导因素;2）森林景观破碎化程度加剧,表现为林地斑块密度提高,平均斑块面积减小,但破碎类型与程度具有地域差异;3）城市扩张幅度与空间格局显著影响林地破碎化,其中,城市扩张幅度对林地破碎化的影响更为重要。基于森林景观破碎化与城市扩张的现状,落实城市增长边界划定、关键斑块-廊道识别与生态网络构建等措施,有助于保护与连通重要生态空间,保障和提升生态功能。     

Seeking parsimony in landscape metrics

Numerous metrics describing landscape patterns have been used to explain landscape-scale habitat selection by birds. The myriad metrics, their complexity, and inconsistent responses to them by birds have led to a lack of clear recommendations for managing land for desired species. The amount of a target land cover type in the landscape (percentage cover) often has been a useful indicator of the likelihood of species occurrence or of habitat selection; is it also a more adequate and parsimonious measure for explaining species distributions than patch size or more complex measures of landscape configuration? We examined responses of 6 woodland-interior bird species to the percentage tree cover within prescribed areas and to patch size, edge density, and other metrics. We examined responses in 2 landscapes: a mixed woodland-savanna and an eastern deciduous forest. For these 6 species, percentage tree cover explained bird occurrence as well as or better than other measures in both study areas. We then repeated the analysis on a larger group of woodland species, including those associated with woodland edges. The bird species we studied had varied responses to landscape metrics, but percentage tree cover was the strongest explanatory variable overall. Although percentage cover estimated from remotely sensed data is an inexact representation of habitat in the landscape, it does appear to be reliable and easy to conceptualize, relative to other measures. We suggest that, at least for woodland habitat, percentage cover is a broadly useful measure that can be helpful in pragmatic questions of explaining responses to landscapes or in anticipating responses to landscape change. © 2011 The Wildlife Society.     

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.