Spatiotemporal evolution of desertification based on integrated remote sensing indices in Duolun County,Inner Mongolia

Desertification causes not only a reduction of vegetation cover and surface moisture but also the expansion of sandy land. Considering that the modified soil adjusted vegetation index (MSAVI) is linearly related to canopy cover, albedo plays a significant role in the surface energy balance, and the sandy feature index (SFI) can identify sandy land. This paper proposed a desertification monitoring index (DMI) based on MSAVI, albedo and SFI to accurately monitor desertification. In addition, three classification methods (quantile grading, the susceptibility index segmentation method, and Jenks Natural Breaks) were used to divide the DMI into non-desertification, mild desertification, moderate desertification, and severe desertification. The relative exponential entropy model was adopted to select the optimal method for desertification classification. Spatiotemporal distributions of desertification in Duolun County were finally observed from 2005 to 2020. The results show that (1) although MSAVI, albedo and SFI have their own advantages in monitoring desertification, a single indicator is not suitable for various land uses. The DMI can combine the advantages of MSAVI, albedo and SFI in desertification monitoring and is more consistent with the real desertification levels. (2) Compared with quantile grading and susceptibility index segmentation methods, Jenks Natural Breaks has the smallest relative exponential entropy (2.124), with an overall classification accuracy of 88.21%, indicating that Jenks Natural Breaks is an optimal method for desertification monitoring. (3) Although approximately 1708.60 km² of desertification areas have improved from 2005 to 2020, accounting for 44.23% of the whole area, the remaining areas are still under fluctuating or degraded conditions. This shows that the local government has made some progress in desertification protection, but the areas under fluctuating or degraded conditions still require protection or management.     

基于NDVI_Ts特征空间的中国土地覆盖分类研究

 归一化植被指数（NDVI）与地表温度（Ts）是描述地表覆盖特征的两个重要参数, 其构成的NDVI_Ts特征空间具有丰富的地学和生态学内涵。该文在NOAA/AVHRR连续时间序列数据反演Ts的基础上,通过主成分分析、非监督分类和基于DEM的分类后处理等方法,以Ts/NDVI为指标对中国土地覆盖进行分类。结果表明,Ts/NDVI对中国较大尺度上不同土地覆盖类型的差异具有较强的敏感性,其对中国土地覆盖分类结果的野外抽样检验精度比传统的单独利用NDVI时间序列进行非监督分类提高了3.3%,Kappa系数提高了0.020 2;在综合其它反映植被特征及其环境的指标（如气候、地形等）的基础上,利用Ts/NDVI将有可能较为准确 地提取中国植被或土地覆盖的信息,有利于对其进行分类和变化监测,具有深远的研究潜力 和应用价值。     

Regression Tree Analysis of satellite and terrain data to guide vegetation sampling and surveys

Abstract. Monitoring of regional vegetation and surface biophysical properties is tightly constrained by both the quantity and quality of ground data. Stratified sampling is often used to increase sampling efficiency, but its effectiveness hinges on appropriate classification of the land surface. A good classification must be sufficiently detailed to include the important sources of spatial variability, but at the same time it should be as parsimonious as possible to conserve scarce and expensive degrees of freedom in ground data. As part of the First ISLSCP (International Satellite Land Surface Climatology Program) Field Experiment (FIFE), we used Regression Tree Analysis to derive an ecological classification of a tall grass prairie landscape. The classification is derived from digital terrain, land use, and land cover data and is based on their association with spectral vegetation indices calculated from single-date and multi-temporal satellite imagery. The regression tree analysis produced a site stratification that is similar to the a priori scheme actually used in FIFE, but is simpler and considerably more effective in reducing sample variance in surface measurements of variables such as biomass, soil moisture and Bowen Ratio. More generally, regression tree analysis is a useful technique for identifying and estimating complex hierarchical relationships in multivariate data sets.     

Linking vegetation type and condition to ecosystem goods and services

Our focus here is on how vegetation management can be used to manipulate the balance of ecosystem services at a landscape scale. Across a landscape, vegetation can be maintained or restored or modified or removed and replaced to meet the changing needs of society, giving mosaics of vegetation types and ‘condition classes’ that can range from intact native ecosystems to highly modified systems. These various classes will produce different levels and types of ecosystem services and the challenge for natural resource management programs and land management decisions is to be able to consider the complex nature of trade-offs between a wide range of ecosystem services. We use vegetation types and their condition classes as a first approximation or surrogate to define and map the underlying ecosystems in terms of their regulating, supporting, provisioning and cultural services. In using vegetation as a surrogate, we believe it is important to describe natural or modified (e.g. agronomic) vegetation classes in terms of structure – which in turn is related to ecosystem function (rooting depth, nutrient recycling, carbon capture, water use, etc.). This approach enables changes in vegetation as a result of land use to be coupled with changes to surface and groundwater resources and other physical and chemical properties of soils.For Australian ecosystems an existing structural classification based on height and cover of all vegetation layers is suggested as the appropriate functional vegetation classification. This classification can be used with a framework for mapping and manipulating vegetation condition classes. These classes are based on the degree of modification to pre-existing vegetation and, in the case of biodiversity, this is the original vegetation. A landscape approach enables a user to visualise and evaluate the trade-offs between economic and environmental objectives at a spatial scale at which the delivery of ecosystem services can meaningfully be influenced and reported. Such trade-offs can be defined using a simple scoring system or, if the ecological and socio-economic data exist in sufficient detail, using process-based models.Existing Australian databases contain information that can be aggregated at the landscape and water catchment scales. The available spatial information includes socio-economic data, terrain, vegetation type and cover, soils and their hydrological properties, groundwater quantity and surface water flows. Our approach supports use of this information to design vegetation management interventions for delivery of an appropriate mix of ecosystem services across landscapes with diverse land uses.     

Land use change has stronger effects on functional diversity than taxonomic diversity in tropical Andean hummingbirds

Land use change modifies the environment at multiple spatial scales, and is a main driver of species declines and deterioration of ecosystem services. However, most of the research on the effects of land use change has focused on taxonomic diversity, while functional diversity, an important predictor of ecosystem services, is often neglected. We explored how local and landscape scale characteristics influence functional and taxonomic diversity of hummingbirds in the Andes Mountains in southern Ecuador. Data was collected in six landscapes along a land use gradient, from an almost intact landscape to one dominated by cattle pastures. We used point counts to sample hummingbirds from 2011 to 2012 to assessed how local factors (i.e., vegetation structure, flowering plants richness, nectar availability) and landscape factors (i.e., landscape heterogeneity, native vegetation cover) influenced taxonomic and functional diversity. Then, we analyzed environment – trait relationships (RLQ test) to explore how different hummingbird functional traits influenced species responses to these factors. Taxonomic and functional diversity of hummingbirds were positively associated with landscape heterogeneity but only functional diversity was positively related to native vegetation coverage. We found a weak response of taxonomic and functional diversity to land use change at the local scale. Environment‐trait associations showed that body mass of hummingbirds likely influenced species sensitivity to land use change. In conclusion, landscape heterogeneity created by land use change can positively influence hummingbird taxonomic and functional diversity; however, a reduction of native vegetation cover could decrease functional diversity. Given that functional diversity can mediate ecosystem services, the conservation of native vegetation cover could play a key role in the maintenance of hummingbird pollination services in the tropical Andes. Moreover, there are particular functional traits, such as body mass, that increase a species sensitivity to land use change.     

Numeric classification as an aid to spectral mapping of vegetation communities

This study demonstrates a vegetation mapping methodology that relates the reflectance information contained in multispectral imagery to traditionally accepted ecological classifications. Key elements of the approach used are (a) the use of cover rather than density or presence/absence to quantify the vegetation, (b) the inclusion of physical components as well as vegetation cover to describe and classify field sites, (c) development of an objective land cover classification from this quantitative data, (d) use of the field sample sites as training areas for the spectral classification, and (e) the use of a discriminant function to effectively tie the two classifications together. Land cover over 39000 ha of Australian chenopod shrubland was classified into nine groups using agglomerative hierarchical clustering, a discriminant function developed to relate cover and spectral classes, and the vegetation mapped using a maximum likelihood classification of multi-date Landsat TM imagery. The accuracy of the mapping was assessed with an independent set of field samples and by comparison with a map of land systems previously interpreted from aerial photography. Overall agreement between the digital classification and the land system map was good. The units that have been mapped are those derived from numeric vegetation classification, demonstrating that accepted ecological methods and sound image analysis can be successfully combined.     

基于长时间序列landsat数据的科尔沁沙地土地利用演变分析

以科尔沁沙地沙丘-草甸过渡带区域主要土地覆被类型为研究对象,以1987-2017年多时相Landsat TM/OLI遥感影像解译分类为基础,参考生态学植被演替研究方法,系统分析研究区30年来的土地利用/覆被动态演变规律,研究结果表明：（1）决策树法在复杂下垫面不同覆被类型的同步识别效果较好,所有影像分类精度均达到88%以上,分类效果较好,其中2017年分类精度最高为95.24%,达到了分类研究的要求;（2）研究区存在着"半灌丛-草甸地-灌丛"的植被结构特征,且整体表现为"南进北退"的变化趋势。结合土地利用动态度分析结果表明人类活动干涉下,研究区整体上遵循了半干旱区植被条件改善的一般规律,侧面反映该研究区域生态环境的持续不稳定性和脆弱性;（3）研究区覆被类型发生变化的总面积达到2623.59 hm²,总变化强度为63.76%。其中正向演替的比例为52.61%,以半灌丛面积的持续减小与沙地草甸面积的持续扩张为主要变化特征。但同时,半灌丛转为沙地的面积为184.95 hm²,表明以放牧为主的研究区同时发生着局部的逆行演变;（4）质心迁移结果反映了1987-2017年间,除人为影响较大的林地、草地以及耕地向北迁移外,其他植被类型的质心都有很明显的南迁,主要植被类型重心迁移距离依次由大到小为耕地 > 半灌丛 > 灌丛 > 沙地草甸 > 湿地草甸 > 林地。研究通过记录科尔沁沙地连续扩展的时空模式,展示了遥感-生态和时间序列影像在30 m分辨率下跟踪土地利用/覆被变化的潜力,为提高干旱半干旱区土地利用情况的动态监测效率,开展土地利用/覆被动态演变研究提供参考。     

Monitoring rapid vegetation succession in estuarine wetland using time series MODIS-based indicators: An application in the Yangtze River Delta area

Frequent and continuous time series is required for the detection of plant phenology and vegetation succession. The launch of novel remote sensor MODIS (moderate resolution imaging spectroradiometer) provided us with an opportunity to make a new trial of studying the rapid vegetation succession in estuarine wetlands. In this study, the spatiotemporal variations of vegetation cover and tidal flat elevation along a transect (covering 6 pixels of MODIS) of an estuarine wetland at Dongtan, Chongming Island, in Yangtze River estuary, China were investigated to assess its rapid vegetation succession and physical conditions. By combining the field data collected, the time series of MODIS-based VIs (vegetation indices), including NDVI (normalized difference vegetation index), EVI (enhanced vegetation index) and MSAVI (modified soil adjusted vegetation index), and a water index, LSWI (land surface water index) were utilized to characterize the rapid vegetation succession between 2001 and 2006. We found that NDVI, EVI and MSAVI exhibited significant spatial and temporal correlations with vegetation succession, while LSWI behaved in a positive manner with surface water and soil moisture along with the successional stages. In order to take the advantages of both VIs and water index, a composite index of VWR (vegetation water ratio) combining LSWI and EVI or MSAVI was proposed in this paper. This index facilitates the identification of vegetation succession by simply comparing the values of VWR at different stages, and therefore it could track vegetation succession and estimate community spread rate. Additionally, this study presented an attempt of using MODIS datasets to monitor the change of tidal flat elevation, which demonstrated a potential remote sensing application in geodesy of coastal and estuarine areas.     

Spatial patterns of the Chaco vegetation of central Argentina: Integration of remote sensing and phytosociology

Abstract. In this study we report the first application of Landsat TM imagery to Chaco vegetation studies at a regional scale in Argentina. We produced a map showing 13 clearly differentiated land‐cover types, and described the composition and structure of the plant communities, in an area of almost 42002 km² in central Argentina. The land‐cover map obtained shows that the Chaco vegetation in central Argentina is highly disturbed. In the lowland part of the area the dominant land‐cover types are largely cultural landscapes and substitute shrublands, which have displaced the original Chaco forests, leaving only small isolated remnants generally confined to sites with some kind of constrain for agriculture. The use of TM images and the multivariate analysis of phytosociological data showed a qualified, high accuracy mapping capability for land‐cover types in the Chaco region (ca. 85% overall accuracy). Our results highlight the utility of TM and field data in a subtropical to warm‐temperate region, which is promising where other ancillary data are not available and a rapid acquisition of reliable vegetation data is required, so constituting a starting point for an imperative and more extensive classification and mapping of the endangered Chaco region.     

Direct and indirect effects of land use on floral resources and flower‐visiting insects across an urban landscape

Although urban areas are often considered to have uniformly negative effects on biodiversity, cities are most accurately characterized as heterogeneous mosaics of buildings, streets, parks, and gardens that include both ‘good’ and ‘bad’ areas for wildlife. However, to date, few studies have evaluated how human impacts vary in direction and magnitude across a heterogeneous urban landscape. In this study, we assessed the distribution of floral resources and flower‐visiting insects across a variety of land uses in New York City. We visited both green spaces (e.g. parks, cemeteries) and heavily developed neighborhood blocks (e.g. with high or low density residential zoning) and used structural equation modeling (SEM) to evaluate the direct and indirect effects of median income, vegetation, and development intensity on floral resources and insects in both settings. Abundance and taxonomic richness of flower‐visiting insects was significantly greater in green spaces than neighborhood blocks. The SEM results indicated that heavily‐developed neighborhoods generally had fewer flower‐visiting insects consistent with reductions in floral resources. However, some low‐density residential neighborhoods maintained high levels of floral resources and flower‐visiting insects. We found that the effects of surrounding vegetation on floral resources, and thus indirect effects on insects, varied considerably between green spaces and neighborhood blocks. Along neighborhood blocks, vegetation consisted of a mosaic of open gardens and sparsely distributed trees and had a positive indirect effect on flower‐visiting insects. In contrast, vegetation in urban green spaces was associated with increased canopy cover and thus had a negative indirect effect on flower‐visiting insects through reductions in floral resources. In both neighborhood blocks and green spaces, vegetation had a positive direct effect on flower‐visiting insects independent of the influence of vegetation on floral resources. Our results demonstrate how inter‐related components of an urban ecosystem can vary with respect to one another across a heterogeneous urban landscape, suggesting that it is inappropriate to generalize about urban systems as a whole without first addressing differences among component land use types.     

城市绿地生态评价研究进展

城市绿地的科学评价为绿地的规划和管理提供了参考,对人们正确认识和改造绿地建设起着重要的作用,为城市绿地生态功能的发挥提供了重要的依据和保障。以绿地结构的评价为基础,总结了城市绿地生态功能、服务评价、健康评价的方法和研究进展;提出了城市绿地生态风险评价的基本方法并概括了城市绿地可持续评价的研究方向。结果显示:当前城市绿地的生态评价在范围上逐渐从微观尺度发展到宏观尺度,日益强调绿地的生态服务功能,重点关注于绿地的碳储量与降温增湿功能与效益评价以及价值化研究;城市绿地的健康评价和可持续性评价均尚处于初级的探索阶段。指出了城市绿地生态评价的关键在于评价结果在管理和规划中的应用,在评价内容上必须关注各部分的结合,在评价方法上应该借助基础研究的长期监测、评价的量化以及多学科交叉的渗透。     

Vegetation characteristics influence fine-scale intensity of habitat use by wild turkey and white-tailed deer in a loblolly pine plantation

Habitat quality is often evaluated based on food availability. However, ecological theory suggests cover should be a more important decision rule when food is not a proximate threat to fitness, as cover mediates predation risk as well as other important factors of fitness. In reality, vegetation characteristics related to food availability and cover are rarely coupled with animal use in the same space and time to determine their relative influences on habitat use. Using an array of 81 camera traps in a matrix of forest management strategies used to deliberately cause a wide disparity in vegetation characteristics, we monitored intensity of use by white-tailed deer (Odocoileus virginianus) and wild turkey (Meleagris gallopavo). We measured vegetation characteristics related to food and cover at each camera trap location then used a generalized additive model to determine how vegetation characteristics specific to the location affected intensity of habitat use by animals at the location. Consistent among both species, cover best explained intensity of habitat use. Contrastingly, food did not explain intensity of habitat use well for either species. Some vegetation simultaneously provides cover and food, and our data indicate that areas with vegetation characteristics providing both resources had the greatest influence on intensity of habitat use by both species. Our results suggest deer and turkey may perceive cover as a more important habitat component when food is not a proximate fitness threat.     

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。     

Automatic detection of woody vegetation in repeat landscape photographs using a convolutional neural network

Repeat photography is an efficient method for documenting long-term landscape changes. So far, the usage of repeat photographs for quantitative analyses is limited to approaches based on manual classification. In this paper, we demonstrate the application of a convolutional neural network (CNN) for the automatic detection and classification of woody regrowth vegetation in repeat landscape photographs. We also tested if the classification results based on the automatic approach can be used for quantifying changes in woody vegetation cover between image pairs. The CNN was trained with 50 × 50 pixel tiles of woody vegetation and non-woody vegetation. We then tested the classifier on 17 pairs of repeat photographs to assess the model performance on unseen data. Results show that the CNN performed well in differentiating woody vegetation from non-woody vegetation (accuracy = 87.7%), but accuracy varied strongly between individual images. The very similar appearance of woody vegetation and herbaceous species in photographs made this a much more challenging task compared to the classification of vegetation as a single class (accuracy = 95.2%). In this regard, image quality was identified as one important factor influencing classification accuracy. Although the automatic classification provided good individual results on most of the 34 test photographs, change statistics based on the automatic approach deviated from actual changes. Nevertheless, the automatic approach was capable of identifying clear trends in increasing or decreasing woody vegetation in repeat photographs. Generally, the use of repeat photography in landscape monitoring represents a significant added value to other quantitative data retrieved from remote sensing and field measurements. Moreover, these photographs are able to raise awareness on landscape change among policy makers and public as well as they provide clear feedback on the effects of land management.     

Can ecological land classification increase the utility of vegetation monitoring data?

Vegetation dynamics in rangelands and other ecosystems are known to be mediated by topoedaphic properties. Vegetation monitoring programs, however, often do not consider the impact of soils and other sources of landscape heterogeneity on the temporal patterns observed. Ecological sites (ES) comprise a land classification system based on soil, topographic, and climate variations that can be readily applied by land managers to classify topoedaphic properties at monitoring locations. We used a long-term (>40 y) vegetation record from southeastern Arizona, USA to test the utility of an ES classification for refining interpretations of monitoring data in an area of relatively subtle soil differences. We focused on two phenomena important to rangeland management in the southeastern Arizona region: expansion of the native tree velvet mesquite (Prosopis velutina Woot.) and spread of the introduced perennial grass Lehmann lovegrass (Eragrostis lehmanniana Nees). Specifically, we sought to determine if a quantitative, ES-specific analysis of the long-term record would (1) improve detection of changes in plant species having heightened ecological or management importance and (2) further clarify topoedaphic effects on vegetation trajectories. We found that ES class membership was a significant factor explaining spatiotemporal variation in velvet mesquite canopy cover, Lehmann lovegrass basal cover, and Lehmann lovegrass density measurements. In addition, we observed that the potential magnitude of velvet mesquite and Lehmann lovegrass increases varied substantially among ES classes. Our study brings attention to a practical land management tool that might be called upon to increase the effectiveness of vegetation-based indicators of ecosystem change.     

Spatial genetic structure and landscape connectivity in black bears: Investigating the significance of using different land cover datasets and classifications in landscape genetics analyses

Landscape genetic analyses allow detection of fine‐scale spatial genetic structure (SGS) and quantification of effects of landscape features on gene flow and connectivity. Typically, analyses require generation of resistance surfaces. These surfaces characteristically take the form of a grid with cells that are coded to represent the degree to which landscape or environmental features promote or inhibit animal movement. How accurately resistance surfaces predict association between the landscape and movement is determined in large part by (a) the landscape features used, (b) the resistance values assigned to features, and (c) how accurately resistance surfaces represent landscape permeability. Our objective was to evaluate the performance of resistance surfaces generated using two publicly available land cover datasets that varied in how accurately they represent the actual landscape. We genotyped 365 individuals from a large black bear population (Ursus americanus) in the Northern Lower Peninsula (NLP) of Michigan, USA at 12 microsatellite loci, and evaluated the relationship between gene flow and landscape features using two different land cover datasets. We investigated the relative importance of land cover classification and accuracy on landscape resistance model performance. We detected local spatial genetic structure in Michigan''s NLP black bears and found roads and land cover were significantly correlated with genetic distance. We observed similarities in model performance when different land cover datasets were used despite 21% dissimilarity in classification between the two land cover datasets. However, we did find the performance of land cover models to predict genetic distance was dependent on the way the land cover was defined. Models in which land cover was finely defined (i.e., eight land cover classes) outperformed models where land cover was defined more coarsely (i.e., habitat/non‐habitat or forest/non‐forest). Our results show that landscape genetic researchers should carefully consider how land cover classification changes inference in landscape genetic studies.     

基于TM遥感影像的陕北黄土区结构化植被因子指数提取

根据结构化植被因子指数的概念,以TM影像为信息源,探讨了利用遥感技术提取陕北黄土区结构化植被因子指数（C_s）的途径与方法.结果表明：在陕北黄土区,C_s能更好地描述植被群落的水土保持效益,其与绿度植被指数（归一化植被指数NDVI、修正土壤调整植被指数MSAVI）和黄度植被指（归一化差异衰败指数NDSVI、归一化耕作指数NDTI）等单一的遥感植被指数虽然均存在良好的相关关系,但用绿度与黄度植被指数相结合可综合反映植被的水土保持功能,能较好地克服单一指数在描述植被控制水土流失中的不足;MSAVI、NDTI分别是基于遥感影像提取C_s较为理想的绿度和黄度植被指数;根据群落结构化植被因子指数与遥感植被指数的关系推算区域尺度上的结构化植被因子指数是可行的,但由于不同地区植物物候期的差异,要使该方法在其他地区适用,仍需开展相应的率定和验证工作.     

长三角地区植被覆盖演变城乡差异及其原因

城市生态环境和城市化之间的关系是城市可持续发展的关键。研究不同城市化水平下植被覆盖的长时间演变趋势,对理解城市化过程对植被生长动态的影响,城市更新以及推进城市绿化的科学管理具有重要意义。然而,目前对城市内部沿城乡梯度植被生长趋势差异的认识还比较有限。以我国城市化发展最为强烈的长三角地区为研究对象,基于2000-2020年长三角归一化植被指数数据,采用趋势分析和地理探测器方法,探究了长三角地区城市内部植被覆盖演变城乡差异,并从土地利用/覆被变化和城市化发展的角度解析其成因。研究结果表明：（1）2000-2020年长三角地区植被总体呈绿化趋势,植被明显绿化占最大比例（52.06%）,轻微绿化与稳定不变地区占31.68%,零星分布的褐化区占6.82%。（2）城市老城区植被覆盖变化总体呈现返绿趋势（0.016/10 a）,新城区褐化明显（-0.019/10 a）,农郊区绿化突出（0.023/10 a）。在上海、南京和杭州等人口城市化水平较高的城市中,老城区土地利用变化强度最高,其绿化趋势也最高,体现了城市更新过程对绿地空间的促进作用;而在人口城市化水平相对较低的宣城、蚌埠和阜阳等城市,土地利用变化强度相对较低的农郊区也呈现明显的绿化趋势,更多的是受到区域生态保护的影响。（3）土地城市化是长三角地区老城区和新城区植被覆盖变化的主导因子,而城市化因子对农郊区解释程度总体不显著。从长三角总体区域看,城镇人口比重、不透水面积/总面积以及地区生产总值三者对植被覆盖演变存在显著影响,其中城镇人口比重影响最大。     

Comparison of Vegetation Indices and Red-edge Parameters for Estimating Grassland Cover from Canopy Reflectance Data

There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the canopy scale. In this paper, the percentage of vegetation cover was estimated from vegetation indices using Moderate Resolution Imaging Spectroradiometer （MODIS） data and red-edge parameters through the first derivative spectrum from in situ hypserspectral reflectance data. Hyperspectral reflectance measurements were made on grasslands in Inner Mongolia, China, using an Analytical Spectral Devices spectroradiometer. Vegetation indices such as the difference, simple ratio, normalized difference, renormalized difference, soil-adjusted and modified soil-adjusted vegetation indices （DVI, RVI, NDVI, RDVI, SAVI L=0.5 end MSAVI2） were calculated from the hyperspectral reflectance of various vegetation covers. The percentage of vegetation cover was estimated using an unsupervised spectral-contextual classifier automatically. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that MSAVI2 and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression, respectively.     

云南干热河谷植被与环境研究进展

特殊的地理位置和独特的地形地貌特征组合形成了典型的干热气候环境, 在云南省亚热带高原山地河谷下部发育了一类特有的植被类型, 即干热河谷植被。干热河谷植被具有非地带性和稀有性, 以及由土地利用变化为主的人为活动干扰导致的脆弱性。本文回顾了干热河谷植被的研究历史, 分别从干热河谷的植物群落学和植物区系学、干热河谷植被与土地的关系以及干热河谷植被保护与恢复三个方面进行了总结。植物群落与区系研究主要集中于群落分类、植被分类、群落特征、人为干扰影响、区系特征、性质和起源; 植被与土地关系研究侧重于土壤特性、土地利用/覆盖变化、土地退化及水土流失状况; 植被保护与恢复的热点在植被恢复目标、植被恢复功能区划、植被恢复引种及筛选及植被恢复效益评价研究。未来在这些区域应注重自然灾害及预防、水电工程建设对植被的影响及其响应等方面的研究, 深入开展大尺度植被时空格局的监测和动态服务功能分析。