基于遥感和GIS 的秦岭山系大熊猫生境评价

大熊猫生境状况关系到大熊猫的长期生存与繁衍, 从大尺度来评价大熊猫生境状况有利于生境的保护与保护区的规划与建设, 从而有利于大熊猫的保护。在大熊猫生物学与行为生态学的研究成果基础上, 通过广泛的野外调查, 在地理信息系统(GIS) 和遥感(RS) 技术支持下, 利用大熊猫生境结构理论模型, 选取海拔、坡度、植被类型、竹子分布等评价因子, 系统地研究了秦岭山系大熊猫生境的分布、生境质量与空间格局、以及生境保护现状, 并提出了相应的生境保护对策。研究表明:①秦岭山系的大熊猫生境面积约为44 万hm 2, 80% 的生境分布于海拔1 500～ 2 400 m 之间; ②由于交通、河流以及沿途的开发建设, 整个生境被分为大小不等的若干部分; ③当前的大熊猫保护区与拟建的保护区及走廊带之间互相连接, 形成了一个较为完整的保护区体系, 保护了70% 以上的大熊猫生境。研究结果能为该山系的大熊猫生境保护提供依据。     

基于种群动态膜系统的大熊猫种群模型

大熊猫谱系数据是研究大熊猫种群动态性的重要数据基础,因此从保护大熊猫的角度考虑,大熊猫生态系统的数据建模具有重要意义。针对该问题提出了一种使用种群动态膜系统对大熊猫生态系统进行数据建模的方法。基于中国动物园协会已发布的大熊猫谱系数据从大熊猫个体行为上来模拟研究中国大熊猫保护研究中心圈养大熊猫种群特征,详细分析繁殖参数的变化规律并加入野放模块,设计了一个符合其特点的具有两层嵌套膜结构、对象集合和一系列进化规则的种群动态膜系统。在该膜系统的仿真实验中得到的全体大熊猫仿真结果与实际数据的相对误差最大不超过±4.13%,基本控制在±2.7%以内。实验结果验证了该计算模型的有效性和正确性,能够模拟预测大熊猫种群的变化趋势,给管理者的决策提供依据。     

The application of a binary division procedure to the classification of forest subcategories using MODIS time-series data during 2000–2010 in China

Forests account for more than 23% of China’s total area. As the most important terrestrial ecosystem, forests have tremendous ecological value. However, it remains difficult to classify forest subcategories at the national scale. In this study, a newly developed binary division procedure was used to categorize forest areas, including their spatiotemporal dynamics, during the period 2000–2010. Time-series images acquired using the Moderate Resolution Imaging Spectroradiometer (MODIS), together with auxiliary data on land use, climate zoning, and topography, were utilized. Hierarchical classification and zoning were combined with remote-sensing auto-classification. Based on the forest extent mask, the state-level forest system was divided into four classes and 18 subcategories. The method achieved an acceptable overall accuracy of 73.1%, based on a comparison to the sample points of China’s fourth forest general survey data set. In 2010, the total forest area was 1.755 × 10⁶ km², and the total area of and shrubs was 4.885 × 10⁵ km². The total area of woodland increased by 2536.25 km² during the decade 2000–2010. The shrub subcategories exhibited almost no change during this time period; however, significant changes in forest area occurred in the mountainous region of Northeast China as well as in the hilly regions of Southern China. The main transformations took place in cold-temperate and temperate mountainous deciduous coniferous forest, subtropical deciduous coniferous forest, subtropical evergreen coniferous forest, and temperate and subtropical deciduous broadleaved mixed forests. The binary division procedure proposed herein can be used not only to rapidly classify more forest subcategories and monitor their dynamic changes, but also to improve the classification accuracy compared with global and national land-cover maps.     

祁连山区植被净初级生产力的空间变化特征分析

基于MODIS数据分析了2000~2010年祁连山区植被净初级生产力(Net Primary Productivity,NPP)的空间变化特征。结果显示:祁连山区植被NPP并不高,多年平均植被NPP仅为121.95gC/(m2·a),自东向西植被NPP逐渐减少。不同植被类型其NPP具有明显差异,大体上为:常绿阔叶林平原草地常绿针叶林典型草地农田高寒草甸草地荒漠草地落叶针叶林。祁连山区植被NPP变化在区域间也存在差异。植被NPP呈增长趋势的地区主要分布在青海年南山、拉脊山、达坂山和青海湖及其西侧,约占47.30%;乌鞘岭东部及以东的地区(约占1.97%)植被NPP呈减少趋势。降水是祁连山区植被NPP变化的主要因素,气温对植被NPP的影响并不明显,不合理的人类活动可能是造成部分区域植被NPP减少的重要原因。     

Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data

A snow-cover mapping method accounting for forests (SnowFrac) is presented. SnowFrac uses spectral unmixing and endmember constraints to estimate the snow-cover fraction of a pixel. The unmixing is based on a linear spectral mixture model, which includes endmembers for snow, conifer, branches of leafless deciduous trees and snow-free ground. Model input consists of a land-cover fraction map and endmember spectra. The land-cover fraction map is applied in the unmixing procedure to identify the number and types of endmembers for every pixel, but also to set constraints on the area fractions of the forest endmembers. SnowFrac was applied on two Terra Moderate Resolution Imaging Spectroradiometer (MODIS) images with different snow conditions covering a forested area in southern Norway. Six experiments were carried out, each with different endmember constraints. Estimated snow-cover fractions were compared with snow-cover fraction reference maps derived from two Landsat Enhanced Thematic Mapper Plus (ETM+) images acquired the same days as the MODIS images. Results are presented for non-forested areas, deciduous forests, coniferous forests and mixed deciduous/coniferous forests. The snow-cover fraction estimates are enhanced by increasing constraints introduced to the unmixing procedure. The classification accuracy shows that 96% of the pixels are classified with less than 20% error (absolute units) on 7 May 2001 when all forested and non-forested areas are included. The corresponding figure for 4 May 2000 is 88%.     

Improved understorey bamboo cover mapping using a novel hybrid neural network and expert system

The giant panda is an obligate bamboo grazer. Therefore, the availability and abundance of understorey bamboo determines the quantity and quality of panda habitat. However, there is little or no information about the spatial distribution or abundance of bamboo underneath the forest canopy, due to the limitations of traditional remote sensing classification techniques. In this paper, a new method combines an artificial neural network and a GIS expert system in order to map understorey bamboo in the Qinling Mountains of south‐western China. Results from leaf‐off ASTER imagery, using a neural network and an expert system, were evaluated for their suitability to quantify understorey bamboo. Three density classes of understorey bamboo were mapped, first using a neural network (overall accuracy 64.7%, Kappa 0.45) and then using an expert system (overall accuracy 62.1%, Kappa 0.43). However, when using the results of the neural network classification as input into the expert system, a significantly improved mapping accuracy was achieved with an overall accuracy of 73.8% and Kappa of 0.60 (average z‐value = 3.35, p = 0.001). Our study suggests that combining a neural network with an expert system makes it possible to successfully map the cover of understorey species such as bamboo in complex forested landscapes (e.g. coniferous‐dominated and dense canopy forests), and with higher accuracy than when using either a neural network or an expert system.     

概率膜系统在大熊猫种群数据建模中的应用

大熊猫种群数据是掌握大熊猫种群动态变化的重要依据,因此大熊猫种群数据建模对保护大熊猫具有重要意义.本文针对现有种群动态性建模方法无法捕获复杂生态系统的随机效应和可扩展性差的问题,提出一种使用概率膜系统对成都大熊猫繁育研究基地大熊猫种群数据建模的方法.基于成都大熊猫繁育研究基地大熊猫谱系数据,设计了一个具有两层膜结构,形式化表示大熊猫生态系统的一系列对象和规则的概率膜系统模型.仿真实验结果表明,该模型能反映大熊猫种群动态变化趋势,给管理者提供参考.     

Spatial,seasonal, and topographical patterns of surface albedo in Norwegian forests and cropland

Land surface albedo is a key parameter of the Earth’s climate system. It has high variability in space, time, and land cover and it is among the most important variables in climate models. Extensive large-scale estimates can help model calibration and improvement to reduce uncertainties in quantifying the influence of surface albedo changes on the planetary radiation balance. Here, we use satellite retrievals of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo (MCD43A3), high-resolution land-cover maps, and meteorological records to characterize climatological albedo variations in Norway across latitude, seasons, land-cover type (deciduous forests, coniferous forests, and cropland), and topography. We also investigate the net changes in surface albedo and surface air temperature through site pair analysis to mimic the effects of land-use transitions between forests and cropland and among different tree species. We find that surface albedo increases at increasing latitude in the snow season, and cropland and deciduous forests generally have higher albedo values than coniferous forests, but for few days in spring. Topography has a large influence on MODIS albedo retrievals, with values that can change up to 100% for the same land-cover class (e.g. spruce in winter) under varying slopes and aspect of the terrain. Cropland sites have surface air temperature higher than adjacent forested sites, and deciduous forests are slightly colder than adjacent coniferous forests. By integrating satellite measurements and high-resolution vegetation maps, our results provide a large semi-empirical basis that can assist future studies to better predict changes in a fundamental climate-regulating service such as surface albedo.     

Monitoring forest conditions in a protected Mediterranean coastal area by the analysis of multiyear NDVI data

The operational utilization of remote sensing techniques for monitoring terrestrial ecosystems is often constrained by problems of under-sampling in space and time, particularly in heterogeneous and unstable Mediterranean environments. The current work deals with the use of the NOAA-AVHRR and Landsat-TM/ETM+ images to produce long-term NDVI data series characterising coniferous and broadleaved forests in a protected coastal area in Tuscany (Central Italy). Two methods to extract NDVI values of relatively small vegetated areas from NOAA-AVHRR data were first evaluated by comparison to estimates from higher resolution Landsat-TM/ETM+images. The optimal method was then applied to multitemporal AVHRR data series to derive 10-day NDVI profiles of coniferous and broadleaved forests over a 15-year period (1986-2000). Trend analyses performed on these data series showed that notable NDVI decreases occurred during the study period, particularly for the coniferous forest in summer and early fall. Further analysis carried out on local meteorological measurements led to identify the likely causes of these negative trends in contemporaneous winter rainfall decreases which were significantly correlated with the found NDVI variations.     

Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability

The lack of maps depicting forest three-dimensional structure, particularly as pertaining to snags and understory shrub species distribution, is a major limitation for managing wildlife habitat in forests. Developing new techniques to remotely map snags and understory shrubs is therefore an important need. To address this, we first evaluated the use of LiDAR data for mapping the presence/absence of understory shrub species and different snag diameter classes important for birds (i.e. ≥ 15 cm, ≥ 25 cm and ≥ 30 cm) in a 30,000 ha mixed-conifer forest in Northern Idaho (USA). We used forest inventory plots, LiDAR-derived metrics, and the Random Forest algorithm to achieve classification accuracies of 83% for the understory shrubs and 86% to 88% for the different snag diameter classes. Second, we evaluated the use of LiDAR data for mapping wildlife habitat suitability using four avian species (one flycatcher and three woodpeckers) as case studies. For this, we integrated LiDAR-derived products of forest structure with available models of habitat suitability to derive a variety of species-habitat associations (and therefore habitat suitability patterns) across the study area. We found that the value of LiDAR resided in the ability to quantify 1) ecological variables that are known to influence the distribution of understory vegetation and snags, such as canopy cover, topography, and forest succession, and 2) direct structural metrics that indicate or suggest the presence of shrubs and snags, such as the percent of vegetation returns in the lower strata of the canopy (for the shrubs) and the vertical heterogeneity of the forest canopy (for the snags). When applied to wildlife habitat assessment, these new LiDAR-based maps refined habitat predictions in ways not previously attainable using other remote sensing technologies. This study highlights new value of LiDAR in characterizing key forest structure components important for wildlife, and warrants further applications to other forested environments and wildlife species.     

Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study

Landscapes containing differing amounts of ecological disturbance provide an excellent opportunity to validate and better understand the emerging Moderate Resolution Imaging Spectrometer (MODIS) vegetation products. Four sites, including 1‐year post‐fire coniferous, 13‐year post‐fire deciduous, 24‐year post‐fire deciduous, and >100 year old post‐fire coniferous forests, were selected to serve as a post‐fire chronosequence in the central Siberian region of Krasnoyarsk (57.3°N, 91.6°E) with which to study the MODIS leaf area index (LAI) and vegetation index (VI) products. The collection 4 MODIS LAI product correctly represented the summer site phenologies, but significantly underestimated the LAI value of the >100 year old coniferous forest during the November to April time period. Landsat 7‐derived enhanced vegetation index (EVI) performed better than normalized difference vegetation index (NDVI) to separate the deciduous and conifer forests, and both indices contained significant correlation with field‐derived LAI values at coniferous forest sites (r ² = 0.61 and r ² = 0.69, respectively). The reduced simple ratio (RSR) markedly improved LAI prediction from satellite measurements (r ² = 0.89) relative to NDVI and EVI. LAI estimates derived from ETM+ images were scaled up to evaluate the 1 km resolution MODIS LAI product; from this analysis MODIS LAI overestimated values in the low LAI deciduous forests (where LAI<5) and underestimated values in the high LAI conifer forests (where LAI>6). Our results indicate that further research on the MODIS LAI product is warranted to better understand and improve remote LAI quantification in disturbed forest landscapes over the course of the year.     

Application of photon recollision probability in coniferous canopy reflectance simulations

A new semi-physical forest reflectance model, PARAS, is presented in the paper. PARAS is a simple parameterization model for taking into account the effect of within-shoot scattering on coniferous canopy reflectance. Multiple scattering at the small scale represented by a shoot is a conifer-specific characteristic which causes the spectral signature of coniferous forests to differ from that of broadleaved forests. This has for long led to problems in remote sensing of canopy structural variables in coniferous dominated regions. The PARAS model uses a relationship between photon recollision probability and leaf area index (LAI) for simulating forest reflectance. The recollision probability is a measurable, wavelength independent variable which is defined as the probability with which a photon scattered in the canopy interacts with a phytoelement again. In this study, we present application results using PARAS in simulating reflectance of coniferous forests for approximately 800 Scots pine and Norway spruce dominated stands. The results of this study clearly indicate that a major improvement in simulating canopy reflectance in near-infrared (NIR) is achieved by simply accounting for the within-shoot scattering. In other words, the low NIR reflectance observed in coniferous areas is mainly due to within-shoot scattering. In the red wavelength the effect of within-shoot scattering was not pronounced due to the high level of needle absorption in the red range. To conclude the paper, further application possibilities of the presented parameterization model are discussed.     

基于Sentinel-2数据的天山山地针叶林识别方法研究

阴影是影响山地针叶林遥感识别精度的关键因素。选取天山一块面积约为10 000 km²的区域为案例,基于太阳高度角和方位角差异较大的两期Sentinel-2影像,从遥感数据阴影分布的时相特性、分类特征以及分类器选择三方面进行综合分析,提出了一种适用于天山山地针叶林的遥感综合分类方案。该综合分类方案首先开展阴影识别以及阴影再分类以排除阴影对针叶林识别的影响;然后筛选出了海拔、归一化差值植被指数（NDVI）、红光到近红外波段斜率、蓝光波段、红光波段、短波红外波段和坡度作为区分天山山地针叶林的重要特征;最后比较支持向量机（Support Vector Machine,SVM）、随机森林（Random Forest,RF）和BP神经网络（Back Propagation Neural Network,BPNN）3种分类器的分类效果。结果表明：采用地形校正方法来消除山体阴影的效果不但不明显,反而还会造成过矫正现象,从而影响后续的针叶林识别,但利用太阳高度角和方位角差异较大的两期影像开展阴影识别以及阴影再分类来排除阴影对针叶林识别的影响,可使针叶林的总体精度提高1.3%~3.7%;SVM、RF和BPNN 3种分类器都能取得较好的山地针叶林识别精度,但SVM分类器的分类精度最高,其总体分类精度和Kappa系数分别是93.33%和0.87。该遥感综合分类方案经参数调整之后有望应用于北方干旱半干旱区的其他山地针叶林区域。     

地形校正对叶面积指数遥感估算的影响

利用经过6S模型大气校正的地面反射率图像、数字地面高程数据以及改进的CIVCO地形校正模型,分别计算了褒河流域不同植被类型（阔叶林、针叶林和灌木林）的3类光谱植被指数（NDVI、SR和SAVI）,并建立了各个植被类型叶面积指数与同时相的各个植被指数的相关关系。结果表明,地形校正能有效地消除大部分的地形影响,显著地提高各植被指数与叶面积指数的相关关系;对于阴坡和阳坡来讲,阴坡较阳坡提高显著;对于不同的植被类型,针叶林和灌木较阔叶林提高较为显著;对于同一植被指数如SAVI,灌木提高较针叶林和阔叶林显著,说明地形校正对叶面积指数的遥感估算结果有很大的影响。因此在利用遥感数据定量估算叶面积指数时,尤其对于山区,不仅要进行地形校正,而且要针对不同的植被类型选择合适的植被指数进行估算。     

融合AHP和GIS技术的自然遗产地边界界定方法研究

自然遗产地边界界定对于遗产地保护和管理具有重要的意义。已有的遗产地边界界定方法多基于先验知识和专家经验，具有一定的主观性，精度也有待进一步验证。以卫星遥感影像为基础数据，提出层次分析法（Analytic Hierarchy Process, AHP）和GIS结合的自然遗产地边界界定方法，并将其用于四川卧龙自然保护区边界确定。首先利用AHP分析并构建自然遗产地边界影响因子等级体系，然后基于GIS分析建模，通过数字化得到卧龙自然保护区的边界。与已公开的相关资料相比，该方法划定的保护区面积和边界更加科学、客观、精确，为珍稀动物大熊猫栖息地提供了明确的自然边界，而且突出了人与保护区相互关系及保护区的自然和生态保护价值。     

Evaluating MODIS data for mapping wildlife habitat distribution

Habitat distribution models have a long history in ecological research. With the development of geospatial information technology, including remote sensing, these models are now applied to an ever-increasing number of species, particularly those located in areas in which it is logistically difficult to collect habitat data in the field. Many habitat studies have used data acquired by multi-spectral sensor systems such as the Landsat Thematic Mapper (TM), due mostly to their availability and relatively high spatial resolution (30 m/pixel). The use of data collected by other sensor systems with lower spatial resolutions but high frequency of acquisitions has largely been neglected, due to the perception that such low spatial resolution data are too coarse for habitat mapping. In this study we compare two models using data from different satellite sensor systems for mapping the spatial distribution of giant panda habitat in Wolong Nature Reserve, China. The first one is a four-category scheme model based on combining forest cover (derived from a digital land cover classification of Landsat TM imagery acquired in June, 2001) with information on elevation and slope (derived from a digital elevation model obtained from topographic maps of the study area). The second model is based on the Ecological Niche Factor Analysis (ENFA) of a time series of weekly composites of WDRVI (Wide Dynamic Range Vegetation Index) images derived from MODIS (Moderate Resolution Imaging Spectroradiometer – 250 m/pixel) for 2001. A series of field plots was established in the reserve during the summer–autumn months of 2001–2003. The locations of the plots with panda feces were used to calibrate the ENFA model and to validate the results of both models. Results showed that the model using the seasonal variability of MODIS-WDRVI had a similar prediction success to that using Landsat TM and digital elevation model data, albeit having a coarser spatial resolution. This suggests that the phenological characterization of the land surface provides an appropriate environmental predictor for giant panda habitat mapping. Therefore, the information contained in remotely sensed data acquired with low spatial resolution but high frequency of acquisitions has considerable potential for mapping the habitat distribution of wildlife species.     

融合AHP和GIS技术的自然遗产地边界界定方法研究

自然遗产地边界界定对于遗产地保护和管理具有重要的意义。已有的遗产地边界界定方法多基于先验知识和专家经验,具有一定的主观性,精度也有待进一步验证。以卫星遥感影像为基础数据,提出层次分析法（Analytic Hierarchy Process, AHP）和GIS结合的自然遗产地边界界定方法,并将其用于四川卧龙自然保护区边界确定。首先利用AHP分析并构建自然遗产地边界影响因子等级体系,然后基于GIS分析建模,通过数字化得到卧龙自然保护区的边界。与已公开的相关资料相比,该方法划定的保护区面积和边界更加科学、客观、精确,为珍稀动物大熊猫栖息地提供了明确的自然边界,而且突出了人与保护区相互关系及保护区的自然和生态保护价值。     

Topographic constrained land cover classification in mountain areas using fully convolutional network

Mountains are an important kind of landform on the earth’s surface. Due to harsh mountainous environment, such as steep slopes and cliffs, remote sensing has become an indispensable tool for surveying mountain areas instead of traditional ground surveys. However, the accuracy of current land cover products derived from remote sensing in mountain areas is still low. In this paper, we propose a three-level architecture for land cover classification in mountain areas. Topographic partitioning is first performed in order to partition a large area into several smaller zones, and then, multiresolution segmentation is implemented in each individual zone. Thus, we can obtain initial geo-semantic objects with terrain, spectrum and texture homogeneities. A fully convolutional network (FCN)-based classifier (U-Net) is further introduced for supervised classification of land cover. From the perspectives of both visual interpretation and quantitative evaluation, the proposed method achieved robust and high-precision results for all land cover types. We also investigate the contributions of multimodal features for classification accuracy improvement. First, the results showed that additional features resulted in higher classification accuracies than 3-features only; 6-features achieved the best performance on farmland, impervious surfaces and coniferous forests, while 5-features performed well on water and broad-leaved forests. The elevation feature did not have a positive effect on water and broad-leaved forests, which can be explained by their physical distribution in the landscape. Second, the most significant improvement was achieved on water (Kappa coefficient increased from 0.741 to 0.924), followed by coniferous forests (Kappa coefficient increased from 0.629 to 0.805), whereas only a minor improvement was observed for the other three types. Furthermore, the accuracies of farmland and impervious surfaces remained relatively high even without the assistance of additional features, and texture feature plays a key role. The final land cover map was generated by combining the optimal results of each type via a hierarchical integrating strategy. The overall accuracy of classification achieved 90.6%.     

多波段多极化成像雷达图象识别森林类型效果分析

要陕西省宜川县三北防护林地区获取的航天飞机成像雷达ＳＩＲ－Ｃ／Ｘ－ＳＡＲ数据为基础,进行了森林类型的识别与分类,区分出针叶林、阔叶林和混交林３种森林类型,并通过从ＳＩＲ－Ｃ／Ｘ－ＳＡＲ数据中提取这３种类型森林的后向散射系数,分析了多波段、多极化雷达在森林类型识别中的作用。     

Using artificial neural networks to map the spatial distribution of understorey bamboo from remote sensing data

Understorey vegetation is a critical component of biodiversity and an essential habitat component for many wildlife species. However, compared to overstorey, information about understorey vegetation distribution is scant, available mainly over small areas or through imprecise large area maps from tedious and time-consuming field surveys. A practical approach to classifying understorey vegetation from remote sensing data is needed for more accurate habitat analyses and biodiversity estimates. As a case study, we mapped the spatial distribution of understorey bamboo in Wolong Nature Reserve (south-western China) using remote sensing data from a leaf-on or growing season. Training on a limited set of ground data and using widely available Landsat TM data as input, a nonlinear artificial neural network achieved a classification accuracy of 80% despite the presence of co-occurring mid-storey and understorey vegetation. These results suggest that the influences of understorey vegetation on remote sensing data are available to practical approaches to classifying understorey vegetation. The success here to map bamboo distribution has important implications for giant panda conservation and provides a good foundation for developing methods to map the spatial distributions of other understorey plant species.