Vegetation classification of East China with multi-temporal NOAA-AVHRR data

East China lies in the subtropical monsoon climatic zone and is dominated by subtropical evergreen broad-leaved forests,a unique vegetation type mainly distributed in East Asia with the largest distnbution in China.It is important to be able to monitor and estimate forest biomass and production,regional carbon storage,and global climate change impacts on these important vegetation types.In this paper,we used coarse resolution remote sensing data to identify the vegetation types in East China and developed a map of the spatial distribution of vegetation types in this region.Nineteen maximum normalized difference vegetation index(NDVI)composite images(acquisition time span of 7 months from February to August),which were derived from 10 days National Oceanographic and Atmospheric Administration(NOAA)Advanced Very High Resolution Radiometer(AVHRR)channel 1 and channel 2 observations,an unsupervised classification method,and the ISODATA algorithm were employed to identify the vegetation types.To reduce the dimensions of the dataset resulted in a total of 28 spectral clusters of land-cover of which two clusters were urban/bare soil and water,the images were processed using principal component analysis(PCA).The 26 remaining spectral clusters were merged into six vegetation types using the Chinese vegetation taxonomy system:evergreen broad-leaved forest,coniferous forest,bamboo forest,shrub-grass,aquatic vegetation,and agricultural vegetation.The spatial distribution and areal extent for the coniferous forests,shrub-grass,evergreen broad-leaved forests,and agricultural vegetation were calculated and comscale.The spatial accuracy and the area accuracy for coniferous forests,shrub-grass,evergreen broad-leaved forests,and agricultural vegetation were 79.2%,91.3%,68.2% and 95.9% and 92.1%,95.9%,63.8% and 90.5%,respectively.The spatial accuracy and area accuracy of the bamboo forest were 28.7% and 96.5%,respectively;the spatial accuracy of aquatic vegetation was 69.6%,but there was a significant difference in its area accuracy because image acquisition did not cover the full year.Our study demonstrated the fea sibility of using NOAA-AVHRR to identify the different vegetation types in the subtropical evergreen broad-leaved forest zone in East China.The spatial location of the six identified vegetation types agreed with the actual geo graphical distribution of the vegetation types in East China.     

基于NOAA-AVHRR数据的中国东部地区植被遥感分类研究

该文采用 19幅 (时间跨 8个月 ) 时间序列的NOAA_AVHRR的归一化植被指数 (NDVI) 最大值合成影像遥感数据, 经过主分量分析 (Principlecomponentanalysis, PCA) 处理后, 用非监督分类方法的ISODATA算法, 对中国东部地区的 (五省一市 ) 植被进行分类, 结果可以分出 2 8种土地覆盖类型, 除了两种类型为水体和城市或裸地外, 其余 2 6种类型均为植被类型, 根据中国植被分类系统, 这 2 6类可以归并为 6大植被类型 :1) 常绿阔叶林 ;2 ) 针叶林 ;3) 竹林 ;4 ) 灌草丛 ;5 ) 水生植被 ;6 ) 农业植被。用 1∶10 0 0 0 0 0数字化《中国植被图集》的植被类型检验遥感分类结果表明, 针叶林、灌草丛、常绿阔叶林和农业植被的分类具有较高的位置精度和面积精度, 位置精度分别为 79.2 %、91.3%、6 8.2 %和 95.9%, 面积精度分别达到 92.1%、95.9%、6 3.8%和 90.5 %。这 6大植被类型在地理空间上的分布规律与中国东部常绿阔叶林区植被的地带性分布基本一致。     

Vegetation classification of East China with multi-temporal NOAA-AVHRR data

East China lies in the subtropical monsoon climatic zone and is dominated by subtropical evergreen broad-leaved forests, a unique vegetation type mainly distributed in East Asia with the largest distribution in China. It is important to be able to monitor and estimate forest biomass and production, regional carbon storage, and global climate change impacts on these important vegetation types. In this paper, we used coarse resolution remote sensing data to identify the vegetation types in East China and developed a map of the spatial distribution of vegetation types in this region. Nineteen maximum normalized difference vegetation index (NDVI) composite images (acquisition time span of 7 months from February to August), which were derived from 10 days National Oceanographic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) channel 1 and channel 2 observations, an unsupervised classification method, and the ISODATA algorithm were employed to identify the vegetation types. To reduce the dimensions of the dataset resulted in a total of 28 spectral clusters of land-cover of which two clusters were urban/bare soil and water, the images were processed using principal component analysis (PCA). The 26 remaining spectral clusters were merged into six vegetation types using the Chinese vegetation taxonomy system: evergreen broad-leaved forest, coniferous forest, bamboo forest, shrub-grass, aquatic vegetation, and agricultural vegetation. The spatial distribution and areal extent for the coniferous forests, shrub-grass, evergreen broad-leaved forests, and agricultural vegetation were calculated and compared with the Vegetation Atlas of China at a 1:1,000,000 scale. The spatial accuracy and the area accuracy for coniferous forests, shrub-grass, evergreen broad-leaved forests, and agricultural vegetation were 79.2%, 91.3%, 68.2% and 95.9% and 92.1%, 95.9%, 63.8% and 90.5%, respectively. The spatial accuracy and area accuracy of the bamboo forest were 28.7% and 96.5%, respectively; the spatial accuracy of aquatic vegetation was 69.6%, but there was a significant difference in its area accuracy because image acquisition did not cover the full year. Our study demonstrated the feasibility of using NOAA-AVHRR to identify the different vegetation types in the subtropical evergreen broad-leaved forest zone in East China. The spatial location of the six identified vegetation types agreed with the actual geographical distribution of the vegetation types in East China. __________ Translated from Acta Phytoecologica Sinica, 2005, 29(3): 436–443 [译自: 植物生态学报, 2005, 29(3): 436–443]     

A comparison of photographic analyses used to quantify zooxanthella density and pigment concentrations in Cnidarians

Many cnidarians exist in an obligatory mutualism with dinoflagellates commonly called zooxanthellae. When these symbioses are stressed, zooxanthella densities often decrease (i.e., bleaching), resulting in reduced host fitness or mortality. Because zooxanthellae play a prominent role in the coloration of hosts, several analyses of reflected spectra from photographic images have been developed to quantify zooxanthella densities and serve as a proxy for invasive sampling methods. To date these techniques have not been compared. In this study, global information system (GIS) tools, commonly used with aerial and satellite images, and photographs of healthy and bleached sea anemones, Aiptasia pallida (Verrill), were used to compare these image analysis methods. Zooxanthella densities and chlorophyll-a concentrations were correlated with image brightness (i.e., digital number) in: the red, green, and blue bands (RGB); the average of the three RGB bands (RGB/3); intensity and saturation bands (IHS); and using a principal components analysis (PCA) of the RGB bands. RGB brightness correlations with zooxanthella densities and chlorophyll-a concentrations were highest using the blue band, followed by green, then red. Using any one band within RGB, however, restricts comparisons to similar color morphs. RGB/3, IHS or PCA transformations enable intra and inter-specific comparisons where colors may vary. Among these transformations, PCA and intensity had higher correlations, followed by RGB/3, then saturation. RGB/3 and IHS, unlike PCA, ignore the correlations between the three RGB bands, treating each pixel independently. PCA uses these correlations, and in doing so lessens the effects of heteroscedasticity in the data. In addition, the observed reciprocal relationship of intensity and saturation may serve as a standardized criterion for bleaching. Finally, this study demonstrates that GIS has broad interdisciplinary applications for spatial and spectral analyses from the individual colony to reef scale assessments.     

烟草叶面积指数的高光谱估算模型

叶面积指数(1eaf area index,LAI)是重要的生物物理参数,亦是各种生态模型、生产力模型以及碳循环研究等的重要生物物理参量,因此具有重要的研究意义。为了探索不同高光谱模型监测烟草叶面积指数LAI的精度,在烟草伸根期,旺长期和成熟期采用ASD Fieldspec HH光谱仪测定了不同水氮条件下烟草冠层的高光谱反射率和叶面积指数数据。选用四个常用的植被指数RVI (ratio vegetation index)、NDVI (normalized difference vegetation index)、MTVI2（Modified second triangular vegetation index）、MSAVI（Modified Soil-adjusted vegetation index）和PCA (principal component analysis)、neural network (NN)三种方法对烟草LAI进行了估算,比较分析了三种方法的估算结果。研究结果表明,植被指数法,主成分分析,神经网络方法LAI都取得了较为理想的结果,其中植被指数法可以较为精确反演烟草LAI,验证模型确定性系数在0.76～0.85之间,主成分分析方法和神经网络方法精度较高,分别为0.938和0.889。主成分分析方法验证模型的稳定性更好,其验证模型的RMSE为0.172,低于四个植被指数和神经网络。MTVI2和MSAVI能较好地去除土壤、大气等条件影响,反演精度高于RVI和NDVI。与基于植被指数建立的模型相比,主成分分析和神经网络可以更好的提高LAI的反演精度。     

基于图像融合与混合像元分解的城市植被盖度提取

城市植被盖度提取对于开展城市绿色空间保护和城市规划具有重要意义。随着遥感技术的发展,混合像元分解模型被广泛用于从中等分辨率的多光谱影像提取城市植被盖度,但较低的影像空间分辨率限制了该模型的应用领域。为此,以杭州市为例,首先引入Gram-Schmidt(GS)方法对Landsat ETM+的多光谱波段和全色波段进行融合,再通过混合像元分解模型从ETM+融合影像上提取城市植被盖度,最后利用SPOT影像进行精度检验。结果发现,采用GS方法对影像进行融合后,标准差、信息熵、平均梯度提高,相对偏差小于0.07,说明在保留多光谱信息的基础上提高了其空间分辨率。与SPOT影像相比,在融合影像上75%以上样本的植被盖度值相似,误差较大的区域是市区植被特别稀疏或茂盛的像元。与源影像相比,从融合影像上提取的植被盖度的均方根误差和系统误差降低了0.01。该方法在降低城市植被监测成本、提高监测精度方面具有潜力。     

Qualitative distinction of congeneric and introgressive mangrove species in mixed patchy forest assemblages using high spatial resolution remotely sensed imagery (IKONOS)

This paper is a preliminary report of the ability of IKONOS multispectral satellite imagery with a very high spatial resolution of 1 metre to distinguish two mangrove species in Sri Lanka belonging to the same genus (Rhizophora apiculata and R. mucronata). Not only is this an advancement for the monitoring of forests, it is even more important considering their patchy nature in Sri Lankan mangroves (in contrast to classically zoned forests). Apart from congeneric distinction, intro‐gressive species (Acrostichum aureum) can also be detected from IKONOS imagery, which is important in the early warning for cryptic ecological changes that may affect mangrove species composition (both floral and faunal) and functioning. The results tabulate the usage of various image composites, transformations and classifications, and indicate the danger of too much detail in remote sensing, and the need to apply an optimum resolution. We also highlight that the highest resolutions (as in pansharpened multispectral composites) remain invaluable for visual ecological investigations, which are not at all outdated by new digital satellite images of (sub)metre spatial resolution and their possibility for computer‐aided analysis.     

不同大气校正方法对森林叶面积指数遥感估算影响的比较

利用TM原始图像以及经过6S模型和基于影像自身的Gilabert模型大气校正后的地面绝对反射率图像,分别计算了褒河流域阔叶林和针阔混交林2种林型的5类光谱植被指数(SR、NDVI、MNDVI、ARVI和RSR),并建立各林型森林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,2种大气校正模型均显著提高了各植被指数与森林叶面积指数的相关关系,除了对森林叶面积指数与植被指数SR和NDVI的相关关系影响不显著外,对森林叶面积指数与植被指数MNDVI、ARVI和RSR相关关系的影响均非常显著。说明不同大气校正模型对叶面积指数的遥感估算结果有较大影响。因此,在利用遥感数据进行定量分析、信息提取和生态遥感应用时,不仅要进行大气校正,而且还要慎重选择大气校正模型和植被指数。     

Vegetation structure and greenness in Central Africa from Modis multi-temporal data

African forests within the Congo Basin are generally mapped at a regional scale as broad-leaved evergreen forests, with the main distinction being between terra-firme and swamp forest types. At the same time, commercial forest inventories, as well as national maps, have highlighted a strong spatial heterogeneity of forest types. A detailed vegetation map generated using consistent methods is needed to inform decision makers about spatial forest organization and their relationships with environmental drivers in the context of global change. We propose a multi-temporal remotely sensed data approach to characterize vegetation types using vegetation index annual profiles. The classifications identified 22 vegetation types (six savannas, two swamp forests, 14 forest types) improving existing vegetation maps. Among forest types, we showed strong variations in stand structure and deciduousness, identifying (i) two blocks of dense evergreen forests located in the western part of the study area and in the central part on sandy soils; (ii) semi-deciduous forests are located in the Sangha River interval which has experienced past fragmentation and human activities. For all vegetation types enhanced vegetation index profiles were highly seasonal and strongly correlated to rainfall and to a lesser extent, to light regimes. These results are of importance to predict spatial variations of carbon stocks and fluxes, because evergreen/deciduous forests (i) have contrasted annual dynamics of photosynthetic activity and foliar water content and (ii) differ in community dynamics and ecosystem processes.     

Potential biodiversity map of bird species (Passeriformes): Analyses of ecological niche,environmental characterization and identification of priority conservation areas in southern Patagonia

Different methodologies try to identify priority conservation areas (PCA) to improve habitat conservation and decrease human pressures over bird species at coarse-scale. Map of potential biodiversity (PB) can identify PCA (high PB values) at different scale levels by considering ecological requirements and distributions through potential habitat suitability (PHS) models. The aim was to elaborate a map of PB of bird species based on PHS models to spatially identify PCA in Santa Cruz, Argentina. Moreover, we want to analysis species’ ecology requirements, and evaluate PB values and spatially identify PCA through two scale levels. We computed 47 models using Environmental Niche Factor Analysis (ENFA) on Biomapper software. Each model was visualized and combined to get a unique map of PB. We analyzed ecological requirements by specialization and marginality and PHS maps. Moreover, considering natural environments (regional level) and forest types’ cover (forest landscape level), we evaluated PB values using ANOVAs and identified PCA under different human pressures, using human footprint (HPF) map. Bird species related to Nothofagus forests were most specialist and exhibited a narrower potential distribution than grassland species. At regional level, Magellanic grass steppes displayed the highest PB values, where most of the PCA had high HPF values. At forest landscape level, ecotone N. antarctica forests had the highest PB values, where PCA with low HFP values were outside current protected networking. We conclude that combining PHS models and the map of PB allowed us to improve bird distribution studies and to assist biodiversity conservation strategies under human pressures.     

Remote sensing imagery in vegetation mapping: a review

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.     

基于多端元光谱混合分析方法的大兴安岭火后植被盖度恢复研究

火干扰是北方针叶林结构、功能及动态的主要调节因子之一。研究火后植被恢复对理解火干扰和生态系统的交互作用具有重要意义。火烧迹地通常由植被与基质混合组成,在中低分辨率（ > 10 m）遥感影像中表现为混合像元,因此研究亚像元尺度上植被的恢复是精确量化植被恢复的关键。本研究以2000年大兴安岭呼中自然保护区中8700 hm²火烧迹地为研究区,以两期（2014年6月1日和2010年6月22日）中分辨率Landsat ETM+影像（30 m）为基础数据,比较多端元光谱混合分析（Multiple Endmember Spectral Mixture Analysis,MESMA）和归一化植被指数（Normalized Difference Vegetation Index,NDVI）获得的植被盖度,以高分辨率（2 m）WorldView-2影像（2014年7月1日）为验证数据,对两种方法计算的植被盖度精度进行比较。结果表明,MESMA方法获得的植被盖度（R²=0.691）与传统的NDVI获得的植被盖度（R²=0.700）精度无统计差异,中烈度下获得的植被覆盖精度高于低、高火烧烈度。为验证同一端元能否运用到不同时相的Landsat影像中,本研究将从2014年影像中获取的最佳端元运用到2010年影像中获得植被盖度图,结果表明2014年与2010年得到的RMSE（均方根误差）均值分别为0.0015和0.0065,说明最佳端元可用于不同时相的影像分解。本研究表明MESMA方法可有效监测北方针叶林中火后植被盖度恢复,并可运用于时间序列遥感影像监测植被恢复动态。     

Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management

A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early season site- specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated. Two different sensors, a still visible camera and a six-band multispectral camera, and three flight altitudes (30, 60 and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications and UAV tasks. The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, area covered by each image and flight timing were very sensitive to flight altitude. At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crop and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index and Normalised Difference Vegetation Index), mainly at a 30 m altitude. However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches).     

应用间接排序识别盐沼植被的光谱特征:以崇明东滩为例

遥感技术已成为大尺度植被分类的重要手段,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键。该研究选择上海崇明东滩自然保护区的盐沼植物群落为对象,应用ASD地物光谱仪测定其植物群落的光谱反射率,并采用10个小型机载成像光谱仪(CASI)默认植被波段组,应用主分量分析法和相关分析分析了不同群落光谱特征与生态环境因子之间的关系。分析结果表明,间接排序法PCA能够识别盐沼植被中光滩、海三棱 草(Scirpus mariqueter)群落、芦苇(Phragmites australis)群落和互花米草(Spartina alterniflora)等群落的光谱特征,绝大多数盐沼湿地植物群落组成与光谱特征之间有显著的相关,识别效果最好的波段组是736~744 nm、746~753 nm、775~784 nm、815~824 nm和860~870 nm;对光谱反射率影响最大的生态环境因子分别是植物群落的高度和盖度,高程和其它环境因子的影响次之。研究成果可为遥感监测崇明东滩自然保护区内入侵种互花米草的空间分布和扩散规律提供技术支撑,为高光谱遥感影像的影像判读和解译分类以及盐沼湿地植被制图提供科学依据。     

Seasonal Foliage Changes in the Eastern Amazon Basin Detected from Landsat Thematic Mapper Satellite Images1

Seasonal changes in tropical forests are difficult to measure from the ground, especially in areas of high species diversity and low phenological synchrony. Satellite images, which integrate individual tree canopies and cover a large spatial extent, facilitate tests for stand-level canopy phenology. Variability in near-infrated radiance (TM bands 4 and 5) of several distinct vegetation types was used to detect seasonal changes in a series of three Landsat Thematic Mapper (TM) images from the wet season to the dry season in Marabá, Brazil (eastern Amazon basin). Despite different atmospheric and instrumental conditions among the images, spectral changes were distinguishable. A phenological process (leaf aging, leaf drop, water stress) was determined from the spectral changes for each vegetation type. Changes in the spectral properties suggest that during the dry season, upland terra firme forest increased the rate of leaf exchange and some riparian vegetation was deciduous. Terra firme forest that had been altered by penetration of fires from nearby pastures increased in leaf biomass over a 14-month period. This study shows that a time series of images can provide information on temporal changes in primary vegetation and guide field studies to investigate seasonal changes that may not be detectable from the ground.     

Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data

Vegetation is an integral component of wetland ecosystems. Mapping distribution, quality and quantity of wetland vegetation is important for wetland protection, management and restoration. This study evaluated the performance of object-based and pixel-based Random Forest (RF) algorithms for mapping wetland vegetation using a new Chinese high spatial resolution Gaofen-1 (GF-1) satellite image, L-band PALSAR and C-band Radarsat-2 data. This research utilized the wavelet-principal component analysis (PCA) image fusion technique to integrate multispectral GF-1 and synthetic aperture radar (SAR) images. Comparison of six classification scenarios indicates that the use of additional multi-source datasets achieved higher classification accuracy. The specific conclusions of this study include the followings:(1) the classification of GF-1, Radarsat-2 and PALSAR images found statistically significant difference between pixel-based and object-based methods; (2) object-based and pixel-based RF classifications both achieved greater 80% overall accuracy for both GF-1 and GF-1 fused with SAR images; (3) object-based classifications improved overall accuracy between 3%-10% in all scenarios when compared to pixel-based classifications; (4) object-based classifications produced by the integration of GF-1, Radarsat-2 and PALSAR images outperformed any of the lone datasets, and achieved 89.64% overall accuracy.     

基于遥感和空间分析的上海城市森林生物量分布特征

城市森林发挥着改善和维护城市生态环境质量的作用, 研究城市森林生物量和分布特点对其生态系统服务评价和林分经营均具有重要意义。该文根据上海城市森林的种植分布和经营状况利用2011年6月-2012年6月样地实测森林生物量数据和同期Landsat ETM+遥感图像, 在基于逐步回归分析建立森林生物量反演模型的基础上, 引入回归残差及空间分析, 研究了城市森林及其主要优势树种樟(Cinnamomum camphora)林分的生物量分布特征, 探讨了区域尺度森林生物量的遥感估测方法。结果表明: (1)上海城市森林生物量密度总体呈现中心城区(静安区、黄浦区等)较高, 生物量密度集中在35-70 t·hm^-2之间, 郊区(嘉定区、青浦区等)空间分布状况相对较低, 生物量密度介于15-50 t·hm^-2之间的变化特征。上海优势树种樟林分生物量密度范围为20-110 t·hm^-2; 空间上呈现出东北部较高、西南部较低的变化特征。(2)上海城市森林及樟林分的生物量总量分别为3.57 Tg和1.33 Tg。林地面积小, 具有较高森林生物量密度的上海中心城区, 其森林生物量占总量的6.1%, 其中林地面积最小的静安区生物量最低, 仅占总量的0.11%。在所有区县中, 林地面积最大的崇明县、浦东新区具有较高的森林生物量, 分别占总量的20.08%和19.18%。(3)所建立的基于回归反距离插值的城市森林生物量估测模型, 其标准误差、平均绝对误差、平均相对误差分别为8.39、6.86、24.22%, 较回归模型分别降低了57.69%、55.43%、64.00%, 较空间插值的方法分别降低了62.21%、58.50%、65.40%。残差的引入减少了由于空间变异引发的城市森林生物量遥感估测的不确定性。相比基于实测数据通过空间插值的估测, 遥感为快速便捷、客观高效的森林生物量监测提供了可能, 更加完善的结果和模型的优化有待引入其他信息源如高分高光谱信息或改善残差空间分析方法获得。     

川西亚高山森林恢复的空间格局分析

以米亚罗林区为例,利用森林样地调查和遥感影像解译方法,通过森林植被图与数字地形的叠加,分析了川西亚高山森林大规模采伐和更新后,主要森林植被类型外貌与起源之间的联系,以及各类型分布的地形分异规律和空间格局.结果表明,大规模采伐和更新后,森林植被类型的外貌与起源相关,老龄针叶林为保留下来的原始林,中幼龄针叶林为人工林,落叶阔叶林为天然次生林,而针阔混交林中既有天然次生的成分,也有人工、天然更新共同作用的成分.海拔2 800~3 600 m是米亚罗的主要伐区,森林恢复表现出坡向分异:人工更新的中幼龄针叶林主要分布于阳坡、半阳坡;落叶阔叶林和针阔混交林受天然更新的影响,主要分布于阴坡、半阴坡.老龄针叶林主要保留在海拔3 600 m以上.恢复过程中各种森林植被类型镶嵌分布,景观破碎化严重.     

林地叶面积指数遥感估算方法适用分析

叶面积指数是与森林冠层能量和CO2交换密切相关的一个重要植被结构参数,为了探讨估算林地叶面积指数LAI的遥感适用方法和提高精度的途径,利用TRAC仪器测定北京城区森林样地的LAI,从Landsat TM遥感图像计算NDVI、SR、RSR、SAVI植被指数,分别建立估算LAI的单植被指数统计模型、多植被指数组合的改进BP神经网络,获取最有效描述LAI与植被指数非线性关系的方法并应用到TM图像估算北京城区LAI。结果表明,单植被指数非线性统计模型估算LAI的精度高于线性统计模型;多植被指数组合神经网络中,以NDVI、RSR、SAVI组合估算LAI的精度最高,估算值与观测值线性回归方程的R2最高,为0.827,而RMSE最低,为0.189,神经网络解决了多植被指数组合统计模型非线性回归方程的系数较多、较难确定的问题,可较为有效的应用于遥感图像林地LAI的估算。     

Research on characteristics of biomass distribution in urban forests of Shanghai metropolis based on remote sensing and spatial analysis

Aims
Monitoring and quantifying the biomass and its distribution in urban trees and forests are crucial to understanding the role of vegetation in an urban environment. In this paper, an estimation method for biomass of urban forests was developed for the Shanghai metropolis, China, based on spatial analysis and a wide variety of data from field inventory and remote sensing.
Methods
An optimal regression model between forest biomass and auxiliary variables was established by stepwise regression analysis. The residual value of regression model was computed for each of the sites sampled and interpolated by Inverse-distance weighting (IDW) to predict residual errors of other sites not subjected to sampling. Forest biomass in the study area was estimated by combining the regression model based on remote sensing image data and residual errors of spatial distribution map. According to the distribution of plantations and management practices, a total of 93 sample plots were established between June 2011 and June 2012 in the Shanghai metropolis. To determine a suitable model, several spectral vegetation indices relating to forest biomass and structure such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and new images synthesized through band combinations such as the sum of TM2, TM3 and TM4 (denoted Band 234), and the sum of TM3, TM4 and TM5 (denoted Band 345) were used as alternative auxiliary parameters .
Important findings
The biomass density in urban forests of the Shanghai metropolis varied from 15 to 120 t·hm^-2. The higher densities of forest biomass concentrated mostly in the urban areas, e.g. in districts of Jing’an and Huangpu, mostly ranging from 35 to 70 t·hm^-2. Suburban localities such as the districts of Jiading and Qingpu had lower biomass densities at around 15 to 50 t·hm^-2. The biomass density of Cinnamomum camphora trees across the Shanghai metropolis varied between 20 and 110 t·hm^-2. The spatial biomass distribution of urban forests displayed a tendency of higher densities in northeastern areas and lower densities in southwestern areas. The total biomass was 3.57 million tons (Tg) for urban forests and 1.33 Tg for C. camphora trees. The overall forest biomass was also found to be distributed mostly in the suburban areas with a fraction of 93.9%, whereas the urban areas shared a fraction of only 6.1%. In terms of the areas, the suburban and urban forests accounted for 95.44% and 4.56%, respectively, of the total areas in the Shanghai metropolis. Among all the administrative districts, the Chongming county and the new district of Pudong had the highest and the second highest biomass, accounting for 20.1% and 19.18% of the total forest biomass, respectively. In contrast, the Jing’an district accounted for only 0.11% of the total forest biomass. The root-mean-square error (RMSE), mean absolute error (MAE) and mean relative error (MRE) of the model for estimating urban forest biomass in this study were 8.39, 6.86 and 24.22%, respectively, decreasing by 57.69%, 55.43% and 64.00% compared to the original simple regression model and by 62.21%, 58.50%, 65.40% compared to the spatial analysis method. Our results indicated that a more efficient way to estimate urban forest biomass in the Shanghai metropolis might be achieved by combining spatial analysis with regression analysis. In fact, the estimated results based on the proposed model are also more comparable to the up-scaled forest inventory data at a city scale than the results obtained using regression analysis or spatial analysis alone.