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.     

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

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

UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest

Development of vegetation communities in areas of Antarctica without permanent ice cover emphasizes the need for effective remote sensing techniques for proper monitoring of local environmental changes. Detection and mapping of vegetation by image classification remains limited in the Antarctic environment due to the complexity of its surface cover, and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation. As ultra-high resolution aerial images allow a comprehensive analysis of vegetation, this study aims to identify different types of vegetation cover (i.e., algae, mosses, and lichens) in an ice-free area of  Hope Bay, on the northern tip of the Antarctic Peninsula. Using the geographic object-based image analysis (GEOBIA) approach, remote sensing data sets are tested in the random forest classifier in order to distinguish vegetation classes within vegetated areas. Because species of algae, mosses, and lichens may have similar spectral characteristics, subclasses are established. The results show that when only the mean values of green, red, and NIR bands are considered, the subclasses have low separability. Variations in accuracy and visual changes are identified according to the set of features used in the classification. Accuracy improves when multilayer information is used. A combination of spectral and morphometric products and by-products provides the best result for the detection and delineation of different types of vegetation, with an overall accuracy of 0.966 and a Kappa coefficient of 0.946. The method allowed for the identification of units primarily composed of algae, mosses, and lichens as well as differences in communities. This study demonstrates that ultra-high spatial resolution data can provide the necessary properties for the classification of vegetation in Maritime Antarctica, even in images obtained by sensors with low spectral resolution.     

基于随机森林回归的草场植被盖度反演模型研究——以新疆阿勒泰地区布尔津县为例

作为草地资源大国,我国正面临严峻的草场退化形势。掌握草场植被盖度的历史演变趋势,是草场退化驱动力识别及风险评估的基础。目前已有研究多以参数回归方法估算植被盖度,但并未充分考虑其苛刻的使用条件。利用Landsat系列卫星遥感影像及地面植被盖度监测资料建立非参数回归——随机森林回归模型,并与传统线性回归方法进行比较,在此基础上应用随机森林回归模型估算近10年来布尔津县草场植被盖度的变化趋势,并对结果的不确定性进行分析。结果显示:传统的线性回归方法很难满足其基本的统计学假设条件,而随机森林模型不但无需进行假设条件检验,而且预测的准确性也优于以往普遍应用的线性模型。基于Landsat ETM+标准数据得到的反演结果较之TM和OLI数据普遍偏小,地表反射率数据虽然可以大幅降低传感器不同对反演结果所造成的影响,但结果仍存在约±10%的不确定性。涉及的草场类型众多,为了提高反演精度,后续研究需要分别计算其植被指数,并尽量减低传感器差异带来的不确定性。     

遥感技术支持下的植被生产力与生物量研究进展

目前广泛应用于植被生产力与生物量估算的遥感模型主要有经验模型、物理模型、半经验模型和综合模型 ,它们的应用受到诸如大气、背景、地形、植被覆盖率与结构等因素的影响。遥感技术的迅速发展及其它技术的应用 ,包括热红外、微波和激光遥感仪器以及多角度、高光谱和高分辨率技术等 ,正逐步消除或降低影响因素 ,进一步提高植被生产力与生物量估算的范围和精度     

鄂尔多斯植被盖度分布与环境因素的关系

 从植被指数分布与气象因素、地质地层和地质水文的关系入手, 利用遥感解译和地理信息系统的空间分析功能, 分析了不同尺度下, 气候因素、地质水文因素、基质和地貌等对鄂尔多斯高原植被盖度分布的影响。指出: 在区域尺度上, 研究区植被盖度分布主要受降水影响, 植被盖度呈从东南到西北逐渐减少的趋势; 高盖度植被主要分布在冲洪积地貌和丘陵地貌区。局部分析, 库布齐沙漠东向延伸的存在与发展与区域型断层的存在有密切的联系; 毛乌素沙地中的高盖度植被分布受地形和基质岩性组合的综合影响, 主要分布在第四系湖积物和第四系冲积物等渗水性差的基质上, 尤其集中分布于凹陷的湖相沉积。     

黄河流域植被时空变化及其对气候要素的响应

在气候变化和人类活动的双重作用下,黄河流域生态环境不断发生变化。探讨植被生长动态对于实施生态保护政策至关重要。利用Advanced Very High Resolution Radiometer(AVHRR) Leaf Area Index(LAI)遥感资料,结合气候要素数据,分析1981—2017年黄河流域植被覆盖的时空分布特征,探讨气候要素对其变化的影响及贡献率。研究结果表明：(1)时序上,黄河流域植被覆盖呈显著增长趋势,夏季植被覆盖的增长幅度和年际波动最大,冬季植被覆盖呈缓慢平稳增长,波动最小。(2)空间上,植被覆盖显著提高的区域占整个区域的52.1%,主要分布在中东部平原;显著降低的区域占4%,主要分布在北部和西部高原山地;生态脆弱的区域植被覆盖率大多有不同程度的提高,但生态环境良好的部分区域植被覆盖率降低。(3)时序上,黄河流域植被覆盖与气温具有显著的正相关关系。春夏冬三季的植被覆盖与气温呈显著正相关,与降水呈不显著关系;秋季的植被覆盖与气温和降水量均呈显著正相关;春秋冬三季的植被覆盖与太阳辐射呈不显著负相关,夏季的植被覆盖与太阳辐射呈不显著正相关。春夏秋冬四季的气温对植被覆...     

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.     

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.     

中国北方草地与农牧交错带植被的NDVI-Ts空间的年内变化特征

利用多时相或时序植被指数(normalize difference vegetation index,NDVI)数据进行地表覆盖研究已取得了大量成果。随着陆地表面温度(1and surface temperature,TS)遥感反演精度的不断提高,将Ts与NDVI结合起来进行地表植被动态变化的监测已成为可能。本文主要包括以下三部分内容：1)介绍了基于卫星遥感数据的NDVI、Ts和Ts／NDVI计算方法。2)讨论NDVI、Ts和Ts／NDVI数据对植被覆盖信息表达的差异,并分析了中国北方草地与农牧交错带植被在NDVI-TS空间的年内变化特征。3)利用信息熵和平均梯度,定量分析了NDVI、Ts和Ts／NDVI数据在信息表达丰富度方面的差异,并对在不同地表植被覆盖下,Ts／NDVI数据对信息提高程度的敏感性进行了讨论。     

中国北方草地与农牧交错带植被的NDVI-Ts空间的年内变化特征

利用多时相或时序植被指数(normalize difference vegetation index,NDVI)数据进行地表覆盖研究已取得了大量成果.随着陆地表面温度(1and surface temperature,Ts)遥感反演精度的不断提高,将Ts与NDVI结合起来进行地表植被动态变化的监测已成为可能.本文主要包括以下三部分内容:1)介绍了基于卫星遥感数据的NDVI、Ts和Ts/NDVI计算方法.2)讨论NDVI、Ts和Ts/NDVI数据对植被覆盖信息表达的差异,并分析了中国北方草地与农牧交错带植被在NDVI-Ts空间的年内变化特征.3)利用信息熵和平均梯度,定量分析了NDVI、Ts和 Ts/NDVI数据在信息表达丰富度方面的差异,并对在不同地表植被覆盖下,Ts/NDVI数据对信息提高程度的敏感性进行了讨论.     

融入植被连续性因子的生态单元制图法在城市生物多样性维护中的应用

基于一个以植被结构为构建框架的生态单元分类系统,构建了融入了植被覆盖连续性因子的改良城市生态单元制图模型,并将其应用于瑞典赫尔辛堡市的绿色空间研究.使用原生林地指示种或林地连续性指示种（AWIS）鉴定长、短连续性林地的分布,对比其含有维管束植物的物种丰富度,对植被覆盖的连续性因子进行评估检验.结果表明： 长连续性林地中含有较多的AWIS;在建群种均龄大于30年的林地中,长连续性林地相对于结构相似的短连续性林地通常含有较高的生物多样性.融入植被连续性因子的生态单元制图模型是调查城市生物多样性的重要工具,通过图谱中各生态单元所含有的生物多样性信息,可对今后城市生物多样性的维护提出相应策略.     

Choosing the appropriate spatial resolution for monitoring coral bleaching events using remote sensing

Bleached corals provide a strong optical signal that suggests that remote sensing investigations of major bleaching events are feasible using airborne or satellite sensors. However, patchy coral cover, varying intensities of bleaching, and water depths are likely to limit the application of remote sensing techniques in monitoring and mapping coral bleaching. Today, satellite multispectral sensors routinely provide images of reefs from 4 m (Ikonos) to 30 m resolution (Landsat); however, the adequacy of these sensors for monitoring and mapping bleaching events remains unclear. To clarify these issues, scanned aerial photographs acquired during the 1998 bleaching event over the Great Barrier Reef (Australia) were analyzed at various spatial resolutions, from 10 cm to 5 m. We found that the accuracy of mapping bleaching is highly sensitive to spatial resolution. Highest accuracy was obtained at 10 cm resolution for detection of totally bleached colonies. At 1 m resolution, as much as 50% of the 10-cm resolution signal is lost, though the spatial patterns remain correctly described. Partially bleached (pale) corals are difficult to detect even in aerial surveys, leading to an underestimation of overall bleaching levels (total and partial bleaching) in aerial photos compared to in-situ surveys. If data volume and processing time are limiting factors, local variance analysis suggests that the optimal resolution necessary to capture spatial patterns of bleaching is in the range 40-80 cm.     

基于植被信息季节变换的植被覆盖度变化——以福建省连江县为例

基于植被覆盖度的植被信息遥感变化检测已成为研究植被及其相关生态系统变化的主要途径,但由于云覆盖等天气条件的影响,很难获得不同年份同一季节覆盖整个研究区的光学遥感影像来进行植被变化检测,而采用季节差异的影像必然会影响植被变化检测的结果.为此,本研究利用中高分辨率遥感数据的空间分辨率优势和MODIS遥感数据的时间分辨率优势,基于二者关系的拟合,提出一种植被信息季节变换的方法,将不同季节影像的植被覆盖度变换到研究所需的季节上.结果表明: 将该方法应用到福建敖江流域连江片区发现,植被信息变换的效果较好,经过将覆盖研究区的2007年冬季和2013年春季的中高分辨率影像的植被信息统一变换到夏季后,2007年的植被覆盖度由66.5%上升到79.7%,2013年由58.6%上升到77.9%,有效消除了因季节差异而对植被覆盖度估算产生的误差,提高了结果的准确性.     

陕西省退耕还林植被覆盖度与湿润指数的变化关系

使用MODIS-NDVI数据和气象站点资料,通过GIS遥感技术和数理统计等方法,分析了陕西省退耕还林后(2000—2012年)植被覆盖度与湿润指数的时空变化规律及两者变化的关系。结果表明,陕西省植被覆盖度和湿润指数都呈现由南向北递减的分布规律并且有明显的季节变化特征。2000—2012年,陕西省植被覆盖度在波动中呈现大幅增加的趋势,陕北地区增加最为显著,生态环境得到明显改善,然而部分城市周边地区植被有退化的迹象。2000—2012年湿润指数年际变化波动较大,有上升的趋势,陕南地区增加显著。空间分布上随着植被覆盖度的增加湿润指数呈指数变化趋势,相关性与植被覆盖度面积取值范围有关,范围取值越大相关系数越高。植被覆盖度的年际变化受到气候和人为因素影响,陕南地区植被覆盖度与湿润指数的相关性较显著,而受到人为影响比较明显的陕北、关中地区相关性不显著。     

USLE/RUSLE模型中植被覆盖管理因子的遥感定量估算研究进展

通用土壤流失方程（USLE）及其后续修正方程（RUSLE）是区域土壤侵蚀风险评估和水土保持规划的有效工具.植被覆盖管理因子作为USLE和RUSLE的重要参数之一,其合理估算对土壤侵蚀的准确预测尤为重要.基于野外实地调查和测量的传统估算法费时、费力且费用高,无法满足宏观尺度上植被覆盖管理因子的快速提取.近年来,遥感技术的发展为大尺度植被覆盖管理因子获取提供了丰富的数据和方法.本文基于国内外相关研究成果,综述了植被覆盖管理因子遥感定量估算方法的研究进展,评述了各类方法的优劣,以期为进一步开展大尺度植被覆盖管理因子的定量估算及拓展现有研究思路提供借鉴.     

Vegetation maps based on remote sensing are informative predictors of habitat selection of grassland birds across a wetness gradient

Vegetation is a major environmental factor influencing habitat selection in bird species. High resolution mapping of vegetation cover is essential to model the distribution of populations and improve the management of breeding habitats. However, the task is challenging for grassland birds because microhabitat variations relevant at the territory scale cannot be measured continuously over large areas to delineate areas of higher suitability. Remote sensing may help to circumvent this problem. We addressed this issue by using SPOT 5 imagery and phytosociological data. We mapped grassland vegetation in a floodplain using two methods. We (i) mapped the continuous Ellenberg index of moisture and (ii) identified 5 vegetation classes distributed across the wetness gradient. These two methods produced consistent output maps, but they also provided complementary results. Ellenberg index is a valuable proxy for soil moisture while the class approach provided more information about vegetation structure, and possibly trophic resources. In spite of the apparent uniformity of meadows, our data show that birds do not settle randomly along the moisture and vegetation gradients. Overall birds tend to avoid the driest vegetation classes, i.e. the highest grounds. Thus, vegetation maps based on remote sensing could be valuable tools to study habitat selection and niche partition in grassland bird communities. It is also a valuable tool for conservation and habitat management.     

Classification and Mapping of Coenotic Diversity of Forests

The proposed approach to the study of regularities of spatial variability of plant cover and to mapping forest vegetation is illustrated by the example of European Russia. It is shown that remote sensing and GIS technologies require particular standards of plant cover classification and reflection in maps. The given principles of classification and compilation of explications for maps of forest cover enable an assessment of its status and dynamics and a comparison of materials of different scales. We use the ecological–phytocoenotic approach to classifying forest vegetation. The specified units correspond to the categories of the main classifications of plant cover used in Russian geobotanics. In our classification, we have verified some parameters and the semantics of the mapped units, using satellite images, for their definite identification and interpretation. The elaborated approach to the classification and mapping of forest cover is applied for the study of the diversity of spruce forests under different climatic conditions in two regions, where they occupy about 20% of the total area. The first example characterizes the northern taiga subzone of forests of eastern Fennoscandia in the center of Murmansk oblast, and the second one represents the subzone of broad-leaved–coniferous forest in the southwest of Moscow oblast.     

Differentiating geological fertility derived vegetation zones in Kruger National Park,South Africa,using Landsat and MODIS imagery

Spatial technologies present possibilities for producing frequently updated and accurate habitat maps, which are important in biodiversity conservation. Assemblages of vegetation are equivalent to habitats. This study examined the use of satellite imagery in vegetation differentiation in South Africa's Kruger National Park (KNP). A vegetation classification scheme based on dominant tree species but also related to the park's geology was tested, the geology generally consisting of high and low fertility lithology. Currently available multispectral satellite imagery is broadly either of high spatial but low temporal resolution or low spatial but high temporal resolution. Landsat TM/ETM+ and MODIS images were used to represent these broad categories. Rain season dates were selected as the period when discrimination between key habitats in KNP is most likely to be successful. Principal Component Analysis enhanced vegetated areas on the Landsat images, while NDVI vegetation enhancement was employed on the MODIS image. The images were classified into six field sampling derived classes depicting a vegetation density and phenology gradient, with high (about 89%) indicative classification accuracy. The results indicate that, using image processing procedures that enhance vegetation density, image classification can be used to map the park's vegetation at the high versus low geological fertility zone level, to accuracies above 80% on high spatial resolution imagery and slightly lower accuracy on lower spatial resolution imagery. Rainfall just prior to the image date influences herbaceous vegetation and, therefore, success at image scene vegetation mapping, while cloud cover limits image availability. Small scale habitat differentiation using multispectral satellite imagery for large protected savanna areas appears feasible, indicating the potential for use of remote sensing in savanna habitat monitoring. However, factors affecting successful habitat mapping need to be considered. Therefore, adoption of remote sensing in vegetation mapping and monitoring for large protected savanna areas merits consideration by conservation agencies.