Eucalypt forest structure and synthetic aperture radar backscatter: a theoretical analysis

Eucalypt forest attributes describing structure are required for a range of management and inventory programs. The characterization of vegetation structure can be a laborious task and so managers and researchers are seeking alternatives through the use of remote sensing. Forest structure and the distribution of biomass through the forest canopy has a significant effect on radar backscatter and, in many cases, is the overwhelming variable in determining the type and quantity of radar backscatter observed in forest environments. In southern Tasmania a combination of field-based inventory plots and forest type maps were accessed to condense large variations in forest structure into five archetypical structural forest types. We then utilized a forest backscatter model to simulate the backscatter response of these five forest structural types for a range of radar wavelengths and polarizations. Good correlative relationships were obtained between backscatter and forest structure. There was a particularly good relationship between synthetic aperture radar to variations in allocation of biomass to stems, branches, and leaves. These individual and grouped attributes in eucalypt forests appear to influence radar backscatter enough to develop predictive relationships between backscatter and forest structure. Strong correlations were observed with radar P- and C-band wavelengths with foliage and bole biomass and between the ratio of L to C with foliage and branch allocations. Within Australian wet sclerophyll forests, where the biomass is typically very high, the true capacity and applicability of radar imagery may not be in direct biomass estimation but rather in the capacity of radar data to stratify forest stands based on structural variation.     

基于遥感影像的植被指数研究方法述评

随着遥感技术的发展,植被指数作为用来表征地表植被覆盖和生长状况的度量参数,已经在环境、生态、农业等领域有了广泛的应用。本文在分析植被指数形成机制及影响因子的基础上,对其具有一定技术突破的典型植被指数进行了归纳分类与比较分析,并评价了各自的优势和局限性。植被指数按遥感数据采集的平台可以分为航空植被指数和航天植被指数两大类,其中航天植被指数又可以分为基于波段简单线性组合的植被指数、消除影响因子的植被指数和针对高光谱遥感及热红外遥感的植被指数三类。最后就植被指数应用中存在的问题以及发展前景谈了一些看法:植被指数数目繁琐重复,急待规范条理化;植被指数应用领域不同,使用者时要慎重;植被指数影响因子很多,具体使用时应适时修正;植被指数公式繁琐阻碍其应用,应开发植被指数产品;遥感技术日新月异,积极研发新的植被指数。     

土壤水分遥感监测研究进展

土壤水分是陆地表面参数化的一个关键变量。土壤水分含量随时空的转换而变化,在地-气界面间物质、能量交换中起着重要的作用,是农作物生长发育的基本条件和农作物产量预报的重要参数。遥感技术具有大面积同步观测,时效性、经济性强的特点,为大面积动态监测土壤水分提供了可能。总结了近年来国内外遥感监测土壤水分理论、方法的发展和应用,介绍了目前几种比较成熟和广泛应用的土壤水分遥感监测方法与模型,对比分析了各种监测方法的优缺点,指出了土壤水分遥感监测方法存在的不足,指明了今后发展的方向,展望了土壤水分遥感监测方法的发展趋势。     

Mapping migratory bird prevalence using remote sensing data fusion

Background

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA.

Methodology and Principal Findings

A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species.

Conclusion and Significance

Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.     

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.     

The science of the biosphere

This paper considers the needs and potentials for the development of the biosphere. An emphasis is placed on the unusual qualities of the biosphere, such as important time lags, interactions between life and its environment at large scales, and biological evolution, which has led to large scale changes in the environment during the Earth's history. These qualities require a different approach to the development of a theory for this large scale system than has been used in the past, when the biosphere was treated as a steady-state, quasilinear system. Other aspects of the development of the science of the biosphere, including the use of remote sensing, are reviewed, and the application of these techniques to the estimation of certain biological variables is discussed.     

低空无人机遥感在草原监测评价中的应用进展

低空域无人机遥感技术具有高时效性、高分辨率、低成本、易操控等优势,作为地面与高空遥感（航天与航空遥感）间测量尺度空缺的有益补充,低空无人机遥感扩展了样地样方空间尺度,提高了中、小尺度遥感观测信息的精细化程度,实现了草原生境信息的快速采集、处理与分析应用,是草原"星-空-地"一体化监测的重要组成。针对草原监测评价,总结了国内外低空无人机遥感在草原基况调查（草原草层高度监测、草原植被覆盖度监测与草原地上生物量估算）、草原动态监测（草原植被长势监测、草原产草量估测与草畜平衡监测）和草原应急管理（草原火灾、雪灾与生物灾害监测）中的应用。结合大数据、人工智能、云计算与物联网等新型技术,分析了低空无人机遥感在草原生态监测领域存在的不足和未来的发展方向,以期为低空无人机遥感关于草原监测评价与智慧草原的后续研究提供参考。     

利用遥感光谱法进行农田土壤水分遥感动态监测

自 1 997年 4月至 1 998年 1 0月 ,在甘肃省定西县进行了大面积 0～ 5 0 cm土层农田土壤水分按每 1 5 d本底资料实际观测 ,对此间收到的 5幅 TM与 7幅 NOAA卫片数据资料进行了加工处理 ,并对地面光谱资料也进行了观测。在光谱反演与光谱和土壤水分相关性分析基础上 ,利用遥感技术和地理信息系统 ,初步建立了典型试验区 ( 3× 3km2 )遥感信息与土壤含水量之间的遥感光谱相关监测模型 ,做出了观测区土壤水分含量分布图和得到了大面积农田土壤水分宏观动态监测结果 ,并同地面实测土壤水分进行了精度校正。研究结果表明 ,文中提出的“光学植被盖度”概念 ,对土壤水分遥感监测研究是有益的 ,利用遥感光谱法和数学统计方法求出了有关物理参数 ,初步建立了 TM与 NOAA光谱水分监测模型 ,其模型监测 0～2 0 cm土层含水量的精度达到 90 %以上 ,实际监测土壤水分精度达到 72 .3% ;在遥感监测 2 0～ 5 0 cm土层土壤含水量中 ,利用遥感模型监测土壤水分精度达到 80 %以上 ,实际遥感监测精度达到 60 %左右 ,其结果可有效指导干旱半干旱雨养农业区春耕时间和动态监测大面积土壤墒情 ,可为农业生产提供科学依据。另外 ,经地面大量观测表明 ,一般来说 ,当土壤含水量为田间最大持水量的 5 5 %～ 85 %时 ,从生长状况和经济     

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.     

农田土壤水分预测模型的研究进展及应用

本文对国内外土壤湿度的监测,预报发展情况进行了回顾,总结了国内外土壤水分监测方法及其原理,介绍了国内外土壤湿度预测模型的研究进展及其应用情况,讨论了传统的土壤水分平衡方程统计模型法,从土壤水分运动方程出发的物理学模型法,利用气象要素与土壤水相关关系为基础的统计预报法、及新兴的人工神经元网络和遥感数据估测法的优缺点,并对土壤水分研究工作所面临的问题进行了必要的阐述。     

Radiation measurement for plant ecophysiology

The principles of radiation physics for plant ecophysiological studies are outlined with an emphasis on choosing appropriate sensors for specific purposes such as for studies of photosynthesis, UV-B damage or canopy energy balance. Remote sensing, both from the ground and from aircraft or satellites, is increasingly being used as a tool for the study of plant canopies. Therefore, relevant terminology and applications are discussed, including the use of remote sensing for the determination of canopy structural properties and the use of thermal remote sensing for the measurement of canopy temperature, for example, in energy balance studies.     

Implementation of a hierarchical global vegetation classification in ecosystem function models

Abstract. We propose an alternative approach for the currently used biogeographic global vegetation classifications. A hierarchical vegetation classification system is proposed for consistent and routine monitoring of global vegetation. Global vegetation is first defined into six classes based on plant canopy structure and dynamics observable by remote sensing from satellites. Additional biome variability is then represented through a remote sensing derived leaf area index map, and direct climate data sets driving an ecosystem model to compute and map net primary production and evapotranspiration. Simulation results from an ecosystem function model suggest that the six canopy structure-based classes are sufficient to represent global variability in these parameters, provided the spatio-temporal variations in Leaf Area Index and climate are characterized accurately. If a bioclimatically based classification is needed for other purposes, our six class approach can be expanded to a possible 21 classes using archived climatic zones. For example, tropical, subtropical, temperate and boreal labels are defined by absolute minimum temperature. Further separation in each class is possible through changes in water availability defined by precipitation and/or soils. The resulting vegetation classes correspond to many of the existing, conventional global vegetation schemes, yet retain the measure of actual vegetation possible because remote sensing first defines the six biome classes in our classification. Vegetation classifications are no longer an end product but a source of initializing data for global ecosystem function models. Remote sensing with biosphere models directly calculates the ecological functions previously inferred from vegetation classifications, but with higher spatial and temporal accuracy.     

土壤水分遥感反演研究进展

土壤水分精确反演对于理解和解决农业生产、生态规划以及水资源管理中的科学与实际问题至关重要。目前,大量的反演算法被广泛用于土壤水分估算,全球土壤水分遥感反演产品不断发布,反演算法与产品数据集的应用前景亟待系统梳理。基于不同谱段遥感探测技术中的土壤水分反演方法存在各自的特点、优势和局限性。除反演方法研究外,土壤水分遥感反演研究热点可被归纳为遥感土壤水分产品评估、在相关领域的应用、数据同化3个方面。大量研究表明土壤水分遥感反演产品在生态、水文、干旱等研究中表现出巨大的潜力,且在部分研究中已经得到应用。但目前土壤水分的遥感观测与应用需求仍存在一定的差距,因此最后对土壤水分遥感反演在探测的精度和准确度两个方面及其解决方案进行了总结与展望。     

1988-2013年基于BP神经网络的植被叶面积指数遥感定量反演

叶面积指数（Leaf Area Index,LAI）高度综合了植被水平覆盖状况和垂直结构,以及枯枝落叶层厚薄和地下生物量多少,是植被影响土壤侵蚀的主要方面。区域尺度的时间序列叶面积指数揭示了区域土壤侵蚀的演化过程。因此,及时准确地掌握区域尺度上长时间序列的植被LAI,对研究土壤侵蚀动态变化与植被的关系至关重要。选择南京市1988-2013年10期遥感影像,基于反向传播（Back Propagation,BP）神经网络构建LAI反演模型,进行了长时间序列的叶面积指数反演。结合2009和2010年LAI实测值,验证与探讨了该模型的评价精度与适应性。结果表明：（1）该模型拟合度较高,2009和2010年平均相对误差、均方根误差、相关系数分别是0.2395和0.2174,0.2962和0.2581,0.7713和0.6844,各项精度评价指标均较好;（2）统计分析去除耕地后全市LAI变化,低植被覆盖（LAI<2）面积不断增加,高植被覆盖区（LAI>3）面积先减少后增加,耕地面积不断减少,符合南京市的发展变化规律;（3）主城区LAI年际变化与其他学者得到的南京市植被盖度变化趋势一致,反演结果的时序性较高。本文提出的基于反向传播神经网络模型反演长时间序列LAI是可行的,为区域尺度土壤侵蚀定量遥感监测提供新途径。     

Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

Wetland vegetation plays a key role in the ecological functions of wetland environments. Remote sensing techniques offer timely, up-to-date, and relatively accurate information for sustainable and effective management of wetland vegetation. This article provides an overview on the status of remote sensing applications in discriminating and mapping wetland vegetation, and estimating some of the biochemical and biophysical parameters of wetland vegetation. Research needs for successful applications of remote sensing in wetland vegetation mapping and the major challenges are also discussed. The review focuses on providing fundamental information relating to the spectral characteristics of wetland vegetation, discriminating wetland vegetation using broad- and narrow-bands, as well as estimating water content, biomass, and leaf area index. It can be concluded that the remote sensing of wetland vegetation has some particular challenges that require careful consideration in order to obtain successful results. These include an in-depth understanding of the factors affecting the interaction between electromagnetic radiation and wetland vegetation in a particular environment, selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting spectral information of wetland vegetation.     

Remote sensing of riverine landscapes

1. The fashion for examining riverine landscapes is changing as our technical instruments, from microprobes to satellites, expands to be able to examine the spatial and temporal relationships among biota, hydrology and geomorphology across scales from microhabitats to channel units to valleys to catchments. 2. The range of successful applications from remote sensing analyses of riverine landscapes has especially increased with the launch of many new instruments that record data across the electromagnetic spectrum. Engineering of the instruments has also improved such that knowledge of the radiometric properties of the digital data is more complete as a result of better instrumentation and installation of on‐board calibrations systems for many instruments. 3. With the development of faster processing on cheaper computers, it is now common for comprehensive data sets to be processed through algorithms that previously could only be applied to relatively small (<1 Mbyte) rasters of data. This technical advance is especially important for the statistical algorithms such as principal components and spectral mixture analysis that can decompose gradients in the spectral data. The combined effect is the production of regional views of riverine landscapes separated into components of water, vegetation and soil. 4. The landscape properties of riverine landscapes that have been most successfully measured with remote sensing data include community and habitat level classification and connectivity of waterbodies with optical and radar data. Laser and radar altimetric data measured from aircraft provide land elevations at resolutions as fine as decimetres. A remaining challenge is to achieve an exact match between the categories of landscape classification from the remote sensing analysis and data from field surveys or model outputs. 5. In contrast to many landscape properties, several water properties are now routinely measured as absolute values (water surface elevation, temperature, surface sediment concentration and algal concentration) with remote sensing. New analyses of both passive and active radar data in addition have led to measurements of inundation and wetness that are providing valuable insight into the dynamics of flooding and its effect on riverine landscapes. 6. Finally, an effective examination of the variability in landscape cover includes additional analyses of remote sensing products using pattern metrics that measure the scale of patchiness and distribution of the landscape properties. These types of variability measures at the regional scale contribute to an increased understanding of the way in which spatial heterogeneity of riverine landscapes varies across scales and how landscape filters (sensu Poff, 1997) influence the evolution of these diversity patterns.     

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.     

中国陆地植被净初级生产力遥感估算

该文在综合分析已有光能利用率模型的基础上,构建了一个净初级生产力(NPP)遥感估算模型,该模型体现了3方面的特色:1)将植被覆盖分类引入模型,并考虑植被覆盖分类精度对NPP估算的影响,由它们共同决定不同植被覆盖类型的归一化植被指数(NDVI)最大值;2)根据误差最小的原则,利用中国的NPP实测数据,模拟出各植被类型的最大光能利用率,使之更符合中国的实际情况;3)根据区域蒸散模型来模拟水分胁迫因子,与土壤水分子模型相比,这在一定程度上对有关参数实行了简化,使其实际的可操作性得到加强。模拟结果表明,1989~1993年中国陆地植被NPP平均值为3.12 Pg C (1 Pg=10¹⁵ g),NPP模拟值与观测值比较接近,690个实测点的平均相对误差为4.5%;进一步与其它模型模拟结果以及前人研究结果的比较表明,该文所构建的NPP遥感估算模型具有一定的可靠性,说明在区域及全球尺度上,利用地理信息系统技术将遥感数据和各种观测数据集成在一起,并对NPP模型进行参数校正,基本上可以实现全球范围不同生态系统NPP的动态监测。     

森林碳计量方法研究进展

森林是陆地生态系统的主体,不仅是巨大的碳库而且对减缓气候变暖具有积极作用。科学有效的森林碳计量方法,有助于加深对全球碳循环过程的理解。然而,由于森林生态系统结构复杂,对森林碳计量的估算结果普遍存在精度低、不确定性高的问题。近年来,国内外发展了大量对森林碳计量进行估算的方法,主要有基于样地清查的森林植被和土壤碳估算、基于生长收获的经验模型估算、基于定量遥感雷达观测的遥感估测、基于多尺度森林生态系统网络的通量观测和陆地生态系统过程模型模拟等方法。在实际的森林碳计量中,根据不同的森林类型特征和数据获取情况,往往采取不同的碳计量方法,甚至不止一种。以生态过程模型模拟、遥感反演和数据同化技术为主要手段,基于碳通量观测数据、控制实验数据和遥感影像数据,发展多学科、多过程、多尺度的综合联网观测,充分认识森林碳循环过程中碳源/汇的时空分布特征,开展区域、洲际乃至全球尺度碳循环及其对全球变化和人类活动响应的系统性、集成性研究,以便建立高效、可靠的碳计量体系是未来林业碳计量的发展趋势。随着世界各国温室气体排放清单的编制,中国迫切需要科学的方法体系计量森林碳源/汇,提升我国在生态环境问题上的国际发言权和主导权,同时对我国森林可持续经营、生态环境保护以及美丽中国建设提供建议与支持。分析了各类森林碳计量方法的主要特征、优缺点,同时探讨了目前的森林碳计量方法存在的问题和未来的发展趋势,为不同时空尺度下森林碳计量提供参考。     

植被与水土流失关系研究进展

水土流失是世界性的环境问题之一,对人类社会可持续发展构成威胁,控制水土流失成为迫切需要,有许多水土流失控制措施,而生物措施尤其植被一直是人们研究的焦点.根据前人的研究,从斑块、坡面和流域/区域尺度总结了植被与水土流失的关系.斑块尺度植被对降雨和径流侵蚀能量具有很大的减弱或消除作用,可以改变植株底部的土壤性质,改善其结构,进而降低土壤可蚀性,增加入渗能力,从而减轻土壤侵蚀程度.不同植被类型、植被的不同层次结构,不同植被的形态结构具有不同的土壤侵蚀控制作用.坡面尺度主要从坡位、坡度、坡向对植被生长和分布格局的影响、对水土流失过程和格局的影响以及裸地-植被镶嵌格局、植被的条带格局对水土流失的影响和反映水土流失过程的景观格局指数的构建等方面进行了研究.更多是从植被恢复及其水土流失效应方面进行了探讨,为退化生态系统恢复和格局设计提供了极其有用的信息.流域/区域尺度植被与水土流失的关系更大程度上受到气候、地貌特征的影响,因此研究多从一定气候条件控制的土地覆盖（植被覆盖）及其格局的水土流失效应方面进行的.由于大尺度监测非常困难,研究多从遥感监测、GIS集成和模型模拟方面开展,是流域、区域等大尺度生态安全格局设计的有力支持.前人的研究为生态环境建设和保护提供了大量参考信息,但仍有一些问题需要进一步探讨,对此进行了初步的概括和归纳,希望能够对植被和水土流失关系的研究起到一定促进作用.