Synergy of airborne LiDAR and Worldview-2 satellite imagery for land cover and habitat mapping: A BIO_SOS-EODHaM case study for the Netherlands

A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications.     

Importance of landscape features and Earth observation derived habitat maps for modelling amphibian distribution in the Alta Murgia National Park

Traditionally, analyses of relationships between amphibians and habitat focused on breeding environments (i.e., pond features) more than on the features of the surrounding environment. Nevertheless, for most amphibians the terrestrial phase is longer than the aquatic phase, and consequently landscape features (i.e., habitat mosaics) may have an important role for modelling amphibian distribution.There were different aims in this analysis. Firstly, we compared the effectiveness of the information provided by land cover/use (LC/LU) classes and habitat classes defined according to a new habitat taxonomy named General Habitat Category (GHC), which is based on the concept of biological forms of dominant vegetation and class naturalness. The GHC map used was obtained from a pre-existing validated LC/LU map, by integrating spectral and spatial measurements from very high resolution Earth observation data according to ecological expert rules involving concepts related to spatial and temporal relationships among LC/LU and habitat classes.Then, we investigated the importance for amphibians of the landscape surrounding ponds within the Italian Alta Murgia National Park. The work assessed whether LC/LU classes in pond surrounds are important for the presence/absence of amphibians in this area, and identified which classes are more important for amphibians. The results obtained can provide useful indications to management strategies aiming at the conservation of amphibians within the study area. An information-theoretic approach was adopted to assess whether GHC maps allow to improve the performance of species distribution models. We used the Akaike's Information Criterion (AICc) to compare the effectiveness of GHC categories versus LC/LU categories in explaining the presence/absence of pool frogs. AICc weights suggest that GHC categories can better explain the distribution of frogs, compared to LC/LU classes.     

Object-based class modelling for multi-scale riparian forest habitat mapping

Object-based class modelling allows for mapping complex, hierarchical habitat systems. The riparian zone, including forests, represents such a complex ecosystem. Forests within riparian zones are biologically high productive and characterized by a rich biodiversity; thus considered of high community interest with an imperative to be protected and regularly monitored. Satellite earth observation (EO) provides tools for capturing the current state of forest habitats such as forest composition including intermixture of non-native tree species. Here we present a semi-automated object based image analysis (OBIA) approach for the mapping of riparian forests by applying class modelling of habitats based on the European Nature Information System (EUNIS) habitat classifications and the European Habitats Directive (HabDir) Annex 1. A very high resolution (VHR) WorldView-2 satellite image provided the required spatial and spectral details for a multi-scale image segmentation and rule-base composition to generate a six-level hierarchical representation of riparian forest habitats. Thereby habitats were hierarchically represented within an image object hierarchy as forest stands, stands of homogenous tree species and single trees represented by sunlit tree crowns. 522 EUNIS level 3 (EUNIS-3) habitat patches with a mean patch size (MPS) of 12,349.64 m² were modelled from 938 forest stand patches (MPS = 6868.20 m²) and 43,742 tree stand patches (MPS = 140.79 m²). The delineation quality of the modelled EUNIS-3 habitats (focal level) was quantitatively assessed to an expert-based visual interpretation showing a mean deviation of 11.71%.     

EARTH RESOURCE OBSERVATION AND ENVIRONMENTAL PROTECTION

A senior Russian remote sensing specialist describes plans for the development of the Earth Observation System (EOS), providing interesting insights into Russian interest and expertise in satellite-based global monitoring of the environment. The system of scientific instrumentation developed for earth resource monitoring satellites and stages in the implementation of an observation program system are described. Changes in the implementation of the program, reflecting new priorities brought about by budgetary constraints, also are considered.     

地球资源环境动态监测技术的现状与未来

针对国家全球化战略和迫切需要解决的全球环境和资源问题,本文阐述了国内外地球资源环境动态监测技术主要研究进展,发现存在地球资源环境监测高精度产品缺乏、动态监测能力不完备、遥感信息服务及时性和便携不足等主要问题。在此基础上,提出中国迫切需要发展面向全球和重点区域的持续、动态观测能力,建立全球视野的资源环境动态监测产品和应用系统,突破全球资源环境研究的理论和关键技术,建立全球资源环境遥感监测指标和技术体系,形成全球立体协同观测、资源汇聚优化、信息智能处理、云平台业务应用的自主技术体系,完善支撑任务驱动的数据汇聚、模型调度、产品生成等在线遥感信息服务能力,发布全球、洲际和全国高质量空间要素遥感信息产品、专题应用系统、技术报告等成果。最终为全球资源环境研究提供知识发现的数据和服务,支撑中国在全球资源环境监测评估、重大灾害事件监测预警、应对国家安全与全球变化等领域的服务。     

Satellite Earth observation data to identify anthropogenic pressures in selected protected areas

Protected areas are experiencing increased levels of human pressure. To enable appropriate conservation action, it is critical to map and monitor changes in the type and extent of land cover/use and habitat classes, which can be related to human pressures over time. Satellite Earth observation (EO) data and techniques offer the opportunity to detect such changes. Yet association with field information and expert interpretation by ecologists is required to interpret, qualify and link these changes to human pressure. There is thus an urgent need to harmonize the technical background of experts in the field of EO data analysis with the terminology of ecologists, protected area management authorities and policy makers in order to provide meaningful, context-specific value-added EO products. This paper builds on the DPSIR framework, providing a terminology to relate the concepts of state, pressures, and drivers with the application of EO analysis. The type of pressure can be inferred through the detection of changes in state (i.e. changes in land cover and/or habitat type and/or condition). Four broad categories of changes in state are identified, i.e. land cover/habitat conversion, land cover/habitat modification, habitat fragmentation and changes in landscape connectivity, and changes in plant community structure. These categories of change in state can be mapped through EO analyses, with the goal of using expert judgement to relate changes in state to causal direct anthropogenic pressures. Drawing on expert knowledge, a set of protected areas located in diverse socio-ecological contexts and subject to a variety of pressures are analysed to (a) link the four categories of changes in state of land cover/habitats to the drivers (anthropogenic pressure), as relevant to specific target land cover and habitat classes; (b) identify (for pressure mapping) the most appropriate spatial and temporal EO data sources as well as interpretations from ecologists and field data useful in connection with EO data analysis. We provide detailed examples for two protected areas, demonstrating the use of EO data for detection of land cover/habitat change, coupled with expert interpretation to relate such change to specific anthropogenic pressures. We conclude with a discussion of the limitations and feasibility of using EO data and techniques to identify anthropogenic pressures, suggesting additional research efforts required in this direction.     

Assessment of forest fragmentation in the conservation priority Dudhwa landscape,India using FRAGSTATS computed class level metrics

The Dudhwa landscape, a priority conservation area representing Terai ecosystem (woodland-grassland-wetland complex) has witnessed a sea change in past 150 years or so on account of long history of forest management, changes in land use, and rapid economic development. We assessed fragmentation in two constituent protected areas (Dudhwa National Park-DNP and Katerniaghat Wildlife Sanctuary-KAT) of the landscape due to forest management activities (clear cutting, development of rail and road network, and plantations) and compared the magnitude among them using select metrics at the forest class level. We applied FRAGSTATS spatial pattern analysis software (ver.3.3) on different forest classes deciphered by land use/ cover maps generated using IRS P6 LISS IV digital data. Study amply revealed that the forests in DNP are less fragmented and of better habitat quality than forests of KAT. The set of seven metrics (patch density, mean patch size, edge density, mean shape index, mean core area, mean nearest neighbour, and interspersion and juxtaposition index) at the class level quantified in the present study are simple and proved useful for quantifying complex spatial processes and can be used as an effective means of monitoring in Dudhwa landscape.     

Assessment of the three factors affecting Myanmar's forest cover change using Landsat and MODIS vegetation continuous fields data

Long-term observation of the earth is essential for studying the factors affecting global environmental changes. Digital earth technology can facilitate the monitoring of global environmental change with its ability to process vast amounts of information. In this study, we map the forest cover change of Myanmar from 2000 to 2005 using a training data automation procedure and support vector machines algorithm. Our results show that Myanmar's forests have declined 0.68% annually over this six-year period. We validated our derived change results and found the overall accuracy to be greater than 88%. We also assessed forest loss from protected areas, areas close to roads, and areas subject to fire, which were most likely to lose forested area. The results revealed the main reasons for forest losses in some hotspots to be increased agricultural conversion, fire, and the construction of highways. This information is useful for identifying the driving forces behind forest changes and to support environmental policy development in Myanmar.     

北斗Ⅱ载波相位差分定位法在高填方变形监测中的应用

通过对北斗Ⅱ载波相位差分定位技术在高填方变形监测中的应用研究,包括实时动态差分定位(RTK)和静态相对定位,构建了完整的北斗Ⅱ高填方变形监测软硬件系统。该系统采用基于贯序极限学习机的卫星信号周跳探测与修复方法进行数据预处理,并利用基于卫星历元数的分层置信滤波算法对静态相对定位结果进行修正,提高了定位结果的准确性和可靠性。通过自制的精度测试装置,以百分表和钢尺测量的位移变化量为相对真值,完成了上述变形监测系统的精度试验。试验结果表明:该系统的RTK高程测量精度优于2 cm,静态高程测量精度优于2 mm,静态水平位移测量精度优于1.5 mm,可有效抑制卫星信号周跳和卫星历元数量较少等因素的影响。     

Comparative assessment of thematic accuracy of GLC maps for specific applications using existing reference data

Inputs to various applications and models, current global land cover (GLC) maps are based on different data sources and methods. Therefore, comparing GLC maps is challenging. Statistical comparison of GLC maps is further complicated by the lack of a reference dataset that is suitable for validating multiple maps. This study utilizes the existing Globcover-2005 reference dataset to compare thematic accuracies of three GLC maps for the year 2005 (Globcover, LC-CCI and MODIS). We translated and reinterpreted the LCCS (land cover classification system) classifier information of the reference dataset into the different map legends. The three maps were evaluated for a variety of applications, i.e., general circulation models, dynamic global vegetation models, agriculture assessments, carbon estimation and biodiversity assessments, using weighted accuracy assessment. Based on the impact of land cover confusions on the overall weighted accuracy of the GLC maps, we identified map improvement priorities. Overall accuracies were 70.8 ± 1.4%, 71.4 ± 1.3%, and 61.3 ± 1.5% for LC-CCI, MODIS, and Globcover, respectively. Weighted accuracy assessments produced increased overall accuracies (80–93%) since not all class confusion errors are important for specific applications. As a common denominator for all applications, the classes mixed trees, shrubs, grasses, and cropland were identified as improvement priorities. The results demonstrate the necessity of accounting for dissimilarities in the importance of map classification errors for different user application. To determine the fitness of use of GLC maps, accuracy of GLC maps should be assessed per application; there is no single-figure accuracy estimate expressing map fitness for all purposes.     

Updating the Phase 1 habitat map of Wales, UK, using satellite sensor data

The Phase 1 Survey is the most comprehensive and widely used national level map of semi-natural habitats in Wales. However, the survey was based largely on field survey and was conducted over several decades, before being completed in 1997. Given that resources for a repeat survey were limited, this study has used an object-orientated rule-based classification implemented within eCognition of multi-temporal satellite sensor data acquired between 2003 and 2006 to map semi-natural habitats and agricultural land across Wales, thereby allowing a progressive update of the Phase 1 Survey. The classification of objects to Phase 1 habitat classes was undertaken in two steps; firstly the landscape of Wales was divided into objects using orthorectified SPOT-5 High Resolution Geometric (HRG) reflectance data (10 m spatial resolution) and Land Parcel Information System (LPIS) boundaries. A rule-base was then developed to progressively discriminate and map the distribution of 105 sub-habitats across Wales based on time-series of SPOT HRG, Terra-1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Indian Remote Sensing Satellite (IRS) LISS-3 data, derived datasets (e.g., vegetation indices, fractional images) and ancillary information (e.g., topography). The rules coupled knowledge of ecology and the information content of these remote sensing data using a combination of thresholds, Boolean operations and fuzzy membership functions. A second rule-base was then developed to translate the more detailed sub-habitat classification to Phase 1 habitat classes. Indicative accuracies of the revised Phase 1 mapping, based on comparisons with the later Phase 2 survey (for selected habitats), were >80% overall and typically between 70% and 90% for many classes. Through this exercise, Wales has become the first country in Europe to produce a national map of habitats (as opposed to land cover) through object-orientated classification of satellite sensor data. Furthermore, the approach can be adapted to allow continual monitoring of the extent and condition of habitats and agricultural land.     

Soil Survey Scale and its Effect on Land use Planning

Soil survey maps compiled at a variety of scales (1:5,000; 1:100,000; 1:500,000) were incorporated into a GIS and compared in terms of the number of soil classes (and discrete soil units belonging to these classes) that could be identified on the basis of the System of Analysis for Agricultural Planning (SAMPA). Significant differences in the number of soil classes were observed between the detailed (1:5,000) survey and the two others. The semi-detailed (1:100,000) and the recognition (1:500,000) maps did not differ in terms of the number of soil classes depicted, but there were nonetheless differences in soil classification, which has a direct bearing on their utility for land use planning.     

遥感与中国可持续发展:机遇和挑战

中国要实现可持续发展,必须积极应对资源短缺、环境恶化、海洋开发和气候变化等一系列重大资源环境问题;随着全球经济发展一体化进程加快,中国必须以全球视角研究和解决面临的资源环境问题。遥感在地球科学、环境科学、资源科学与全球变化研究中具有宏观动态的优点,是不可替代的全球观测手段,是实施可持续发展战略的基础性技术支撑。本文回顾了遥感科学技术进步的历程,总结了国际上围绕可持续发展所开展的全球遥感科学计划,分析了中国遥感现状和服务于可持续发展的前景,并结合国际上地球综合观测系统的发展态势,提出了中国遥感科学技术发展面临的挑战和机遇,进一步阐述了遥感发展面临的建立地球综合观测系统之系统、高精度遥感模型与参数反演、遥感产品真实性检验与遥感性能判据及测试系统、遥感数据与地球系统模式同化、遥感大数据与主动服务等前沿科学与技术问题。最后指出遥感要更好地服务于社会可持续发展,服务于国家的全球战略,服务于国民经济建设;必须创新遥感应用服务模式,加快遥感产业化和商业化进程;建议推进卫星观测系统的商业化,加快无人机遥感发展,促进遥感应用市场化。     

BDS监测站坐标维持的探讨

北斗二代卫星导航系统（BDS）监测站建设是在北斗一代的基础上进行的,为方便管理,监测天线安装在各监测站楼顶上,稳定性不够,同时北斗二代卫星导航系统目前采用的2000中国大地坐标系,归算误差较大。本文针对这些问题,分析了北斗卫星导航系统监测站由时变效应引起的位置变化量和地表沉降对监测站坐标维持的影响,论述了目前时变效应处理方法存在的误差的量级,讨论了美国GPS所使用的WGS-84坐标框架的维持与更新方法。最后,结合北斗卫星导航系统监测站的建设现状,从监测站的建设、数据处理等方面提出了建立测站坐标时间序列,维持北斗卫星导航系统监测站坐标的可行性方法,并提出了意见与建议。      

Applications of generalized additive model (GAM) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea

This study aims to illustrate how remotely sensed oceanic variables and fishing operations data can be used to predict suitable habitat of fishery resources in Geographic Information System. We used sea surface height anomaly (SSHa), sea surface temperature (SST), chlorophyll concentration (CC), photosynthetically active radiation (PAR) and fishing depth as predictor variables. Fishery data of Indian squid (Loligo spp.) and catfish (Tachysurus spp.) for study period (1998–2004) were segregated randomly to create training and validation. Catch was normalized into Catch per unit Effort (kg h^?1). Generalized additive modelling was performed on training data and then tested on validation data. Suitable ranges of SST, CC, SSHa and PAR for different species distributions were derived and integrated to predict their spatial distributions. Results indicated good match between predicted and actual catch. Monthly probability maps of predicted habitat areas coincide with high catch of the particular month for the study period.     

Mapping and characterizing the vegetation types of the Democratic Republic of Congo using SPOT VEGETATION time series

The need for quantitative and accurate information to characterize the state and evolution of vegetation types at a national scale is widely recognized. This type of information is crucial for the Democratic Republic of Congo, which contains the majority of the tropical forest cover of Central Africa and a large diversity of habitats. In spite of recent progress in earth observation capabilities, vegetation mapping and seasonality analysis in equatorial areas still represent an outstanding challenge owing to high cloud coverage and the extent and limited accessibility of the territory. On one hand, the use of coarse-resolution optical data is constrained by performance in the presence of cloud screening and by noise arising from the compositing process, which limits the spatial consistency of the composite and the temporal resolution. On the other hand, the use of high-resolution data suffers from heterogeneity of acquisition dates, images and interpretation from one scene to another. The objective of the present study was to propose and demonstrate a semi-automatic processing method for vegetation mapping and seasonality characterization based on temporal and spectral information from SPOT VEGETATION time series. A land cover map with 18 vegetation classes was produced using the proposed method that was fed by ecological knowledge gathered from botanists and reference documents. The floristic composition and physiognomy of each vegetation type are described using the Land Cover Classification System developed by the FAO. Moreover, the seasonality of each class is characterized on a monthly basis and the variation in different vegetation indicators is discussed from a phenological point of view. This mapping exercise delivers the first area estimates of seven different forest types, five different savannas characterized by specific seasonality behavior and two aquatic vegetation types. Finally, the result is compared to two recent land cover maps derived from coarse-resolution (GLC2000) and high-resolution imagery (Africover).     

中国对全球地球观测系统的贡献

进一步认识地球、关注地球发展规律, 保护人类共同家园已成为世界各国政府的共识。共同发展地球观测技术, 提高对地观测能力成为新世纪世界各国的共同要求。2003年发起, 2005年由欧盟组织的地球观测部长级峰会上通过了全球综合地球观测系统(Global Earth Observation System of Systems, GEOSS)十年执行计划, 构成了世界范围内地球观测领域国际科技合作的主流。中国地球观测领域呈现出快速发展的趋势, 并提出了该领域的全球性发展战略, 预示着中国将在国际地球观测领域发挥越来越重要的作用。文章介绍了中国地球观测领域发展现状和趋势, 在分析中国参与全球地球观测领域国际合作现状及目前存在问题的基础上, 提出进一步促进中国参与该领域国际合作, 为中国乃至国际社会发展做出重要贡献的建议。     

Land Cover Mapping for Namdapha National Park (Arunachal Pradesh), India Using Harmonized Land Cover Legends

Since last few decades RS-GIS is playing vital role in studying and mapping spatiotemporal responses of land cover, however, as a matter of fact, the mapping outputs largely depend on the expert's/user's preferences because location specific and people specific land cover classification systems are adopted autonomously for image classification in GIS. This may actually lead to an ambiguous definition of a particular land cover type when such different maps are compared at global level. In 1993, FAO and UNEP started efforts for development of a software tool know as LCCS which is a comprehensive standardized tool capable of providing land cover characterization to all possible land cover types in the world regardless of spatial relevance, mapping scale, data collection method etc. Adding to the global efforts of land cover legend harmonization and mapping, this study presents development of harmonized land cover legends for Namdapha National Park located in north-eastern Indian Himalayan region using LCCS and subsequent mapping. The potential of Remote Sensing (RS) and Geographical Information Systems (GIS) in forest/land cover mapping is very well recognized. Therefore, adopting the developed harmonized legends for the study area, land cover mapping was done using RS-GIS approach.     

Use of landsat ETM+ SLC-off segment-based gap-filled imagery for crop type mapping

Failure of the Scan Line Corrector (SLC) on the Landsat ETM+ sensor has had a major impact on many applications that rely on continuous medium resolution imagery to meet their objectives. The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) program uses Landsat imagery as the primary source of data to produce crop-specific maps for 20 states in the USA. A new method has been developed to fill the image gaps resulting from the SLC failure to support the needs of Landsat users who require coincident spectral data, such as for crop type mapping and monitoring. We tested the new gap-filled method for a CDL crop type mapping project in eastern Nebraska. Scan line gaps were simulated on two Landsat 5 images (spring and late summer 2003) and then gap-filled using landscape boundary models, or segment models, that were derived from 1992 and 2002 Landsat images (used in the gap-fill process). Various date combinations of original and gap-filled images were used to derive crop maps using a supervised classification process. Overall kappa values were slightly higher for crop maps derived from SLC-off gap-filled images compared to crop maps derived from the original imagery (0.3–1.3% higher). Although the age of the segment model used to derive the SLC-off gap-filled product did not negatively impact the overall agreement, differences in individual cover type agreement did increase (?0.8%–1.6% using the 2002 segment model to ?5.0–5.1% using the 1992 segment model). Classification agreement also decreased for most of the classes as the size of the segment used in the gap-fill process increased.     

Capturing the fugitive: Applying remote sensing to terrestrial animal distribution and diversity

Amongst many ongoing initiatives to preserve biodiversity, the Millennium Ecosystem Assessment again shows the importance to slow down the loss of biological diversity. However, there is still a gap in the overview of global patterns of species distributions. This paper reviews how remote sensing has been used to assess terrestrial faunal diversity, with emphasis on proxies and methodologies, while exploring prospective challenges for the conservation and sustainable use of biodiversity. We grouped and discussed papers dealing with the faunal taxa mammals, birds, reptiles, amphibians, and invertebrates into five classes of surrogates of animal diversity: (1) habitat suitability, (2) photosynthetic productivity, (3) multi-temporal patterns, (4) structural properties of habitat, and (5) forage quality. It is concluded that the most promising approach for the assessment, monitoring, prediction, and conservation of faunal diversity appears to be the synergy of remote sensing products and auxiliary data with ecological biodiversity models, and a subsequent validation of the results using traditional observation techniques.