湿地土壤全氮和全磷含量高光谱模型研究

氮磷是湿地生态系统土壤中的重要营养元素,其对湿地植被生长、湿地生态系统生产力、区域富营养化变化、湿地环境生态净化功能等具有重要的影响作用。研究氮磷营养物质在湿地土壤中的分布变化特征,对湿地生态系统评估、恢复和管理具有重要的意义。以中国高纬度地区面积最大的滨海芦苇湿地——盘锦湿地为研究区,采用不同建模方法(再抽样多元逐步回归模型bootstrap SMLR和再抽样偏最小二乘回归模型bootstrap PLSR)和光谱变换技术(包络线去除CR、光谱一阶微分FD和光谱倒数的对数LR),分别建立了湿地土壤全氮和全磷含量的估算模型。基于湿地土壤实测光谱,模拟高光谱Hyperion数据和多光谱TM数据,在此基础上进行湿地土壤营养元素含量估算。对比所建反演模型的估算精度,探讨高光谱遥感技术对湿地土壤营养元素组分的估算能力和适用性。研究结果表明:bootstrap PLSR相比于bootstrap SMLR建模方法,其对研究区湿地土壤全氮和全磷含量的估算获得了较高精度;对盘锦湿地土壤全氮含量的估算,最高估算精度产生于CR光谱变换技术结合bootstrap PLSR建模;对湿地土壤全磷含量的估算,最高估算精度产生于原光谱数据结合bootstrap PLSR建模;模拟高光谱数据Hyperion对湿地土壤全氮和全磷含量的估算精度均高于模拟多光谱数据TM,模拟Hyperion的估算精度更接近于实测光谱的估算精度。     

基于光谱特征变量的湿地典型植物生态类型识别方法——以北京野鸭湖湿地为例

光谱特征变量的选择对于湿地植被识别的精度和效率有着直接的影响作用.以华北地区典型的淡水湿地——野鸭湖湿地为研究区,采用Field Spec 3野外高光谱辐射仪,获取了野鸭湖典型湿地植物的冠层光谱.以野外高光谱数据为基础,首先利用一阶导数与包络线去除的方法,分析和对比不同植物生态类型的光谱特征,选定了用于识别植物生态类型的光谱特征变量,选定的8个光谱特征变量为红边位置WP_r、红边幅值Dr、绿峰位置WP_g、绿峰幅值Rg、510 nm附近的吸收深度DEP-510和吸收面积AREA-510、675 nm附近的吸收深度DEP-675和吸收面积AREA-675.其中,7种植物生态类型的一阶导数光谱特征差异较小,吸收特征差异性相对较大.除WP_r和WP _g外,沉水植物Rg和Dr平均值最低,湿生植物的Rg平均值最高,达到0.164,栽培植物的Dr平均值最高,达到0.012.7种植物生态类型在675 nm附近的DEP-675和AREA-675均高于510 nm附近的DEP-510与AREA-510,除去栽培植物,随着水分梯度的变化,其他6种植物生态类型的吸收深度和吸收面积都表现出先升高后降低的趋势.然后利用单因素方差分析(One-way ANOVA)验证了所选光谱特征变量的区分度,在P≤0.01的置信水平下,选取的8个光谱特征变量都能够较好的区分7种植物生态类型,区分度的最小值为13,最大值为18,并且吸收特征参数的区分度优于一阶导数参数.最后应用非线性的反向传播人工神经网络(BP-ANN)与线性判别分析(FLDA)的类型识别方法,利用选定的8个光谱特征变量进行湿地植物生态类型识别,取得了较好的识别精度,两种方法的总分类精度分别达到85.5％和87.98％.单因素方差分析(One-way ANOVA)和不同分类器的分类精度表明,所选的8个光谱特征变量具有一定的普适性和可靠性.     

Ningaloo Reef: Shallow Marine Habitats Mapped Using a Hyperspectral Sensor

Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km² of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km²). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas.     

Spectral response characteristics and identification of typical plant species in Ebinur lake wetland national nature reserve (ELWNNR) under a water and salinity gradient

The spectral characteristics of variable selection are particularly important for wetland vegetation mapping. In the present study, we combined soil salt and water content with spectral data collected in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in Western China to understand the effects of soil salt and water content on plant spectra. The results showed the following: (1) the distribution of plants reflect the macroscopic response characteristics of plants on water and salt environment; (2) a certain response rule exists between the spectra of different plants under a water and salt gradient, e.g., with increase in water and salt gradient, the spectral reflectivity of salt-dilution plant decreases, and salt-exclusion plant increases; (3) a response pattern is formed between the “trilateral” characteristics of plant spectrum and water salt gradient. With the increase of salinity gradient, the “red edge”, “blue edge”, and “yellow edge” shows the most obvious changes in the 0.8 order derivatives, e.g., when the soil salt content was range from 4.2 to 8.8 g/kg, the spectral characteristics of the plants were the most obvious; (4) Fisher linear discriminant analysis found that during fractional order to integer promotion, classification accuracy of the 0.8 order derivative was higher than the integer order derivatives. Therefore, the “trilateral” characteristics of plants spectra in the 0.8 order derivatives were more accurate than the first derivative. The 0.8 order derivative was more advantageous to distinguishing plants, with a classification accuracy of 89.37%, indicating the potential of 0.8-order derivative for hyperspectral remote sensing of plants. This study introduced a fractional order derivative to hyperspectral remote sensing for the quantitative analysis of differences in the vegetation spectrum, provided new insights to the research and application of vegetation remote sensing.     

Mapping an invasive bryophyte species using hyperspectral remote sensing data

Reliable distribution maps are crucial for the management of invasive plant species. An alternative to traditional field surveys is the use of remote sensing data, which allows coverage of large areas. However, most remote sensing studies on invasive plant species focus on mapping large stands of easily detectable study species. In this study, we used hyperspectral remote sensing data in combination with field data to derive a distribution map of an invasive bryophyte species, Campylopus introflexus, on the island of Sylt in Northern Germany. We collected plant cover data on 57 plots to calibrate the model and presence/absence data of C. introflexus on another 150 plots for independent validation. We simultaneously acquired airborne hyperspectral (APEX) images during summer 2014, providing 285 spectral bands. We used a Maxent modelling approach to map the distribution of C. introflexus. Although C. introflexus is a small and inconspicuous species, we were able to map its distribution with an overall accuracy of 75 %. Reducing the sampling effort from 57 to 7 plots, our models performed fairly well until sampling effort dropped below 12 plots. The model predicts that C. introflexus is present in about one quarter of the pixels in our study area. The highest percentage of C. introflexus is predicted in the dune grassland. Our findings suggest that hyperspectral remote sensing data have the potential to provide reliable information about the degree of bryophyte invasion, and thus provide an alternative to traditional field mapping approaches over large areas.     

Effects of flooding regime on wetland plant growth and species dominance in a mesocosm experiment

Flooding regimes are a primary influence on the wetland plant community. Human-induced disturbance often changes the duration and frequency of flooding in wetlands, and has a marked influence on wetland plant composition and viability. Comprehensive studies of the environmental thresholds of wetland plants are required for the development of proper practices for wetland management and restoration after hydrological disturbance. This study provides a quantitative assessment of the establishment, growth, and community shifts in dominance of three emergent plant species (Scirpus tabernaemontani, Typha orientalis, and Zizania latifolia) typical of South Korean wetlands, under five hydrological regimes (waterlogged, low-level standing water, high-level standing water, intensive periodic flooding, and intermittent flooding) over four growing seasons. A mesocosm experiment was conducted in the campus of Seoul National University, South Korea. The number and biomass of shoots of Z. latifolia responded positively to increased water level and flooding frequency, while that of the other plants did not. Zizania latifolia outcompeted S. tabernaemontani and T. orientalis irrespective of hydrological regime. This study suggests that Z. latifolia can outcompete the other two macrophytes in the field. This study will improve our ability to predict the dynamics of wetland vegetation and so facilitate the formulation of wetland management and restoration strategies.     

Timing of white-tailed deer browsing affects wetland plant communities

White-tailed deer (Odocoileus virginianus Zimm.) have the potential to alter plant community composition and successional trajectory by browsing differentially on forb, graminoid, and woody species. The objective of this study was to determine if seasonal elimination of deer browsing changed wetland plant community composition and structure. We established 66 deer exclosure plots in two wetland vegetation communities in Canaan Valley, West Virginia, USA. Plots were established in April 2005 and monitoring was conducted in June and October, 2005–2007 to obtain data on both early and late species. Composition differed between control and treatment plots in Solidago spp.–Rubus hispidus L. communities in late-protected plots (enclosed July–October) when data were gathered in October. Community composition also varied in early-protected plots (enclosed April–July) when data were gathered in June. Forb cover increased in treatment plots in Solidago spp.–Rubus hispidus communities. Composition differed in Populus tremuloides Michx. communities in late-protected and continuously protected plots. There was no increase in cover by any wetland indicator status categories after 2 years of protection. Timing of browse played an influential role in the effect that white-tailed deer have on wetland plant communities. Our results suggest that reducing browsing pressure seasonally can increase forb species cover.     

Application of hydrogeophysical techniques to study the distribution of a burrowing crayfish in a wetland

Crayfish are important in wetland systems because of their function in soil nutrient turnover. Since many crayfishes are imperiled by anthropogenic activities, it is important to understand factors that are associated with their distribution within and among wetlands. This study investigated the soil and hydrogeological characteristics of a wetland and related them to the spatial distribution of crayfish burrows found within it. The study utilized field-collected soil cores, electrical resistivity, and ground penetrating radar to map subsurface characteristics at Bartram Forest, Baldwin County, Georgia. Wetland delineation was also conducted in the field to establish the wetland boundaries. Both 2D and 3D geophysical profiles were created. Soils samples were analyzed for grain size distribution, porosity, and hydraulic conductivity in the lab. Hydraulic conductivity of the wetland soils was also determined in the field using slug tests. Results show subsurface physical differences between crayfish inhabited zones of the wetland and those that do not have crayfish burrows.The Ambiguous Crayfish, Cambarus striatus was found in soils with a hydraulic conductivity of 0.01–0.4 m/day where soils outside of their colony boundary had a hydraulic conductivity of 0.4–1.2 m/day. Areas where C. striatus were located had a higher porosity (0.36) than areas without crayfish (0.26). Subsurface stratigraphy varied between the areas with and without burrows. C. striatus was found to live in a subsurface with relatively gradual stratigraphical boundaries when compared to surrounding areas.     

Hyperspectral Analysis of Columbia Spotted Frog Habitat

Abstract: Wildlife managers increasingly are using remotely sensed imagery to improve habitat delineations and sampling strategies. Advances in remote sensing technology, such as hyperspectral imagery, provide more information than previously was available with multispectral sensors. We evaluated accuracy of high-resolution hyperspectral image classifications to identify wetlands and wetland habitat features important for Columbia spotted frogs (Rana luteiventris) and compared the results to multispectral image classification and United States Geological Survey topographic maps. The study area spanned 3 lake basins in the Salmon River Mountains, Idaho, USA. Hyperspectral data were collected with an airborne sensor on 30 June 2002 and on 8 July 2006. A 12-year comprehensive ground survey of the study area for Columbia spotted frog reproduction served as validation for image classifications. Hyperspectral image classification accuracy of wetlands was high, with a producer's accuracy of 96% (44 wetlands) correctly classified with the 2002 data and 89% (41 wetlands) correctly classified with the 2006 data. We applied habitat-based rules to delineate breeding habitat from other wetlands, and successfully predicted 74% (14 wetlands) of known breeding wetlands for the Columbia spotted frog. Emergent sedge microhabitat classification showed promise for directly predicting Columbia spotted frog egg mass locations within a wetland by correctly identifying 72% (23 of 32) of known locations. Our study indicates hyperspectral imagery can be an effective tool for mapping spotted frog breeding habitat in the selected mountain basins. We conclude that this technique has potential for improving site selection for inventory and monitoring programs conducted across similar wetland habitat and can be a useful tool for delineating wildlife habitats.     

Ground-level spectroscopy analyses and classification of coral reefs using a hyperspectral camera

With the general aim of classification and mapping of coral reefs, remote sensing has traditionally been more difficult to implement in comparison with terrestrial equivalents. Images used for the marine environment suffer from environmental limitation (water absorption, scattering, and glint); sensor-related limitations (spectral and spatial resolution); and habitat limitation (substrate spectral similarity). Presented here is an advanced approach for ground-level surveying of a coral reef using a hyperspectral camera (400–1,000 nm) that is able to address all of these limitations. Used from the surface, the image includes a white reference plate that offers a solution for correcting the water column effect. The imaging system produces millimeter size pixels and 80 relevant bands. The data collected have the advantages of both a field point spectrometer (hyperspectral resolution) and a digital camera (spatial resolution). Finally, the availability of pure pixel imagery significantly improves the potential for substrate recognition in comparison with traditionally used remote sensing mixed pixels. In this study, an image of a coral reef table in the Gulf of Aqaba, Red Sea, was classified, demonstrating the benefits of this technology for the first time. Preprocessing includes testing of two normalization approaches, three spectral resolutions, and two spectral ranges. Trained classification was performed using support vector machine that was manually trained and tested against a digital image that provided empirical verification. For the classification of 5 core classes, the best results were achieved using a combination of a 450–660 nm spectral range, 5 nm wide bands, and the employment of red-band normalization. Overall classification accuracy was improved from 86 % for the original image to 99 % for the normalized image. Spectral resolution and spectral ranges seemed to have a limited effect on the classification accuracy. The proposed methodology and the use of automatic classification procedures can be successfully applied for reef survey and monitoring and even upscaled for a large survey.     

Multi-species monitoring of rare wetland fishes should account for imperfect detection of sampling devices

Accuracy in estimating occupancy of a threatened species is important for conservation but false absences bias many monitoring programs. Imperfect detection is especially relevant to surveys of rare wetland fishes which are often small-bodied and cryptic. Many factors influence probability of detection, including fish size and abundance, habitat characteristics and sampling devices. Imperfect detection can be addressed by accounting for probability of detection when estimating occupancy by modelling detection/non-detection data collected in replicate surveys. Three ecological specialists were once common in habitats associated with Lake Alexandrina at the terminus of the Murray–Darling Basin, Australia. The threatened Murray Hardyhead (Craterocephalus fluviatilis), Southern Pygmy Perch (Nannoperca australis) and Yarra Pygmy Perch (N. obscura) are now rare in the region following population collapses during a prolonged drought, and ongoing monitoring aims to assess their statuses for management purposes. This study compares probability of detection of the rare wetland fishes and cohabiting species during 2 years of multi-species monitoring using contrasting sampling devices (fyke and seine). The findings suggest large variations in estimated probability of detection can occur between devices for Murray Hardyhead and Southern Pygmy Perch. Yarra Pygmy Perch was undetected during the study. Overall, the findings show multi-species monitoring programs using a single sampling device may wrongly estimate the occupancy of a target fish. By accounting for imperfect detection, multi-species monitoring programs will improve inferences regarding population status, recovery and habitat quality of fishes to more accurately inform wetland management.     

How accurate are probability-based estimates of wetland extent? Results of a California validation study

Estimates of wetland and stream extent and distribution form the basis for state and federal monitoring and management programs and guide policy development decisions. The current default approach, comprehensive mapping, provides the most complete information on extent and distribution but is prohibitively expensive across large geographic areas. In contrast, probabilistic mapping produces statistical estimates of extent and distribution at a fraction of the cost of comprehensive mapping. This study provides a direct comparison to address how well probability-based estimates of wetland extent approximate results from comprehensive mapping, and the degree to which inter-mapper variability contributes to overall error in probability-based estimates. Two regions of California were selected based on existence of recent, comprehensive wetland and stream maps. Probabilistic sample plot locations were selected by generalized random tessellation stratified sampling and sample plot maps were produced from the same source imagery as the comprehensive maps. Sample maps were compared for inter-mapper variability, plot-by-plot differences between sample and comprehensive maps, and differences between sample estimates and comprehensive totals. On a plot-by-plot basis differences in mapped wetland area between comprehensive maps and probabilistic sample maps approached 50 % in either the positive or negative direction, leading to uncertainty in directly comparing maps derived from these two approaches. With application of standardized protocols and rigorous quality control measures, we were able to achieve a 97 % overall accuracy rate between independent mapping teams applying the probabilistic mapping approach. Our results suggest caution when comparing comprehensive and sample based wetland extent estimates and highlight the importance of mapper intercalibration.     

Microbial community composition and methanotroph diversity of a subarctic wetland in Russia

This study assessed the microbial diversity, activity, and composition of methane-oxidizing communities of a subarctic wetland in Russia with mosaic cover of Sphagnum mosses and lichens of the genera Cladonia and Cetraria. Potential methane-oxidizing activity of peat sampled from lichen-dominated wetland sites was higher than that in the sites dominated by Sphagnum mosses. In peat from lichen-dominated sites, major bacterial groups identified by high-throughput sequencing of the 16S rRNA genes were the Acidobacteria (35.4–41.2% of total 16S rRNA gene reads), Alphaproteobacteria (19.1–24.2%), Gammaproteobacteria (7.9–11.1%), Actinobacteria (5.5–13.2%), Planctomycetes (7.2–9.5%), and Verrucomicrobia (5.1–9.5%). The distinctive feature of this community was high proportion of Subdivision 2 Acidobacteria, which are not characteristic for boreal Sphagnum peat bogs. Methanotrophic community composition was determined by molecular analysis of the pmoA gene encoding particulate methane monooxygenase. Most (~80%) of all pmoA gene fragments revealed in peat from lichen-dominated sites belonged to the phylogenetic lineage represented by a microaerobic spiral-shaped methanotroph, “Candidatus Methylospira mobilis”. Members of the genus Methylocystis, which are typical inhabitants of boreal Sphagnum peat bogs, represented only a minor group of indigenous methanotrophs. The specific feature of a methanotrophic community in peat from lichen-dominated sites was the presence of uncultivated USCα (Upland Soil Cluster alpha) methanotrophs, which are typical for acidic upland soils showing atmospheric methane oxidation. The methanotrophic community composition in lichen-dominated sites of a tundra wetland, therefore, was markedly different from that in boreal Sphagnum peat bogs.     

叶冠尺度野鸭湖湿地植物群落含水量的高光谱估算模型

利用高光谱遥感技术定量估测野鸭湖湿地植被含水量,对于监测和诊断野鸭湖湿地植被的生理状况及生长趋势具有重要意义,也能够为高光谱遥感影像在野鸭湖湿地植被含水量诊断中的实际应用提供理论依据和技术支持.采用Field Spec 3野外高光谱辐射仪,获取了野鸭湖典型湿地植被冠层和叶片的光谱,并测定了对应的含水量.以上述实测数据为基础,首先以芦苇为例初步探明了不同含水量水平下典型湿地植被冠层和叶片光谱反射率的响应模式,然后采用相关性及单变量线性与非线性拟合分析技术,从冠层和叶片两种层次,对不同尺度下的含水量与“三边”参数及高光谱植被指数进行了分析拟合,并采用交叉检验中的3K-CV方法对估算模型进行了测试和检验,确立了不同尺度下野鸭湖湿地植被含水量的定量监测模型.结果表明:(1)随着含水量水平的增加,芦苇冠层与叶片光谱在可见光波段(350-760 nm)和红外波段(760-2500 nm)的反射率均呈逐渐降低趋势.(2)不同尺度含水量与选取的光谱特征参数整体上相关性较强,与“三边”参数基本上都呈极显著相关,相关系数最大达到0.906;与高光谱指数全部呈极显著相关,相关系数最小为0.455,最大达到0.919,并通过选取不同尺度上相关性最佳的光谱特征参数,分别基于“三边”参数和高光谱植被指数构建了不同尺度下的含水量估算模型.其中,冠层尺度下,黄边面积(SDy)与SRWI( Simple Ratio Water Index)的估算效果最好,估算模型分别为y=-9.462x2 -2.671x+0.608和y=0.219e1.010x;叶片尺度下,红边面积(SDr)与WI( Water Index)的估算效果最好,估算模型分别为y=0.562x+0.376和y=2.028x2 -0.476x-1.009.通过3K-CV的交叉验证,不同尺度下的含水量估算模型均取得了较为理想的预测精度,预测精度的最小值为94.92％,最大值为97.06％,表明估测模型具有较高的可靠性与普适性.(3)高光谱植被指数与含水量拟合方程的拟合度相对高于“三边”参数与之建立方程的拟合度,说明多波段组合的光谱特征参数更适合含水量的判别.     

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.     

Working with land and sea rangers to tackle tropical wetland restoration and conservation on the north-western islands,Torres Straits,Australia

Importance of community stakeholder participation in coastal freshwater and tidal wetland monitoring and restoration has become increasingly recognised. In Australia, Land and Sea Rangers (LSR) are appointed land and sea custodians from local indigenous communities and under guidance of experts learn a range of scientifically relevant and rigorous sampling techniques to protect and conserve Country. Scientific training to build LSR confidence to tackle restoration and conservation of sensitive and culturally important wetlands is shown here. Between May 2014 and May 2015 three training campaigns were completed where LSR on Boigu and Saibai Islands (the most northern islands in the Torres Straits, Australia), completed water quality and wetland flora/fauna surveys across both islands. Forty wetland fauna species were documented (with a similar wetland assemblage on each ANOSIM P?>?0.4) comprising 35 fish species (including the invasive freshwater climbing perch, Anabas testudineus), two crustaceans, a freshwater turtle (Chelodina oblonga) (a relic freshwater species after the last sea level rise approximately 6,000 years ago in the region), and two mangrove snakes (Myron richardsoni and Fordonia leucobalia) (both snake records represent a range extension). This data was presented at community workshops with the purpose to build LSR confidence, and with the community, develop a plan to conserve wetland cultural and environmental values. Five thematic wetland conservation themes were identified which resulted in agreeing to management actions necessary on both islands. Since the inception of this program in 2014, additional LSR restoration and monitoring programs have extended to wetlands on other islands in the Torres Straits. We advocate the need for more remote area wetland monitoring and management programs facilitated through LSR programs.     

Hyperspectral discrimination of coral reef benthic communities in the western Caribbean

Determining a subset of wavelengths that best discriminates reef benthic habitats and their associated communities is essential for the development of remote sensing techniques to monitor them. This study measured spectral reflectance from 17 species of western Caribbean reef biota including coral, algae, seagrasses, and sediments, as well as healthy and diseased coral. It sought to extend the spectral library of reef-associated species found in the literature and to test the spectral discrimination of a hierarchy of habitats, community groups, and species. We compared results from hyperspectral reflectance and derivative datasets to those simulated for the three visible multispectral wavebands of the IKONOS sensor. The best discriminating subset of wavelengths was identified by multivariate stepwise selection procedure (discriminant function analysis). Best discrimination at all levels was obtained using the derivative dataset based on 6–15 non-contiguous wavebands depending on the level of the classification, followed by the hyperspectral reflectance dataset which was based on as few as 2–4 non-contiguous wavebands. IKONOS wavebands performed worst. The best discriminating subset of wavelengths in the three classification resolutions, and particularly those of the medium resolution, was in agreement with those identified by Hochberg and Atkinson (2003) and Hochberg et al. (2003) for reef communities worldwide. At all levels of classification, reflectance wavebands selected by the analysis were similar to those reported in recent studies carried out elsewhere, confirming their applicability in different biogeographical regions. However the greater accuracies achieved using the derivative datasets suggests that hyperspectral data is required for the most accurate classification of reef biotic systems.     

Mapping multiple plant species abundance patterns - A multiobjective optimization procedure for combining reflectance spectroscopy and species ordination

Nature conservation and ecological restoration crucially depends on the knowledge about spatial patterns of plant species that control habitat conversion and disturbance regimes. Especially, species abundances are capable of indicating early development tendencies for setting habitat management strategies. This study demonstrates the transfer of field spectroscopy to hyperspectral imagery to map multiple plant species abundances in an open dryland area using two imaging spectrometers in two different phenological phases. We show that species abundances can partially be described by multiple gradients forming different coordinates in a contour map. For this purpose, species abundances were projected into an ordination space using non-metric multidimensional scaling and subsequent spatial interpolation. It was demonstrated that different gradients can be modeled in a Partial Least Squares regression framework resulting in distinct spectral features for certain gradient directions. We combine both objectives in a multiobjective NSGA-II procedure to maximize the quantitative determination of species abundance in ordination and spectral predictability in related field spectra, simultaneously. NSGA-II was finally used to select optimal spectral models for n = 35 single species that were transferred to hyperspectral imagery for mapping purpose. We can show that abundance predictabilities can be evaluated on the basis of individual model performances that hold different spectral features for each species in a designated phenological phase. Finally, we present spatially explicit multi-species maps for the best n = 18 and abundance maps for n = 8 models that could be linked to patterns of species richness, coexistence, succession stages and habitat type conditions.     

Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities,Taiwan

The purpose of this study is to apply different remote sensing techniques to monitor shifting mangrove vegetation in the Danshui River estuary in Taipei, Taiwan, in order to evaluate a long-term wetland conservation strategy compromising between comprehensive wetland ecosystem management and urban development. In the Danshui estuary, mangrove dominated by Kandelia candel is the major vegetation, and a large area of marsh with freshwater grasses has been protected in three reserves along the river shore. This study applied satellite imagery from different remote sensors of various resolutions for spectral analysis in order to compare shifting wetland vegetation communities at different times. A two-stage analytical process was used for extracting vegetation area and types. In the first-stage, a normalized difference vegetation index (NDVI) was adopted to analyze SPOT, Landsat, and QuickBird imagery to obtain the spatial distribution of vegetation covers. In the second stage, a maximum likelihood classification (MLC) program was used to classify mangrove and non-mangrove areas. The results indicated that the spatial distribution of mangroves expanded 15.18 and 40 ha in two monitoring sites in 10 years, demonstrating the success of establishing reserves for protecting mangrove habitats. The analytical results also indicated that satellite imagery can easily discern the difference in characteristics between imagery of mangrove and other vegetation types, and that the logistical disadvantages of monitoring long-term vegetation community changes as well as evaluating an inaccessible area may be overcome by applying remote sensing techniques.     

Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

1. This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water‐quality standards. 2. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near‐infrared (NIR)‐Green Angle Index (NGAI) and normalized water absorption depth (D_H)_, at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. 3. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral‐derived NDVI. The IKONOS‐based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the D_H. 4. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High‐resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. 5. Interpretation of biophysical parameters derived from high‐resolution satellite or airborne imagery should prove to be a valuable approach for assessing the effectiveness of management practices for controlling aquatic plant growth in inland waters, as well as for routine monitoring of aquatic plants in lakes and suitable lentic environments.