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.     

Monitoring the dynamics of an invasive emergent macrophyte community using operational remote sensing data

Potamogeton crispus L. (curly pondweed) is a cosmopolitan aquatic macrophyte considered invasive in North America and elsewhere. Its range is expanding and, on individual water bodies, its coverage can be dynamic both within and among years. In this study, we evaluate the use of free and low-cost satellite remote sensing data to monitor a problematic emergent macrophyte community dominated by P. crispus. Between 2000 and 2006, we acquired eight satellite images of 24,000-ha Lake Sharpe, South Dakota (USA). During one of the dates for which satellite imagery was acquired, we sampled the lake for P. crispus and other emergent macrophytes using GPS and photography for documentation. We used cluster analysis to assist in classification of the satellite imagery and independently validated results using the field data. Resulting estimates of emergent macrophyte coverage ranged from less than 20 ha in 2002 to 245 ha in 2004. Accuracy assessment indicated 82% of image pixels were correctly classified, with errors being primarily due to failure to identify emergent macrophytes. These results emphasize the dynamic nature of P. crispus-dominated macrophyte communities and show how they can be effectively monitored over large areas using low-cost remote sensing imagery. While results may vary in other systems depending on water quality and local flora, such an approach could be applied elsewhere and for a variety of macrophyte communities.     

Mapping three invasive weeds using airborne hyperspectral imagery

Invasive plant species present a serious problem to the natural environment and have adverse ecological and economic impacts on both terrestrial and aquatic ecosystems they invade. This article presents three case studies on the use of hyperspectral remote sensing for mapping invasive plant species in both terrestrial and aquatic environments. Methods and procedures for acquisition, processing and classification of airborne hyperspectral imagery as well as accuracy assessment are presented. Examples are excerpted and adapted from published work to illustrate how airborne hyperspectral imagery has been used to map two terrestrial weeds, Ashe juniper (Juniperus ashei Buchholz) and Broom snakeweed [Gutierrezia sarothrae (Pursh.) Britt. and Rusby], and one aquatic weed, waterhyacinth [Eichhornia crassipes (Mart.) Solms], in Texas. In addition to the standard classification methods used in the previous studies, a spectral unmixing technique, mixture tuned matched filtering (MTMF), was applied to the three study cases and the classification results are reported in this paper. A brief discussion is provided on the considerations of different types of remote sensing imagery for mapping invasive weeds.     

Habitat suitability modelling of an invasive plant with advanced remote sensing data

Aim  Lepidium latifolium (Brassicaceae; perennial pepperweed) is a noxious Eurasian weed invading riparian and wetland areas of the western USA. Understanding which sites are most susceptible to invasion by L. latifolium will allow more efficient management of this weed. We assessed the ability of advanced remote sensing techniques to develop habitat suitability models for L. latifolium .
Location  San Francisco Bay/Sacramento-San Joaquin River Delta, California, USA.
Methods  Lepidium latifolium distribution was mapped with hyperspectral image data of Rush Ranch Open Space Preserve, providing presence/absence data to train and validate habitat models. A high-resolution light detection and ranging digital elevation model was used to derive predictor environmental variables (distance to channel, distance to upland, elevation, slope, aspect and convexity). Aggregate decision tree models were used to predict the potential distribution of this species.
Results  Lepidium latifolium infested two zones: near the marshland–upland margin and along channels within the marsh. Topographical data, which are typically strongly correlated with wetland species distributions, were relatively unimportant to L. latifolium occurrence, although relevant microtopography information, particularly relative elevation, was subsumed in the distance to channel variable. The map of potential L. latifolium distribution reveals that Rush Ranch contains considerable habitat that it is susceptible to continued invasion.
Main conclusions  Lepidium latifolium invades relatively less stressful sites along the inundation and salinity gradients. Advanced remote sensing datasets were shown to be sufficient for species distribution modelling. Remote sensing offers powerful tools that deserve wider use in ecological research and management.     

Correlating species and spectral diversities using hyperspectral remote sensing in early‐successional fields

Advances in remote sensing technology can help estimate biodiversity at large spatial extents. To assess whether we could use hyperspectral visible near‐infrared (VNIR) spectra to estimate species diversity, we examined the correlations between species diversity and spectral diversity in early‐successional abandoned agricultural fields in the Ridge and Valley ecoregion of north‐central Virginia at the Blandy Experimental Farm. We established plant community plots and collected vegetation surveys and ground‐level hyperspectral data from 350 to 1,025 nm wavelengths. We related spectral diversity (standard deviations across spectra) with species diversity (Shannon–Weiner index) and evaluated whether these correlations differed among spectral regions throughout the visible and near‐infrared wavelength regions, and across different spectral transformation techniques. We found positive correlations in the visible regions using band depth data, positive correlations in the near‐infrared region using first derivatives of spectra, and weak to no correlations in the red‐edge region using either of the two spectral transformation techniques. To investigate the role of pigment variability in these correlations, we estimated chlorophyll, carotenoid, and anthocyanin concentrations of five dominant species in the plots using spectral vegetation indices. Although interspecific variability in pigment levels exceeded intraspecific variability, chlorophyll was more varied within species than carotenoids and anthocyanins, contributing to the lack of correlation between species diversity and spectral diversity in the red‐edge region. Interspecific differences in pigment levels, however, made it possible to differentiate these species remotely, contributing to the species‐spectral diversity correlations. VNIR spectra can be used to estimate species diversity, but the relationships depend on the spectral region examined and the spectral transformation technique used.     

Identifying plants under natural gas micro-leakage stress using hyperspectral remote sensing

Natural gas is an important clean energy source. The demand for, and consumption of, natural gas have been increasing in recent years. Slight natural gas leakage can occur during transportation, which can have a negative impact on the environment, economy, and safety. However, it is relatively difficult to directly detect natural gas microleakage. Hyperspectral remote sensing technology is useful for analyzing the spectral characteristics of vegetation near leakage areas, thereby indirectly obtaining leakage information. In this study, a field experiment was designed to simulate natural gas leakage from an underground pipeline and gas stress on three plant species. The canopy spectral reflectance of the vegetation throughout the growth period of the plants was collected and analyzed. Variational mode decomposition was then used to decompose the spectra. Based on the stress distance (SD) and intrinsic mode functions, it was found that the second intrinsic mode function, with a decomposition scale of 32, was sensitive to gas stress. According to the results of SD, the bands (616 and 829 nm) sensitive to natural gas stress for the three plant species were extracted, and the variational mode decomposition index (VMDI) was constructed. The Jeffries–Matusita distance (JMD) was used to quantitatively evaluate the VMDI index and three indices were used to evaluate the ability to recognize stress. It was found that the index proposed in this study could identify stressed wheat and grass one week earlier than other indices and could better identify stressed vegetation throughout the phenological cycle (JMD > 1.8). The results show that the proposed index can be used as a reliable method to identify natural gas-stressed plants, and that hyperspectral technology is promising for detecting the location of natural gas leaks from underground pipelines.     

Predicting species distributions across the Amazonian and Andean regions using remote sensing data

Aim We explore the utility of newly available optical and microwave remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and QuikSCAT (QSCAT) instruments for species distribution modelling at regional to continental scales. Using eight Neotropical species from three taxonomic groups, we assess the extent to which remote sensing data can improve predictions of their geographic distributions. For two bird species, we investigate the specific contributions of different types of remote sensing variables to the predictions and model accuracy at the regional scale, where the benefits of the MODIS and QSCAT satellite data are expected to be most significant. Location South America, with a focus on the tropical and subtropical Andes and the Amazon Basin. Methods Potential geographic distributions of eight species, namely two birds, two mammals and four trees, were modelled with the maxent algorithm at 1‐km resolution over the South American continent using climatic and remote sensing data separately and combined. For each species and model scenario, we assess model performance by testing the agreement between observed and simulated distributions across all thresholds and, in the case of the two focal bird species, at selected thresholds. Results Quantitative performance tests showed that models built with remote sensing and climatic layers in isolation performed well in predicting species distributions, suggesting that each of these data sets contains useful information. However, predictions created with a combination of remote sensing and climatic layers generally resulted in the best model performance across the three taxonomic groups. In Ecuador, the inclusion of remote sensing data was critical in resolving the known geographically isolated populations of the two focal bird species along the steep Amazonian–Andean elevational gradients. Within remote sensing subsets, microwave‐based data were more important than optical data in the predictions of the two bird species. Main conclusions Our results suggest that the newly available remote sensing data (MODIS and QSCAT) have considerable utility in modelling the contemporary geographical distributions of species at both regional and continental scales and in predicting range shifts as a result of large‐scale land‐use change.     

不同演替阶段典型树种幼苗对酸胁迫响应的高光谱监测

通过在为期2a的可控酸雨试验下对处于不同演替阶段的典型树种幼苗的高光谱测定,得到不同梯度酸雨下各树种的叶片光谱反应曲线及相应叶片的生理生化参量.对测定的3种树种的叶片叶绿素含量及一阶导数光谱进行分析,发现随着酸雨浓度的增加,处于演替前期的先锋树种马尾松的幼苗叶绿素含量呈现增加趋势,而处于演替中、后期的木荷和青冈幼苗叶绿素含量则呈现减少趋势;随着试验时间的推移,马尾松的红边位置呈现"红移"趋势,其中pH2.5处理下的"红移"趋势较明显;而木荷和青冈的红边位置则呈现不同程度的"蓝移"趋势.较长时期高浓度酸胁迫对先锋树种马尾松的幼苗生长有一定促进作用,而对演替中期和后期树种木荷和青冈幼苗生长则主要表现为抑制作用.     

植被含水量高光谱遥感监测研究进展

植被含水量是陆地植被重要的生物物理特征, 其定量遥感反演有助于植被干旱胁迫的实时监测与诊断评估。该文系统综述了国内外利用高光谱遥感评估植被水分状况的4个常见植被水分指标——冠层含水量、叶片等量水厚度、活体可燃物湿度和相对含水量的概念及其遥感估算方法研究进展, 评述了植被含水量高光谱遥感估算各类方法的优缺点, 探讨了植被含水量高光谱遥感估算目前存在的问题, 并提出进一步的研究任务, 即服务于植被干旱胁迫的高光谱遥感监测、预警与评估。     

Research progress on monitoring vegetation water content by using hyperspectral remote sensing

植被含水量是陆地植被重要的生物物理特征, 其定量遥感反演有助于植被干旱胁迫的实时监测与诊断评估。该文系统综述了国内外利用高光谱遥感评估植被水分状况的4个常见植被水分指标——冠层含水量、叶片等量水厚度、活体可燃物湿度和相对含水量的概念及其遥感估算方法研究进展, 评述了植被含水量高光谱遥感估算各类方法的优缺点, 探讨了植被含水量高光谱遥感估算目前存在的问题, 并提出进一步的研究任务, 即服务于植被干旱胁迫的高光谱遥感监测、预警与评估。     

乌梁素海湿地芦苇最大羧化速率的高光谱遥感

湿地植被生产力和固碳潜力的研究是全球碳循环和全球变化的热点研究问题。湿地植被的光合能力能够指示其生长的健康状态。最大羧化速率是重要的植被光合参数之一,对精确模拟湿地植被光合作用和气体交换模型中的固碳过程具有重要的作用。以内蒙古乌梁素海湖泊湿地为研究区,进行了芦苇叶片光合参数和光谱的测量。芦苇叶片最大羧化速率(V_(cmax))数值是基于Farquhar光合作用模型,从光合测量获取的A-C_i曲线计算并校正到25℃得到的。分别基于bootstrap PLSR模型、单波段和高光谱植被指数(包括简单比值指数SR和归一化差值指数ND),构建湿地芦苇叶片最大羧化速率(V_(cmax))估算模型。基于高光谱遥感图像HJ-1A HSI,采用ND高光谱指数中具有较高V_(cmax)估算精度的入选波段702和756 nm,获取研究区湿地芦苇最大羧化速率空间分布图。研究结果表明,湿地植被光谱特征和高光谱植被指数,可用于估算湿地芦苇V_(cmax),其中最高精度产生于基于bootstrap PLSR模型的建模方法(R~2=0.87,RMSECV=3.90,RPD=2.72),ND高光谱指数的V_(cmax)估算精度高于SR高光谱指数的估算精度;从获取的V_(cmax)空间分布图上提取估算值,其与测量值对比,存在较好的相关性(R~2=0.80,RMSE=4.74)。     

Mapping large-scale distribution of submerged aquatic vegetation coverage using remote sensing

This study investigated the potential of using remote sensing for mapping and monitoring of submerged aquatic vegetation (SAV) on a large scale. The spectral characteristics of SAV with varied coverage were measured using a portable spectroradiometer on a man-made lake at the Chongming International Wetland Park, Shanghai, China. A good relationship between the coverage of SAV and their field spectral characteristics was established and the reflectance of SAV increased with its increasing coverage. A regression analysis was then carried out between the coverage and the reflectance at the wavelengths of four QuickBird bands. After making an atmospheric correction from a synchronous QuickBird image for the study site, the image digital number (DN) was converted into the ground reflectance. The reflectance image was then deduced into a distribution map of SAV coverage by using the results of the regression functions between the coverage of SAV and the reflectance rate measured in situ. An accuracy assessment indicated that this approach could be used to quickly monitor the distribution and growth situation of SAV. The implications of this observation, in terms of the ability of remote sensing to estimate and monitor the distribution and dynamics of SAV on a large scale are discussed.     

Benefits of hyperspectral remote sensing for tracking plant invasions

Aim We aim to report what hyperspectral remote sensing can offer for invasion ecologists and review recent progress made in plant invasion research using hyperspectral remote sensing. Location United States. Methods We review the utility of hyperspectral remote sensing for detecting, mapping and predicting the spatial spread of invasive species. We cover a range of topics including the trade‐off between spatial and spectral resolutions and classification accuracy, the benefits of using time series to incorporate phenology in mapping species distribution, the potential of biochemical and physiological properties in hyperspectral spectral reflectance for tracking ecosystem changes caused by invasions, and the capacity of hyperspectral data as a valuable input for quantitative models developed for assessing the future spread of invasive species. Results Hyperspectral remote sensing holds great promise for invasion research. Spectral information provided by hyperspectral sensors can detect invaders at the species level across a range of community and ecosystem types. Furthermore, hyperspectral data can be used to assess habitat suitability and model the future spread of invasive species, thus providing timely information for invasion risk analysis. Main conclusions Our review suggests that hyperspectral remote sensing can effectively provide a baseline of invasive species distributions for future monitoring and control efforts. Furthermore, information on the spatial distribution of invasive species can help land managers to make long‐term constructive conservation plans for protecting and maintaining natural ecosystems.     

Automated GIS-based derivation of urban ecological indicators using hyperspectral remote sensing and height information

Urban ecological indicators allow the objective and quantitative characterisation of ecological conditions in a spatially continuous way by evaluating the influence of urban surface types with respect to ecological functions and ecosystem services. Although the concept had already been developed in the 1980s, the variety of existing indicators had not been widely applied yet in urban planning practice, because of the high manual mapping effort that is required for spatially differentiated urban surface mapping. This paper presents a new automated remote sensing and GIS-based system for the flexible and user-defined derivation of urban ecological indicators. The system is based on automated surface material mapping using airborne hyperspectral image data and height information. Because the material classes obtained from remote sensing analysis differ in part from the surface types needed for the calculation of urban ecological indicators, they have been transformed into so-called linking categories representing the basis for the automated GIS-based derivation of urban ecological indicators. For this purpose, a computer-based system for flexible indicator derivation has been developed, allowing the user-defined integration of indicators based on the variable determination of mapping units, linking categories and respective weighting factors. Based on a comprehensive review of existing ecological indicators, 14 indicators have been selected and implemented in the system. To demonstrate the potential of the new system, a variety of indicators has been derived for two test sites situated in the German cities of Dresden and Potsdam, using city blocks defined by the municipal authorities as spatial mapping units. The initial mapping of surface materials was automatically performed on the basis of airborne hyperspectral image data acquired by the HyMAP system. The results of subsequent GIS-based indicator calculation were validated using results from field-based reference mapping that had been carried out for selected city blocks situated in both cities. An accuracy assessment for these reference city blocks has revealed mean errors of approximately 4%, confirming the suitability of the developed automated GIS-based system for flexible and efficient indicator calculation.     

Monitoring an invasive perennial at the landscape scale with remote sensing

Summary   Worldwide, invasive weeds threaten agricultural, natural and urban ecosystems. In Australia's agricultural and grazing regions, invasive species often establish across extensive areas where weed management is hampered by an inability to detect the location and timing of an outbreak. In these vast landscapes, an effective detection and monitoring system is required to delineate the extent of the invasion and identify spatial and temporal factors associated with weed establishment and thickening. In this study, we utilize a time series of remote sensing imagery to detect the spatial and temporal patterns of Prickly Acacia ( Acacia nilotica ) invasion in the Mitchell grass plains of North Queensland. We develop a spectral index from Landsat images which is applied to images from 1989 to 2004, in combination with a classification mask, to identify locations and monitor changes in Prickly Acacia density across 29 000 km² of Mitchell grass plains. The approach identified spectral and temporal signatures consistent with Prickly Acacia infestation on 1.9% of this landscape. Field checking of results confirmed presence of the weed in previously unrecorded locations. The approach may be used to evaluate future spread, or outcomes of management strategies for Prickly Acacia in this landscape and could be employed to detect and monitor invasions in other extensive landscapes.