Predictability of reef fish diversity and abundance using remote sensing data in Diego Garcia (Chagos Archipelago)

The diversity, abundance and distribution of reef fish are related to heterogeneity and physical complexity of benthic habitat. However, the field effort required to evaluate these aspects of the benthos in situ, at the scale of entire reefscapes, is greatly constrained by logistical and resource limitations. With moderate ground truthing, both substratum type and seabed topography are amenable to monitoring using satellite data. Here, remote sensing imagery was used to resolve the bathymetry and benthic character of a reef system in Diego Garcia (British Indian Ocean Territory). Replicate fish counts were made at seven measurement stations across the study area using visual census. Monte Carlo simulation revealed that species richness and abundance of several guilds and size groupings of reef fish appraised in situ were correlated with the satellite-derived seabed parameters over areas of seafloor as large as 5,030 m². The study suggests that satellite remote sensing is capable of predicting habitat complexity at a scale relevant to fish. Furthermore, as larger size classes of fish were better predicted with the satellite habitat complexity data, this technique could be used to predict fish stocks and identify potential sites for marine protected areas where intensive field surveys are not practical.     

High resolution mapping of sediment organic matter from acoustic reflectance data

Spatial mapping of the marine environment is challenging when the properties concerned are difficult to measure except by shore-based analysis of discrete samples of material, usually from sparsely distributed sites. This is the case for many seabed sediment properties. We developed an indirect approach to mapping the organic content of coastal sediments from hydro-acoustic reflectance data. The basis was that both organic matter and acoustic reflectance are related to sediment type and grain size composition. Hence there is a collateral relationship between organic matter content and reflectance properties which can be exploited to enable high resolution mapping. We surveyed an area of seabed off the east coast of Scotland using a vessel mounted single beam echosounder with RoxAnn signal processing. Organic carbon, nitrogen and phytoplankton pigment contents were then measured in material from grab and core samples collected at intervals over a year. Relationships between the organic components and hydro–acoustic characteristics were derived by general additive models, and used to construct high resolution maps from the acoustic survey data. Our method is an advance on traditional interpolation techniques sparse spatial data, and represents a generic approach that could be applied to other properties.     

Associations between wasp communities and forest structure: Do strong local patterns hold across landscapes?

Abstract Forest structure and habitat complexity have been used extensively to predict the distribution and abundance of insect assemblages in forest ecosystems. We tested empirically derived predictions of strong, consistent relationships between wasp assemblages and habitat complexity, using both field assessments and vegetation indices from remote sensing as measures of habitat complexity. Wasp samples from 26 paired ‘high and low’ complexity sites in two forests approximately 70 km apart, were compared with normalized difference vegetation indices (NDVIs) derived from multispectral videography of the survey sites. We describe a strong unequivocal link between habitat complexity and wasp communities, the patterns holding over coarse and fine landscape scales. NDVIs were also excellent predictors of habitat complexity and hence wasp community patterns. Sites with greater NDVIs consistently supported a greater abundance and species richness, and a different composition of wasps to sites with low NDVIs. Using vegetation indices from remote sensing to gauge habitat complexity has significant potential for ecosystem modelling and rapid biodiversity assessment.     

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.     

Agricultural land-use in China: a comparison of area estimates from ground-based census and satellite-borne remote sensing

We compare estimates of total cropland area, paddy rice area, and irrigated cropland area in China from land cover maps derived from optical remote sensing in 1992–93 (1-km resolution NOAA AVHRR) and county-level agricultural census data for 1990. At national, regional, provincial, and county scales, the total cropland area estimated by remote sensing is 50–100% higher than reported in the agricultural census. For topographically flat North and Central China, there is a high correlation between county-level cropland area estimates by the two approaches. For other regions, the correlation between remote sensing and agricultural census cropland area is much weaker. Throughout China, there is only moderate to weak correlation between remote sensing-based and census-bases estimates of paddy rice area and total irrigated cropland area. It is likely that the census data underestimates and the remote sensing overestimates the actual cropland area. These uncertainties in agricultural land cover estimates will contribute to uncertainty in any large-scale biogeochemical analyses.     

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Phenological events, such as bud burst, are strongly linked to ecosystem processes in temperate deciduous forests. However, the exact nature and magnitude of how seasonal and interannual variation in air temperatures influence phenology is poorly understood, and model‐based phenology representations fail to capture local‐ to regional‐scale variability arising from differences in species composition. In this paper, we use a combination of surface meteorological data, species composition maps, remote sensing, and ground‐based observations to estimate models that better represent how community‐level species composition affects the phenological response of deciduous broadleaf forests to climate forcing at spatial scales that are typically used in ecosystem models. Using time series of canopy greenness from repeat digital photography, citizen science data from the USA National Phenology Network, and satellite remote sensing‐based observations of phenology, we estimated and tested models that predict the timing of spring leaf emergence across five different deciduous broadleaf forest types in the eastern United States. Specifically, we evaluated two different approaches: (i) using species‐specific models in combination with species composition information to ‘upscale’ model predictions and (ii) using repeat digital photography of forest canopies that observe and integrate the phenological behavior of multiple representative species at each camera site to calibrate a single model for all deciduous broadleaf forests. Our results demonstrate variability in cumulative forcing requirements and photoperiod cues across species and forest types, and show how community composition influences phenological dynamics over large areas. At the same time, the response of different species to spatial and interannual variation in weather is, under the current climate regime, sufficiently similar that the generic deciduous forest model based on repeat digital photography performed comparably to the upscaled species‐specific models. More generally, results from this analysis demonstrate how in situ observation networks and remote sensing data can be used to synergistically calibrate and assess regional parameterizations of phenology in models.     

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.     

Modelling tree diversity in a highly fragmented tropical montane landscape

Aim There is an urgent need for conservation in threatened tropical forest regions. We explain and predict the spatial variation of α (i.e. within plot) and β (i.e. between plot) tree diversity in a tropical montane landscape subjected to a high deforestation rate. A major aim is to demonstrate the potential of a method that combines data from multiple sources (field data, remote sensing imagery and GIS) to evaluate and monitor forest diversity on a broad scale over large unexplored areas. Location The study covered an area of c. 3500 km² in the Highlands of Chiapas, southern Mexico. Methods We identified all of the tree species within 204 field plots (1000 m² each) and measured different environmental, human disturbance‐related, and spatial variables using remote sensing and GIS data. To obtain a predictive model of α tree diversity (Fisher's alpha) based on selected explanatory variables, we used a generalized linear model with a gamma error distribution. Mantel tests of matrix correspondence were used to determine whether similarities in floristic composition were correlated with similarities in the explanatory variables. Finally, we used a method that combines α and β tree diversity to define priority areas for conservation. Results The model for α tree diversity explained 44% of the overall variability, of which most was mainly related to precipitation, temperature, NDVI, and canopy (all relationships were positive, and quadratic for temperature and NDVI). There were no spatially structured regional factors that were ignored. Similarity in tree composition was correlated positively with climate and NDVI. Main conclusions The results were used to: (1) identify and assign conservation priority of unexplored areas that have high tree diversity, and (2) demonstrate the importance of several vegetation formations in the region's biodiversity. The method we present can be particularly useful in assessing regional needs and in developing local conservation strategies in poorly surveyed (and often at risk) tropical areas worldwide, where accessibility is usually limited.     

Modelling relationships between birds and vegetation structure using airborne LiDAR data: a review with case studies from agricultural and woodland environments

Airborne LiDAR (Light Detection and Ranging) is a remote sensing technology that offers the ability to collect high horizontal sampling densities of high vertical resolution vegetation height data, over larger spatial extents than could be obtained by field survey. The influence of vegetation structure on the bird is a key mechanism underlying bird–habitat models. However, manual survey of vegetation structure becomes prohibitive in terms of time and cost if sampling needs to be of sufficient density to incorporate fine-grained heterogeneity at a landscape extent. We show that LiDAR data can help bridge the gap between grain and extent in organism–habitat models. Two examples are provided of bird–habitat models that use structural habitat information derived from airborne LiDAR data. First, it is shown that data on crop and field boundary height can be derived from LiDAR data, and so have the potential to predict the distribution of breeding Sky Larks in a farmed landscape. Secondly, LiDAR-retrieved canopy height and structural data are used to predict the breeding success of Great Tits and Blue Tits in broad-leaved woodland. LiDAR thus offers great potential for parameterizing predictive bird–habitat association models. This could be enhanced by the combination of LiDAR data with multispectral remote sensing data, which enables a wider range of habitat information to be derived, including both structural and compositional characteristics.     

普洱季风常绿阔叶林乔木物种多样性高分辨率遥感估测

季风常绿阔叶林是我国南亚热带典型的地带性植被,也是云南省普洱地区重要森林类型。季风常绿阔叶林乔木物种多样性遥感估测对研究区域尺度生物多样性格局及其规律具有重要作用。根据光谱异质性假说和环境异质性假说,首先使用1m空间分辨率的机载高光谱数据和激光雷达数据提取了光谱多样性特征和垂直结构特征。然后利用基于随机森林算法的递归特征消除方法选择对研究区森林乔木物种多样性指数具有较好解释能力的遥感特征,并对Shannon-Winner物种多样性指数进行建模、制图。研究结果表明：(1)基于机载LiDAR数据提取的垂直结构特征和机载高光谱数据提取的光谱多样性特征均对研究区森林乔木物种多样性具有较好的解释能力,随机森林模型估测结果分别为R²=0.48,RMSE=0.46和R²=0.5,RMSE=0.45;两种数据源融合可以进一步提高遥感数据的森林乔木物种多样性估测精度,随机森林估测模型R²和RMSE分别为0.69和0.37。(2)机载激光雷达数据对研究区针阔混交林乔木物种多样性的估测能力优于机载高光谱数据。(3)机器学习方法有助于从高维遥感...     

Relative importance of hydrological variables in predicting the habitat suitability of Euryale ferox Salisb.

Aims Aquatic ecosystems are a priority for conservation as they have become rapidly degraded with land-use changes. Predicting the habitat range of an endangered species provides crucial information for biodiversity conservation in such rapidly changing environments. However, the complex network structure of aquatic ecosystems restricts spatial prediction variables and has hitherto limited the use of habitat models to predict species occurrence in aquatic ecosystems. We used the maximum entropy model to evaluate the potential distribution of an endangered aquatic species, Euryale ferox Salisb. We tested the relative influence of (i) climatic variables, (ii) topographic variables, and (iii) hydrological variables derived from remote sensing data to improve the prediction of occurrence of aquatic plant species.Methods We considered the southern part of the Korean Peninsula as the modeling extent for the potential distribution of E. ferox. Occurrence records for E. ferox were collected from the literature and field surveys. We applied maximum entropy modeling using remotely sensed environmental variables and evaluated their relative importance as prediction variables with variation partitioning.Important findings The species distribution model predicted potential habitats of E. ferox that matched the actual distribution well. Floodplain wetlands and shallow reservoirs were the favored habitats of E. ferox. Quantitative loss and fragmentation of wetland habitats appeared to be a major reason for the decrease of E. ferox populations. Our results also imply that hydrological variables (i.e. normalized difference water index) derived from remote sensing data greatly increased model prediction (relative contribution: 10.5–37.0%) in the aquatic ecosystem. However, interspecific competition within a similar niche environment should be considered to increase the accuracy of the distribution model.     

Comparing and Combining Landsat Satellite Imagery and Participatory Data to Assess Land-Use and Land-Cover Changes in a Coastal Village in Papua New Guinea

In regions lacking socio-economic data, pairing satellite imagery with participatory information is essential for accurate land-use/cover (LULC) change assessments. At the village scale in Papua New Guinea we compare swidden LULC classifications using remote sensing analyses alone and analyses that combine participatory information and remotely sensed data. These participatory remote sensing (PRS) methods include participatory land-use mapping, household surveys, and validation of image analysis in combination with remotely sensed data. The classifications of the swidden area made using only remote sensing analysis show swidden areas are, on average, two and a half times larger than land managers reported for 1999 and 2011. Classifications made using only remote sensing analysis are homogeneous and lack discrimination among swidden plots, fallow land, and non-swidden vegetation. The information derived from PRS methods allows us to amend the remote sensing analysis and as a result swidden areas are more similar to actual swidden area found when ground-truthing. We conclude that PRS methods are needed to understand swidden system LULC complexities.     

Ecosystem functional units characterized by satellite observed phenology and productivity gradients: A case study for Europe

The present study demonstrates remote sensing derived phenological and productivity indicators of ecosystem functional dynamism. The indices were derived from SPOT VEGETATION NDVI data on 1 km spatial resolution across the pan-European continent using the Phenolo approach. The phenological and productivity indices explained 78% of the variance in the European ecosystem gradient measured by bio-climatic zones. Along this gradient climatic predictors could only explain 57% of the variance in the satellite metrics. Reclassification of the bio-climatic zones into phenology and productivity related ecosystem functional units (EFUs) selected five metrics related to the cyclic and permanent fraction of productivity, to the background, to the growing season start and the timing of the maximum NDVI value. Along the EFU gradient the climatic predictors explained over 90% of the variance of the remote sensing variables, 30% more than along the bio-climatic gradient. The EFUs showed strong correspondence to 14 land-cover types in Europe and the selected remote sensing metrics explained 86% of the variation in the land-cover classes. These results show that remote sensing derived parameters have tremendous potential for the quantification of ecosystem functional dynamism. Phenological and productivity metrics offer an indicator system for ecosystems that climatic indicators alone cannot manifest. Their potential to monitor the spatial pattern, status and inter-annual variability of ecosystems and vegetation cover can deliver reference status information for future assessments of the impacts of human or climate change induced ecosystem changes.     

白洋淀湿地生态系统水分条件遥感监测方法

湿地水文条件对湿地生态系统结构和功能起到关键作用。利用遥感获取与湿地水分条件直接相关的生物物理变量,包括归一化植被指数（NDVI）和地表温度,探讨监测湿地挺水植物缺水状况的可能性,并探讨了建立湿地水分遥感监测的新方法。回归分析表明,对于同一挺水植物而言,在湿地旱化的条件下,由于植物的蒸腾作用的差异,在植被生长状况（NDVI）相同的情况下,地势较高处植物的冠层温度亦较高;在生长处高度相同的情况下,植被覆盖度高（NDVI值高）的地方,植物的冠层温度较低。这说明可以通过地表温度和NDVI来监测挺水植物的缺水程度。     

基于GIS的浙江省水稻遥感估产最佳时相选择

水稻遥感估产最佳时相选择应包括水稻种植面积估算最佳时相和水稻产量预报最佳时相两部分。在水稻遥感估产最佳时相选择中,由于首次引入GIS技术提取水稻可能种植区域,缩小了研究范围,植被种类也较简单一,因此仅用农作物物候历即可确定水稻种植面积估算最佳时相,而不需要考虑所有的植被类型。利用盆栽试验和小区试验研究水稻产量与不同时期的农学参数、农学参数与植被指数及水稻产量与植被指数的关系,结果表明,水稻产量与农业参数、农学参数与光谱变量的关系均以孕穗以抽穗期最好,水稻产量与光谱变量的关系则从分蘖盛期到抽穗期的极显著。因此,以孕穗期到抽穗期作为建立水稻遥感估产模型的最佳时期。再利用1998年各地的水稻发育期观测资料,确定各区水稻产量遥感最佳时相。     

Organic carbon and biogenic silica in marine sediments in the vicinities of the Antarctic Peninsula: spatial patterns across a climatic gradient

To assess whether sea floor sediment reflects the characteristics of the upper water column, organic carbon (OC) and biogenic silica (bSi) were measured in seventeen 5-cm-long sediment cores recovered within a climatic gradient from the northwestern Weddell Sea (WS) to the Drake Passage (DP) across the Bransfield Strait (BS). Climate settings in the study area vary from dry and cold (polar) conditions with seasonal sea ice coverage in the WS to a more humid and warm (oceanic) environment where no seasonal sea ice develops in the DP, with the BS as transitional zone undergoing seasonal sea ice coverage. OC varied between 0.2 and 1.7 % and represented more than 90 % of the total carbon, and bSi varied between 2 and 13 %. The profiles of both variables along the sediment cores suggested that the surface mixed layer is at least 5 cm thick. The inventories of the upper 5 cm of the sediment column were calculated for both variables. Regional averages were significantly lower for OC in DP samples and higher for bSi in the BS. These results suggested relatively high bSi export to the seabed in the BS, higher degradation for OC in the DP and lower bSi export from the euphotic zone in the WS. The observations made evident that the biogenic matter contents in the sediment not necessarily replicate their production characteristics at the upper ocean even across strong climatic gradients. The results may provide a useful baseline for paleo-reconstructions in a rapidly changing environment.     

Spatially modelling native vegetation condition

Summary   The assessment of vegetation condition is seen as an increasingly important requirement for effective biodiversity conservation in Australia. Condition assessments that operate at the scale of the site are well established. However, there is a need for mapped representations of vegetation condition at regional scales to: (i) assist with regional planning and target setting; (ii) provide regional context for site-based assessment; and (iii) monitor the change in vegetation condition at multiple scales. This paper describes a methodology for converting site condition data collected in plots into maps of vegetation condition across entire regions using a predictive statistical modelling framework (Generalized Additive Modelling) combined with a GIS. The research demonstrates how explanatory variables including topographic position, terrain roughness, landscape connectivity and remote sensing derived indices can be used to map the condition of native vegetation at the scale of a subcatchment. The inclusion of indices derived from remotely sensed imagery (SPOT4) as explanatory variables in the modelling is a novel component of this research. Although the methodology generates statistically and ecologically plausible models of vegetation condition, there are nevertheless limitations associated with the way plot data were collected and some of the explanatory variables, which impacts upon model utility. We discuss how these problems can be minimized when embarking upon studies of this type. We demonstrate how maps produced from exercises such as this could be used for conservation planning and discuss the limitations of these data for monitoring.     

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.