Special Issue "Remote Sensing of Biological Diversity"

Abstract: Over the past 60 years, Earth observing has evolved from aerial photographic studies to high-tech airborne 3-D imaging and global satellite-based monitoring. These technological advances have been driven by an increasing call for quantitative and information-rich data on changes in Earth properties and processes. Within the biospheric remote sensing arena, focus has mostly been placed on changes in land cover and land use, ecological disturbance including fire, and basic biophysical properties such as vegetation light absorption and greenness. In recent years, however, interest has rapidly increased in the area of biological diversity monitoring. This special issue of Remote Sensing [1] captures some of the latest thinking on how both traditional and newer mapping technologies can contribute to biodiversity monitoring and analysis.

Abstract: Outlining patches dominated by different plants in wetland vegetation provides information on species succession, microhabitat patterns, wetland health and ecosystem services. Aerial photogrammetry and hyperspectral imaging are the usual data acquisition methods but the application of airborne laser scanning (ALS) as a standalone tool also holds promises for this field since it can be used to quantify 3-dimensional vegetation structure. Lake Balaton is a large shallow lake in western Hungary with shore wetlands that have been in decline since the 1970s. In August 2010, an ALS survey of the shores of Lake Balaton was completed with 1 pt/m² discrete echo recording. The resulting ALS dataset was processed to several output rasters describing vegetation and terrain properties, creating a sufficient number of independent variables for each raster cell to allow for basic multivariate classification. An expert-generated decision tree algorithm was applied to outline wetland areas, and within these, patches dominated by Typha sp. Carex sp., and Phragmites australis. Reed health was mapped into four categories: healthy, stressed, ruderal and die-back. The output map was tested against a set of 775 geo-tagged ground photographs and had a user’s accuracy of > 97% for detecting non-wetland features (trees, artificial surfaces and low density Scirpus stands), > 72% for dominant genus detection and > 80% for most reed health categories (with 62% for one category). Overall classification accuracy was 82.5%, Cohen’s Kappa 0.80, which is similar to some hyperspectral or multispectral-ALS fusion studies. Compared to hyperspectral imaging, the processing chain of ALS can be automated in a similar way but relies directly on differences in vegetation structure and actively sensed reflectance and is thus probably more robust. The data acquisition parameters are similar to the national surveys of several European countries, suggesting that these existing datasets could be used for vegetation mapping and monitoring.

Abstract: This study explores a method to classify seven tropical rainforest tree species from full-range (400–2,500 nm) hyperspectral data acquired at tissue (leaf and bark), pixel and crown scales using laboratory and airborne sensors. Metrics that respond to vegetation chemistry and structure were derived using narrowband indices, derivative- and absorption-based techniques, and spectral mixture analysis. We then used the Random Forests tree-based classifier to discriminate species with minimally-correlated, importance-ranked metrics. At all scales, best overall accuracies were achieved with metrics derived from all four techniques and that targeted chemical and structural properties across the visible to shortwave infrared spectrum (400–2500 nm). For tissue spectra, overall accuracies were 86.8% for leaves, 74.2% for bark, and 84.9% for leaves plus bark. Variation in tissue metrics was best explained by an axis of red absorption related to photosynthetic leaves and an axis distinguishing bark water and other chemical absorption features. Overall accuracies for individual tree crowns were 71.5% for pixel spectra, 70.6% crown-mean spectra, and 87.4% for a pixel-majority technique. At pixel and crown scales, tree structure and phenology at the time of image acquisition were important factors that determined species spectral separability.

Abstract: Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species.

Abstract: Vegetation maps are the starting point for the design of protected areas and regional conservation plans. Accurate vegetation maps are missing for much of Amazonia, preventing the development of effective and compelling conservation strategies. Here we used a network of 160 inventories across northwestern Amazonia to evaluate the use of Landsat and Shuttle Radar Topography Mission (SRTM) data to identify floristic and edaphic patterns in Amazonian forests. We first calculated the strength of the relationship between these remotely-sensed data, and edaphic and floristic patterns in these forests, and asked how sensitive these results are to image processing and enhancement. We additionally asked if SRTM data can be used to model patterns in plant species composition in our study areas. We find that variations in Landsat and SRTM data are strongly correlated with variations in soils and plant species composition, and that these patterns can be mapped solely on the basis of SRTM data over limited areas. Using these data, we furthermore identified widespread patch-matrix floristic patterns across northwestern Amazonia, with implications for conservation planning and study. Our findings provide further evidence that Landsat and SRTM data can provide a cost-effective means for mapping these forests, and we recommend that maps generated from a combination of remotely-sensed and field data be used as the basis for conservation prioritization and planning in these vast and remote forests.

Abstract: Our objective is to identify and map individuals of nine tree species in a Hawaiian lowland tropical forest by comparing the performance of a variety of semi-supervised classifiers. A method was adapted to process hyperspectral imagery, LiDAR intensity variables, and LiDAR-derived canopy height and use them to assess the identification accuracy. We found that semi-supervised Support Vector Machine classification using tensor summation kernel was superior to supervised classification, with demonstrable accuracy for at least eight out of nine species, and for all combinations of data types tested. We also found that the combination of hyperspectral imagery and LiDAR data usually improved species classification. Both LiDAR intensity and LiDAR canopy height proved useful for classification of certain species, but the improvements varied depending upon the species in question. Our results pave the way for target-species identification in tropical forests and other ecosystems.

Abstract: The unique ecosystems of the Hawaiian Islands are progressively being threatened following the introduction of exotic species. Operational implementation of remote sensing for the detection, mapping and monitoring of these biological invasions is currently hampered by a lack of knowledge on the spectral separability between native and invasive species. We used spaceborne imaging spectroscopy to analyze the seasonal dynamics of the canopy hyperspectral reflectance properties of four tree species: (i) Metrosideros polymorpha, a keystone native Hawaiian species; (ii) Acacia koa, a native Hawaiian nitrogen fixer; (iii) the highly invasive Psidium cattleianum; and (iv) Morella faya, a highly invasive nitrogen fixer. The species specific separability of the reflectance and derivative-reflectance signatures extracted from an Earth Observing-1 Hyperion time series, composed of 22 cloud-free images spanning a period of four years and was quantitatively evaluated using the Separability Index (SI). The analysis revealed that the Hawaiian native trees were universally unique from the invasive trees in their near-infrared-1 (700–1,250 nm) reflectance (0.4 > SI > 1.4). Due to its higher leaf area index, invasive trees generally had a higher near-infrared reflectance. To a lesser extent, it could also be demonstrated that nitrogen-fixing trees were spectrally unique from non-fixing trees. The higher leaf nitrogen content of nitrogen-fixing trees was expressed through slightly increased separabilities in visible and shortwave-infrared reflectance wavebands (SI = 0.4). We also found phenology to be key to spectral separability analysis. As such, it was shown that the spectral separability in the near-infrared-1 reflectance between the native and invasive species groups was more expressed in summer (SI > 0.7) than in winter (SI < 0.7). The lowest separability was observed for March-July (SI < 0.3). This could be explained by the invasives taking advantage of the warmer summer period to expand their canopy. There was, however, no specific time window or a single spectral region that always defined the separability of all species groups, and thus intensive monitoring of plant phenology as well as the use of the full-range (400–2,500 nm) spectrum was highly advantageous in differentiating each species. These results set a basis for an operational invasive species monitoring program in Hawai’i using spaceborne imaging spectroscopy.

Abstract: Landsat can be used to map tropical forest cover at 15–60 m resolution, which is helpful for detecting small but important perturbations in increasingly fragmented forests. However, among the remaining Landsat satellites, Landsat-5 no longer has global coverage and, since 2003, a mechanical fault in the Scan-Line Corrector (SLC-Off) of the Landsat-7 satellite resulted in a 22–25% data loss in each image. Such issues challenge the use of Landsat for wall-to-wall mapping of tropical forests, and encourage the use of alternative, spatially coarser imagery such as MODIS. Here, we describe and test an alternative method of post-classification compositing of Landsat images for mapping over 20.5 million hectares of peat swamp forest in the biodiversity hotspot of Sundaland. In order to reduce missing data to levels comparable to those prior to the SLC-Off error, we found that, for a combination of Landsat-5 images and SLC-off Landsat-7 images used to create a 2005 composite, 86% of the 58 scenes required one or two images, while 14% required three or more images. For a 2010 composite made using only SLC-Off Landsat-7 images, 64% of the scenes required one or two images and 36% required four or more images. Missing-data levels due to cloud cover and shadows in the pre SLC-Off composites (7.8% and 10.3% for 1990 and 2000 enhanced GeoCover mosaics) are comparable to the post SLC-Off composites (8.2% and 8.3% in the 2005 and 2010 composites). The area-weighted producer’s accuracy for our 2000, 2005 and 2010 composites were 77%, 85% and 86% respectively. Overall, these results show that missing-data levels, classification accuracy, and geographic coverage of Landsat composites are comparable across a 20-year period despite the SLC-Off error since 2003. Correspondingly, Landsat still provides an appreciable utility for monitoring tropical forests, particularly in Sundaland’s rapidly disappearing peat swamp forests.

Abstract: Tree species diversity is a key parameter to describe forest ecosystems. It is, for example, important for issues such as wildlife habitat modeling and close-to-nature forest management. We examined the suitability of 8-band WorldView-2 satellite data for the identification of 10 tree species in a temperate forest in Austria. We performed a Random Forest (RF) classification (object-based and pixel-based) using spectra of manually delineated sunlit regions of tree crowns. The overall accuracy for classifying 10 tree species was around 82% (8 bands, object-based). The class-specific producer’s accuracies ranged between 33% (European hornbeam) and 94% (European beech) and the user’s accuracies between 57% (European hornbeam) and 92% (Lawson’s cypress). The object-based approach outperformed the pixel-based approach. We could show that the 4 new WorldView-2 bands (Coastal, Yellow, Red Edge, and Near Infrared 2) have only limited impact on classification accuracy if only the 4 main tree species (Norway spruce, Scots pine, European beech, and English oak) are to be separated. However, classification accuracy increased significantly using the full spectral resolution if further tree species were included. Beside the impact on overall classification accuracy, the importance of the spectral bands was evaluated with two measures provided by RF. An in-depth analysis of the RF output was carried out to evaluate the impact of reference data quality and the resulting reliability of final class assignments. Finally, an extensive literature review on tree species classification comprising about 20 studies is presented.

Abstract: The decline of biodiversity is one of the major current global issues. Still, there is a widespread lack of information about the spatial distribution of individual species and biodiversity as a whole. Remote sensing techniques are increasingly used for biodiversity monitoring and especially the combination of LiDAR and hyperspectral data is expected to deliver valuable information. In this study spatial patterns of vascular plant community composition and alpha-diversity of a temperate montane forest in Germany were analysed for different forest strata. The predictive power of LiDAR (LiD) and hyperspectral (MNF) datasets alone and combined (MNF+LiD) was compared using random forest regression in a ten-fold cross-validation scheme that included feature selection and model tuning. The final models were used for spatial predictions. Species richness could be predicted with varying accuracy (R² = 0.26 to 0.55) depending on the forest layer. In contrast, community composition of the different layers, obtained by multivariate ordination, could in part be modelled with high accuracies for the first ordination axis (R² = 0.39 to 0.78), but poor accuracies for the second axis (R² ≤ 0.3). LiDAR variables were the best predictors for total species richness across all forest layers (R² _LiD = 0.3, R² _MNF = 0.08, R² _MNF+LiD = 0.2), while for community composition across all forest layers both hyperspectral and LiDAR predictors achieved similar performances (R² _LiD = 0.75, R² _MNF = 0.76, R² _MNF+LiD = 0.78). The improvement in R² was small (≤0.07)—if any—when using both LiDAR and hyperspectral data as compared to using only the best single predictor set. This study shows the high potential of LiDAR and hyperspectral data for plant biodiversity modelling, but also calls for a critical evaluation of the added value of combining both with respect to acquisition costs.

Abstract: Mapping the spatial distribution of plant species in savannas provides insight into the roles of competition, fire, herbivory, soils and climate in maintaining the biodiversity of these ecosystems. This study focuses on the challenges facing large-scale species mapping using a fusion of Light Detection and Ranging (LiDAR) and hyperspectral imagery. Here we build upon previous work on airborne species detection by using a two-stage support vector machine (SVM) classifier to first predict species from hyperspectral data at the pixel scale. Tree crowns are segmented from the lidar imagery such that crown-level information, such as maximum tree height, can then be combined with the pixel-level species probabilities to predict the species of each tree. An overall prediction accuracy of 76% was achieved for 15 species. We also show that bidirectional reflectance distribution (BRDF) effects caused by anisotropic scattering properties of savanna vegetation can result in flight line artifacts evident in species probability maps, yet these can be largely mitigated by applying a semi-empirical BRDF model to the hyperspectral data. We find that confronting these three challenges—reflectance anisotropy, integration of pixel- and crown-level data, and crown delineation over large areas—enables species mapping at ecosystem scales for monitoring biodiversity and ecosystem function.

Abstract: Most plant species are non-randomly distributed across environmental gradients in light, water, and nutrients. In tropical forests, these gradients result from biophysical processes related to the structure of the canopy and terrain, but how does species richness in tropical forests vary over such gradients, and can remote sensing capture this variation? Using airborne lidar, we tested the extent to which variation in tree species richness is statistically explained by lidar-measured structural variation in canopy height and terrain in the extensively studied, stem-mapped 50-ha plot on Barro Colorado Island (BCI), Panama. We detected differences in species richness associated with variation in canopy height and topography across spatial scales ranging from 0.01-ha to 1.0-ha. However, species richness was most strongly associated with structural variation at the 1.0-ha scale. We developed a predictive generalized least squares model of species richness at the 1.0-ha scale (R² = 0.479, RMSE = 8.3 species) using the mean and standard deviation of canopy height, mean elevation, and terrain curvature. The model demonstrates that lidar-derived measures of forest and terrain structure can capture a significant fraction of observed variation in tree species richness in tropical forests on local-scales.

Abstract: The regional controls of biodiversity patterns have been traditionally evaluated using structural and compositional components at the species level, but evaluation of the functional component at the ecosystem level is still scarce. During the last decades, the role of ecosystem functioning in management and conservation has increased. Our aim was to use satellite-derived Ecosystem Functional Types (EFTs, patches of the land-surface with similar carbon gain dynamics) to characterize the regional patterns of ecosystem functional diversity and to evaluate the environmental and human controls that determine EFT richness across natural and human-modified systems in temperate South America. The EFT identification was based on three descriptors of carbon gain dynamics derived from seasonal curves of the MODIS Enhanced Vegetation Index (EVI): annual mean (surrogate of primary production), seasonal coefficient of variation (indicator of seasonality) and date of maximum EVI (descriptor of phenology). As observed for species richness in the southern hemisphere, water availability, not energy, emerged as the main climatic driver of EFT richness in natural areas of temperate South America. In anthropogenic areas, the role of both water and energy decreased and increasing human intervention increased richness at low levels of human influence, but decreased richness at high levels of human influence.

Abstract: Mangroves are an important bulkhead against climate change: they afford protection for coastal areas from tidal waves and cyclones, and are among the most carbon-rich forests in the tropics. As such, protection of mangroves is an urgent priority. This work provides some new information on patterns of degradation in the Sundarbans, the largest contiguous mangrove forest in the world, which are home to more than 35 reptile species, 120 commercial fish species, 300 bird species and 32 mammal species. Using radar imagery, we contrast and quantify the recent impacts of cyclone Sidr and anthropogenic degradation on this ecosystem. Our results, inferred from changes in radar backscatter, confirm already reported trends in coastline retreat for this region, with areas losing as much as 200 m of coast per year. They also suggest rapid changes in mangrove dynamics for Bangladesh and India, highlighting an overall decrease in mangrove health in the east side of the Sundarbans, and an overall increase in this parameter for the west side of the Sundarbans. As global environmental change takes its toll in this part of the world, more detailed, regular information on mangroves’ distribution and health is required: our study illustrates how different threats experienced by mangroves can be detected and mapped using radar-based information, to guide management action.