Hierarchical object-based mapping of riverscape units and in-stream mesohabitats using LiDAR and VHR imagery

In this paper, we present a new, semi-automated methodology for mapping hydromorphological indicators of rivers at a regional scale using multisource remote sensing (RS) data. This novel approach is based on the integration of spectral and topographic information within a multilevel, geographic, object-based image analysis (GEOBIA). Different segmentation levels were generated based on the two sources of RS data, namely very-high spatial resolution, near-infrared imagery (VHR) and high-resolution LiDAR topography. At each level, different input object features were tested with Machine Learning classifiers for mapping riverscape units and in-stream mesohabitats. The GEOBIA approach proved to be a powerful tool for analyzing the river system at different levels of detail and for coupling spectral and topographic datasets, allowing for the delineation of the natural fluvial corridor with its primary riverscape units (e.g., water channel, unvegetated sediment bars, riparian densely-vegetated units, etc..) and in-stream mesohabitats with a high level of accuracy, respectively of K=0.91 and K=0.83. This method is flexible and can be adapted to different sources of data, with the potential to be implemented at regional scales in the future. The analyzed dataset, composed of VHR imagery and LiDAR data, is nowadays increasingly available at larger scales, notably through European Member States. At the same time, this methodology provides a tool for monitoring and characterizing the hydromorphological status of river systems continuously along the entire channel network and coherently through time, opening novel and significant perspectives to the river science and management, notably for planning and targeting actions.JRC.H.1-Water Resource

Characterizing flood impact on Swiss floodplains using inter-annual time series of satellite imagery

Pressure on the biodiversity of ecosystems along many rivers is growing continuously due to the increasing number of hydropower facilities regulating downstream flow and sediment regimes. Despite a thorough understanding of the shortterm processes and interactions at this hydro-biosphere interface, long-term analyses of the impacts on floodplain dynamics are lacking. We used inter-annual Landsat 4, 5, 7 and 8 time series to analyze the effects of hydrological events on floodplain vegetation in four mountainous floodplains in the Swiss Alps. Using a spectral mixture analysis approach, we demonstrate that the floodplain vegetation dynamics of mountainous rivers can be recovered at a spatial resolution of 30 meters. Our results suggest that interactions between floods and floodplain vegetation are complex and not exclusively related to flood magnitude. Of the four reaches analyzed, only data gathered along the submountainous reach with a quasi-natural flow regime show a clear link between remotely sensed vegetation indices and floods. In addition, our 29-year time series shows a continuous upward trend in vegetation indices along the floodplains, strongest in the reaches affected by hydropower facilities. The approach presented in this study can be easily replicated in other mountain ranges by providing available flow data to verify the impact of hydropower on floodplain vegetation dynamics

A Methodology for Automatic Identification of Units with Ecological Significance in Dehesa Ecosystems

The dehesa is an anthropic complex ecosystem typical of some areas of Spain and Portugal, with a key role in soil and biodiversity conservation and in the search for a balance between production, conservation and ecosystem services. For this reason, it is essential to have tools that allow its characterization, as well as to monitor and support decision-making to improve its sustainability. A multipurpose and scalable tool has been developed and validated, which combines several low-cost technologies, computer vision methods and RGB aerial orthophotographs using open data sources and which allows for automated agroforestry inventories, identifying and quantifying units with important ecological significance such as: trees, groups of trees, ecosystem corridors, regenerated areas and sheets of water. The development has been carried out from images of the national aerial photogrammetry plan of Spain belonging to 32 dehesa farms, representative of the existing variability in terms of density of trees, shrub species and the presence of other ecological elements. First, the process of obtaining and identifying areas of interest was automated using WMS services and shapefile metadata. Then, image analysis techniques were used to detect the different ecological units. Finally, a classification was developed according to the OBIA approach, which stores the results in standardized files for Geographic Information Systems. The results show that a stable solution has been achieved for the automatic and accurate identification of ecological units in dehesa territories. The scalability and generalization to all the dehesa territories, as well as the possibility of segmenting the area occupied by trees and other ecological units opens up a great opportunity to improve the construction of models for interpreting satellite images

Multiscale Anthropogenic Impacts on Stream Condition and Fish Assemblages in Amazonian Landscapes.

Land use change and forest degradation are resulting in pervasive changes to tropical ecosystems around the globe. While evidence from terrestrial systems demonstrates the severity of these disturbances for biodiversity conservation and provision of ecosystem services, the consequences for freshwater ecosystems remain poorly understood. This is especially true for the Amazon basin, the world's largest basin in both area and total discharge, and in particular for the complex network of low-order streams that make up the vast majority of its watercourses. These streams connect terrestrial and aquatic ecosystems throughout landscapes and host much, if not the majority, of the freshwater fish fauna of the Amazon basin, which itself is one of the most diverse in the world. Despite the biological significance of these stream networks, the consequences of land use change for the condition of instream habitat and fish fauna remain very poorly studied and understood. This thesis aims to address part of this knowledge gap by investigating the effects of anthropogenic disturbances occurring at multiple spatial scales on stream condition and fish assemblages from human-modified Amazonian forests in the state of Para, Brazil. The thesis starts by asking how instream habitat (composed of both water quality and physical habitat features) responds to landscape-scale anthropogenic disturbances and natural features (Chapter 2). Chapter 3 then investigates changes in fish species richness, abundance and composition following changes in both instream habitat and landscape-scale anthropogenic disturbance. Last, in Chapter 4 I attempt to disentangle the relative importance of those multiscale environmental predictor variables on species-specific disturbance responses, and evaluate the potential effectiveness of the Brazilian legislation in accounting for them. The thesis uses field data on fish assemblages, instream habitat, and natural features of streams as well as data on land use change at multiple scales of the surrounding landscapes from satellite images. A total of 99 low-order streams were surveyed from five river basins in two large regions (Santarem and Paragominas, both with more than 1 million ha) in the eastern Brazilian Amazon agricultural-forest frontier. I sampled a total of 25,526 fish specimens belonging to 143 species, 27 families and seven orders. Streams appeared to be exceptionally heterogeneous in their abiotic and biotic features. For instance beta diversity of fish assemblages between streams accounted for ca. 70% of the total (gamma) diversity in each river basin. Overall these findings underscore the importance of multiple land use changes and disturbances, at multiple spatial scales, in shaping instream habitat, including links between catchment-scale forest cover and water temperature, and the impacts of road crossings on channel morphology. Both landscape and instream habitat variables were isolated as having a marked effect on stream fish, but instream habitat differences were shown to be particularly important in explaining patterns of fish species abundance compared to other landscape factors that are more amenable to management such as the protection of riparian forest strips. However the results of the thesis also highlight the complexity of Amazonian stream systems and the difficulties in disentangling the effects of multiscale environmental predictor variables underpinned by naturally heterogeneous biophysical characteristics-with instream habitat and fish assemblages affected by a broad suite of drivers that often varied across river basins and regions. I use the findings of the thesis to discuss challenges and recommendations for the management and conservation of low-order streams in Amazonian human-modified landscapes. In particular I emphasize the need for catchment-wide collective management approaches that go beyond the protection of riparian forests within individual properties as prioritized by existing Brazilian environmental legislation. Keywords: forest-agriculture frontier, water quality, physical habitat, human-modified tropical forests, ichthyofauna, deforestation, road crossings

BiodiverCities: A roadmap to enhance the biodiversity and green infrastructure of European cities by 2030

BiodiverCities is a European Parliament Pilot Project, developed with the aim of enhancing the use of Urban Green Infrastructure (UGI) to enhance the condition of urban ecosystems, providing benefits for people and nature. In this report, an evaluation around the most appropriate reporting unit for an urban ecosystems assessment is carried out, comparing Functional Urban Areas (FUA) and Local Administrative Units (LAU). Furthermore, UGI are assessed from a multi-scale perspective. The status and scenarios of UGI in European urbanised areas is first analysed measuring the urban green areas and the tree canopy cover. Secondly, the contribution of UGI to the overall European Green Infrastructure (EU-GI) is quantified, evaluating the respective role of FUA and LAU. Finally, the effect of urban characteristics on biotic homogenization is analysed exploring how urbanised areas impact on avian population and communities in French cities. The results of this study will inform the development of a roadmap for greening cities in Europe in the 2020-2030 decade

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland

An Evaluation of LANDSAT TM Data and GIS Modelling To Identify Significant Woodlands in Southern Ontario

The rapid and reliable identification of woodlands that should be protected from incompatible development is an urgent need in municipal planning to secure a viable natural heritage system. The objective of this research was to test the use of LANDSAT Thematic Mapper (TM) spectral data as a current, unified, and scalable thematic layer to identify ecologically significant woodlands in southwestern Ontario. LANDSAT TM data were classified to obtain a Treed Cover data layer for input into a geographic information system (GIS) model that integrated conventional mapping layers (topography, hydrology, soils, vegetation types); patch metrics (size, shape); and landscape connectivity (proximity, linkages). The Treed Cover layer obtained from the LANDSAT TM data provided a reliable representation of woodland patches when compared to other sources. The integrated data were tested against ecological criteria to identify candidate patches for a preliminary representation of significant woodlands. The GIS model was tested for wildlife habitat conservation planning at the landscape scale using forest area sensitive bird species data and interior habitat data obtained from the Treed Cover layer

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

Ecosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up-to-date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products

High spatial resolution and hyperspectral remote sensing for mapping vegetation species in tropical rainforest

The focus of this study is on vegetation species mapping using high spatial resolution IKONOS-2 and digital Color Infrared (CIR) Aerial Photos (spatial resolution 4 m for IKONOS-2 and 20 cm for CIR) and Hyperion Hyperspectral data (spectral resolution 10 nm) in Pasoh Forest Reserve, Negeri Sembilan. Spatial and spectral separability in distinguishing vegetation species were investigated prior to vegetation species mapping to provide optimal vegetation species discrimination. A total of 88 selected vegetation species and common timber groups of the dominant family Dipterocarpaceae with diameter at breast height more than 30 cm were used in this study, where trees spectra were collected by both in situ and laboratory measurements of foliar samples. The trees spectra were analysed using first and second order derivative analysis together with scatter matrix plot based on multiobjective optimization algorithm to identify the best separability and sensitive wavelength portions for vegetation species mapping. In high spatial resolution data mapping, both IKONOS-2 and CIR data were classified by supervised classification approach using maximum likelihood and neural network classifiers, while the Hyperion data was classified by spectral angle mapper and linear mixture modeling. Results of this study indicate that only a total of ten common timber group of dominant Dipterocarpaceae genus were able to be recognized at significant divergence. Both high spatial resolution data (IKONOS-2 and CIR) gave very good classification accuracy of more than 83%. The classified hyperspectral data at 30 m spatial resolution gave a classification accuracy of 65%, hence confirming that spatial resolution is more sensitive in identification of tree genus. However, for species mapping, both high spatial and spectral remotely sensed data used are marginally less sensitive than at genus level