Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Integration of LiDAR and QuickBird imagery for mapping riparian zones in Australian tropical savannas.

Riparian zones are exposed to increasing pressures because of disturbance from agricultural and urban expansion and overgrazing. Accurate and cost-effective mapping of riparian environments is important for managing their functions associated with water quality, biodiversity, and wildlife habitats. The objective of this research was to integrate Light Detection and Ranging (LiDAR) and high spatial resolution QuickBird-2 imagery to estimate riparian zone attributes. A digital terrain model (DTM), a tree canopy model (TCM) and a plant projective cover (PPC) map were first obtained from the LiDAR data. The LiDAR-derived products and the QuickBird bands were then combined in an object-oriented approach to map riparian vegetation, streambed, vegetation overhang, bare ground, woodlands and rangelands. These products were also used to assess the riparian zone width. The overall result was a combined method, taking advantage of both optical and airborne laser systems, for mapping riparian forest structural parameters and riparian zone dimensions. This work shows the accuracy able to be obtained by integrating LiDAR data with high spatial resolution optical imagery to provide more detailed information for riparian zone management

Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and Lidar

Swanton Pacific Ranch is an approximately 1,300 ha working ranch and forest in northern Santa Cruz County, California, managed by California Polytechnic State University, San Luis Obispo (Cal Poly). On August 12, 2009, the Lockheed Fire burned 300 ha of forestland, 51% of the forested area on the property, with variable fire intensity and mortality. This study used existing inventory data from 47 permanent 0.08 ha (1/5 ac) plots to compare the accuracy of classifying mortality resulting from the fire using digital multispectral imagery and LiDAR. The percent mortality of trees at least 25.4 cm (10”) DBH was aggregated to three classes (0-25, 25-50, and 50-100%). Three separate Classification Analysis and Regression Tree (CART) models were created to classify plot mortality. The first used the best imagery predictor variable of those considered, the Normalized Difference Vegetation Index (NDVI) calculated from 2010 National Agricultural Imagery Program (NAIP) aerial imagery, with shadowed pixel values adjusted, and non-canopy pixels removed. The second used the same NDVI in combination with selected variables from post-fire LiDAR data collected in 2010. The third used the same NDVI in combination with selected variables from differenced LiDAR data calculated using post-fire LiDAR and pre-fire LiDAR collected in 2008. The imagery alone was 74% accurate; the imagery and post-fire LiDAR model was 85% accurate, while the imagery and differenced LiDAR model was 83% accurate. These findings indicate that remote sensing data can accurately estimate post-fire mortality, and that the addition of LiDAR data to imagery may yield only modest improvement

Rooftop surface temperature analysis in an urban residential environment

The urban heat island (UHI) phenomenon is a significant worldwide problem caused by rapid population growth and associated urbanization. The UHI effect exacerbates heat waves during the summer, increases energy and water consumption, and causes the high risk of heat-related morbidity and mortality. UHI mitigation efforts have increasingly relied on wisely designing the urban residential environment such as using high albedo rooftops, green rooftops, and planting trees and shrubs to provide canopy coverage and shading. Thus, strategically designed residential rooftops and their surrounding landscaping have the potential to translate into significant energy, long-term cost savings, and health benefits. Rooftop albedo, material, color, area, slope, height, aspect and nearby landscaping are factors that potentially contribute. To extract, derive, and analyze these rooftop parameters and outdoor landscaping information, high resolution optical satellite imagery, LIDAR (light detection and ranging) point clouds and thermal imagery are necessary. Using data from the City of Tempe AZ (a 2010 population of 160,000 people), we extracted residential rooftop footprints and rooftop configuration parameters from airborne LIDAR point clouds and QuickBird satellite imagery (2.4 m spatial resolution imagery). Those parameters were analyzed against surface temperature data from the MODIS/ASTER airborne simulator (MASTER). MASTER images provided fine resolution (7 m) surface temperature data for residential areas during daytime and night time. Utilizing these data, ordinary least squares (OLS) regression was used to evaluate the relationships between residential building rooftops and their surface temperature in urban environment. The results showed that daytime rooftop temperature was closely related to rooftop spectral attributes, aspect, slope, and surrounding trees. Night time temperature was only influenced by rooftop spectral attributes and slope

Rooftop Surface Temperature Analysis in an Urban Residential Environment

abstract: The urban heat island (UHI) phenomenon is a significant worldwide problem caused by rapid population growth and associated urbanization. The UHI effect exacerbates heat waves during the summer, increases energy and water consumption, and causes the high risk of heat-related morbidity and mortality. UHI mitigation efforts have increasingly relied on wisely designing the urban residential environment such as using high albedo rooftops, green rooftops, and planting trees and shrubs to provide canopy coverage and shading. Thus, strategically designed residential rooftops and their surrounding landscaping have the potential to translate into significant energy, long-term cost savings, and health benefits. Rooftop albedo, material, color, area, slope, height, aspect and nearby landscaping are factors that potentially contribute. To extract, derive, and analyze these rooftop parameters and outdoor landscaping information, high resolution optical satellite imagery, LIDAR (light detection and ranging) point clouds and thermal imagery are necessary. Using data from the City of Tempe AZ (a 2010 population of 160,000 people), we extracted residential rooftop footprints and rooftop configuration parameters from airborne LIDAR point clouds and QuickBird satellite imagery (2.4 m spatial resolution imagery). Those parameters were analyzed against surface temperature data from the MODIS/ASTER airborne simulator (MASTER). MASTER images provided fine resolution (7 m) surface temperature data for residential areas during daytime and night time. Utilizing these data, ordinary least squares (OLS) regression was used to evaluate the relationships between residential building rooftops and their surface temperature in urban environment. The results showed that daytime rooftop temperature was closely related to rooftop spectral attributes, aspect, slope, and surrounding trees. Night time temperature was only influenced by rooftop spectral attributes and slope

Comparison of Urban Tree Canopy Classification With High Resolution Satellite Imagery and Three Dimensional Data Derived From LIDAR and Stereoscopic Sensors

Indiana University-Purdue University Indianapolis (IUPUI)Despite growing recognition as a significant natural resource, methods for accurately estimating urban tree canopy cover extent and change over time are not well-established. This study evaluates new methods and data sources for mapping urban tree canopy cover, assessing the potential for increased accuracy by integrating high-resolution satellite imagery and 3D imagery derived from LIDAR and stereoscopic sensors. The results of urban tree canopy classifications derived from imagery, 3D data, and vegetation index data are compared across multiple urban land use types in the City of Indianapolis, Indiana. Results indicate that incorporation of 3D data and vegetation index data with high resolution satellite imagery does not significantly improve overall classification accuracy. Overall classification accuracies range from 88.34% to 89.66%, with resulting overall Kappa statistics ranging from 75.08% to 78.03%, respectively. Statistically significant differences in accuracy occurred only when high resolution satellite imagery was not included in the classification treatment and only the vegetation index data or 3D data were evaluated. Overall classification accuracy for these treatment methods were 78.33% for both treatments, with resulting overall Kappa statistics of 51.36% and 52.59%

Fire models and methods to map fuel types: The role of remote sensing.

Understanding fire is essential to improving forest management strategies. More specifically, an accurate knowledge of the spatial distribution of fuels is critical when analyzing, modelling and predicting fire behaviour. First, we review the main concepts and terminology associated with forest fuels and a number of fuel type classifications. Second, we summarize the main techniques employed to map fuel types starting with the most traditional approaches, such as field work, aerial photo interpretation or ecological modelling. We pay special attention to more contemporary techniques, which involve the use of remote sensing systems. In general, remote sensing systems are low-priced, can be regularly updated and are less time-consuming than traditional methods, but they are still facing important limitations. Recent work has shown that the integration of different sources of information andmethods in a complementary way helps to overcome most of these limitations. Further research is encouraged to develop novel and enhanced remote sensing techniques

Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images

Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above‐ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment

Assessment of Different Remote Sensing Data for Forest Structural Attributes Estimation in the Hyrcanian forests

Aim of study: The objective of the study was the comparative assessment of various spatial resolutions of optical satellite imagery including Landsat-TM, ASTER, and Quickbird data to estimate the forest structure attributes of Hyrcanian forests, Golestan province, northernIran.Material and methods: The 112 square plots with area of0.09 ha were measured using a random cluster sampling method and then stand volume, basal area, and tree stem density were computed using measured data. After geometric and atmospheric corrections of images, the spectral attributes from original and different synthetic bands were extracted for modelling. The statistical modelling was performed using CART algorithm. Performance assessment of models was examined using the unused validation plots by RMSE and bias measures.Main Results: The results showed that model of Quickbird data for stand volume, basal area, and tree stem density had a better performance compared to ASTER and TM data. However, estimations by ASTER and TM imagery had slightly similar results for all three parameters.Research highlights: This study exposed that the high-resolution satellite data are more useful for forest structure attributes estimation in the Hyrcanian broadleaves forests compared with medium resolution images without consideration of images costs. However, regarding to be free of the most medium resolution data such as ASTER and TM,ETM+ or OLI images, these data can be used with slightly similar results.  Keywords: Forest structure attributes; quickbird; ASTER; TM; CART algorithm; Hyrcanian forests

Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images

Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above‐ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment