Evaluating the use of an object-based approach to lithological mapping in vegetated terrain

Remote sensing-based approaches to lithological mapping are traditionally pixel-oriented, with classification performed on either a per-pixel or sub-pixel basis with complete disregard for contextual information about neighbouring pixels. However, intra-class variability due to heterogeneous surface cover (i.e., vegetation and soil) or regional variations in mineralogy and chemical composition can result in the generation of unrealistic, generalised lithological maps that exhibit the “salt-and-pepper” artefact of spurious pixel classifications, as well as poorly defined contacts. In this study, an object-based image analysis (OBIA) approach to lithological mapping is evaluated with respect to its ability to overcome these issues by instead classifying groups of contiguous pixels (i.e., objects). Due to significant vegetation cover in the study area, the OBIA approach incorporates airborne multispectral and LiDAR data to indirectly map lithologies by exploiting associations with both topography and vegetation type. The resulting lithological maps were assessed both in terms of their thematic accuracy and ability to accurately delineate lithological contacts. The OBIA approach is found to be capable of generating maps with an overall accuracy of 73.5% through integrating spectral and topographic input variables. When compared to equivalent per-pixel classifications, the OBIA approach achieved thematic accuracy increases of up to 13.1%, whilst also reducing the “salt-and-pepper” artefact to produce more realistic maps. Furthermore, the OBIA approach was also generally capable of mapping lithological contacts more accurately. The importance of optimising the segmentation stage of the OBIA approach is also highlighted. Overall, this study clearly demonstrates the potential of OBIA for lithological mapping applications, particularly in significantly vegetated and heterogeneous terrain

Implementation of Regional Burnt Area Algorithms for the GBA2000 Initiative.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Detection of Amazon Forest Degradation Caused by Land Use Changes

Field and satellite optical methods for estimation of chlorophyll content were applied in three study sites of the Ecuadorian Amazon rainforest. Those sites represent a wide range of land use disturbance in secondary and pristine lowland rainforest. The first field method is based on transmittance from the SPAD-502 chlorophyll meter index, the second field method is based on reflectance measurements collected by a spectroradiometer, and the third method estimates chlorophyll content from the PROSPECT radiative transfer model. For the first method, seven models that account for a wide range of vegetation species showed similar average leaf chlorophyll contents until 80 units of SPAD-502. An average of the results of these models was computed and used as ground truth from where a generalized second-order polynomial model was created. For the second method, five chlorophyll indices based on reflectance measurements provided similar chlorophyll content estimations for all SPAD range (15–95 units). The third method estimates chlorophyll content based on the inversion process of the PROSPECT model. The satellite methods estimate vegetation indices sensitive to chlorophyll content from space. All methods have shown to be an alternative approach to detect forest degradation at local and regional levels caused by forest disturbances and land use changes

The impact of vegetation on lithological mapping using airborne multispectral data: a case study for the North Troodos Region, Cyprus

Vegetation cover can affect the lithological mapping capability of space- and airborne instruments because it obscures the spectral signatures of the underlying geological substrate. Despite being widely accepted as a hindrance, few studies have explicitly demonstrated the impact vegetation can have on remote lithological mapping. Accordingly, this study comprehensively elucidates the impact of vegetation on the lithological mapping capability of airborne multispectral data in the Troodos region, Cyprus. Synthetic spectral mixtures were first used to quantify the potential impact vegetation cover might have on spectral recognition and remote mapping of different rock types. The modeled effects of green grass were apparent in the spectra of low albedo lithologies for 30%–40% fractional cover, compared to just 20% for dry grass cover. Lichen was found to obscure the spectra for 30%–50% cover, depending on the spectral contrast between bare rock and lichen cover. The subsequent impact of vegetation on the remote mapping capability is elucidated by considering the outcomes of three airborne multispectral lithological classifications alongside the spectral mixing analysis and field observations. Vegetation abundance was found to be the primary control on the inability to classify large proportions of pixels in the imagery. Matched Filtering outperformed direct spectral matching algorithms owing to its ability to partially unmix pixel spectra with vegetation abundance above the modeled limits. This study highlights that despite the limited spectral sampling and resolution of the sensor and dense, ubiquitous vegetation cover, useful lithological information can be extracted using an appropriate algorithm. Furthermore, the findings of this case study provide a useful insight to the potential capabilities and challenges faced when utilizing comparable sensors (e.g., Landsat 8, Sentinel-2, WorldView-3) to map similar types of terrain

Lithological mapping of the Troodos ophiolite, Cyprus, using airborne LiDAR topographic data

Traditional field-based lithological mapping can be a time-consuming, costly and challenging endeavour when large areas need to be investigated, where terrain is remote and difficult to access and where the geology is highly variable over short distances. Consequently, rock units are often mapped at coarse-scales, resulting in lithological maps that have generalised contacts which in many cases are inaccurately located. Remote sensing data, such as aerial photographs and satellite imagery are commonly incorporated into geological mapping programmes to obtain geological information that is best revealed by overhead perspectives. However, spatial and spectral limitations of the imagery and dense vegetation cover can limit the utility of traditional remote sensing products. The advent of Airborne Light Detection And Ranging (LiDAR) as a remote sensing tool offers the potential to provide a novel solution to these problems because accurate and high-resolution topographic data can be acquired in either forested or non-forested terrain, allowing discrimination of individual rock types that typically have distinct topographic characteristics. This study assesses the efficacy of airborne LiDAR as a tool for detailed lithological mapping in the upper section of the Troodos ophiolite, Cyprus. Morphometric variables (including slope, curvature and surface roughness) were derived from a 4 m digital terrain model in order to quantify the topographic characteristics of four principal lithologies found in the area. An artificial neural network (the Kohonen Self-Organizing Map) was then employed to classify the lithological units based upon these variables. The algorithm presented here was used to generate a detailed lithological map which defines lithological contacts much more accurately than the best existing geological map. In addition, a separate map of classification uncertainty highlights potential follow-up targets for ground-based verification. The results of this study demonstrate the significant potential of airborne LiDAR for lithological discrimination and rapid generation of detailed lithological maps, as a contribution to conventional geological mapping programmes

Application of airborne LiDAR to mapping seismogenic faults in forested mountainous terrain, southeastern Alps, Slovenia

Results are presented of the first airborne LiDAR survey ever flown in Europe for the purpose of mapping the surface expression of earthquake-prone faults. Detailed topographic images derived from LiDAR data of the Idrija and Ravne strike-slip faults in NW Slovenia reveal geomorphological and structural features that shed light on the overall architecture and kinematic history of both fault systems. The 1998 Mw = 5.6, and 2004 Mw = 5.2 Ravne Fault earthquakes and the historically devastating 1511 M = 6.8 Idrija earthquake indicate that both systems pose a serious seismic hazard in the region. Because both fault systems occur within forested terrain, a tree removal algorithm was applied to the data; the resulting images reveal surface scarps and tectonic landforms in unprecedented detail. Importantly, two sites were discovered to be potentially suitable for fault trenching and palaeo-seismological analysis. This study highlights the potential contribution of LiDAR surveying in both low-relief valley terrain and high-relief mountainous terrain to a regional seismic hazard assessment programme. Geoscientists working in other tectonically active regions of the world where earthquake-prone faults are obscured by forest cover would also benefit from LiDAR maps that have been processed to remove the canopy return and reveal the forest floor topography

Multi-disciplinary investigations of active faults in the Julian Alps, Slovenia

UK-Slovenian collaborative research connected to EU COST-Action 625 began in 2003 and has involved interdisciplinary research into the current activity, structural architecture and landscape expression of the Ravne and Idrija strike-slip fault systems in NW Slovenia. The Ravne fault may be the best exposed actively propagating strike-slip fault system in Europe and through combined structural fieldwork, earthquake seismology and airborne LiDAR (Light Detection And Ranging) surveys, a new understanding of the fault’s along-strike segmentation, three dimensional geometry and stepover zone kinematics has been gained. The Idrija Fault in contrast, is poorly exposed, but defines a regional lineament with an intensely brecciated fault core; it may have been responsible for the largest historical earthquake to have ever affected the region. High-resolution LiDAR images recently obtained for both fault systems allow for efficient focussed fieldwork and future work will be devoted to documenting the timing of previous earthquakes and the connectivity and displacement transfer between active faults at the NE corner of the Adria microplate

A contribution for a global nurned area map

The goal of this work was to develop methodologies for burned area mapping at 1 km resolution using SPOT-VEGETATION (VGT) images from tropical (Southeastern Africa and Brazil), temperate (Iberian Peninsula) and boreal (Eastern Siberia / Northeastern China) regions. For each study area seven months of daily images were used in order to map the areas burned during the entire fire season. Linear discriminant analysis or classification trees were applied, depending on the study area, to monthly composite images derived from the daily images, and monthly burned area maps were produced. The final VGT 1 km burned area maps were validated with burned area maps derived from 30 m Landsat imagery, using linear regression. Twenty-four Landsat scenes were used in the validation of the maps produced for the four study areas. The accuracy of the VGT maps was variable, dependent on vegetation type and on the spatial pattern of the burned areas

Tiger habitat quality modelling in Malaysia with Sentinel-2 and InVEST

Deforestation is a threat to habitat quality and biodiversity. In intact forests, even small levels of deforestation can have profound consequences for vertebrate biodiversity. The risk hotspots are Borneo, the Central Amazon, and the Congo Basin. Earth observation (EO) now provides regular, high-resolution satellite images from the Copernicus Sentinel missions and other platforms. To assess the impact of forest conversion and forest loss on biodiversity and habitat quality, forest loss in a tiger conservation landscape in Malaysia is analysed using Sentinel-2 imagery and the InVEST habitat quality model. Forest losses are identified from satellites using the random forest classification and validated with PlanetScope imagery at 3–5 m resolution for a test area. Two scenarios are simulated using InVEST, one with and one without the forest loss maps. The outputs of the InVEST model are maps of tiger habitat quality and habitat degradation in northeast Peninsular Malaysia. In addition to forest loss, OpenStreetMap road vectors and the GLC2000 land-cover map are used to model habitat sensitivity to threats from roads, railways, water bodies, and urban areas. The landscape biodiversity score simulation results fall sharply from ~0.8 to ~0.2 for tree-covered land cover when forest loss is included in the habitat quality model. InVEST makes a reasonable assumption that species richness is higher in pristine tropical forests than in agricultural landscapes. The landscape biodiversity score is used to compare habitat quality between administrative areas. The coupled EO/InVEST modelling framework presented here can support decision makers in reaching the targets of the Kunming-Montreal Global Biodiversity Framework. Forest loss information is essential for the quantification of habitat quality and biodiversity in tropical forests. Next generation ecosystem service models should be co-developed alongside EO products to ensure interoperability

Mapping forest cover and forest cover change with airborne S-band radar

Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover regularly. However, forest classification with optical data is limited by its insensitivity to three-dimensional canopy structure and cloud cover obscuring many forest regions. Synthetic Aperture Radar (SAR) sensors are increasingly being used to mitigate these problems, mainly in the L-, C- and X-band domains of the electromagnetic spectrum. S-band has not been systematically studied for this purpose. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest characterisation. The Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model is utilised to understand the scattering mechanisms in forest canopies at S-band. The MIMICS-I model reveals strong S-band backscatter sensitivity to the forest canopy in comparison to soil characteristics across all polarisations and incidence angles. Airborne S-band SAR imagery over the temperate mixed forest of Savernake Forest in southern England is analysed for its information content. Based on the modelling results, S-band HH- and VV-polarisation radar backscatter and the Radar Forest Degradation Index (RFDI) are used in a forest/non-forest Maximum Likelihood classification at a spatial resolution of 6 m (70% overall accuracy, κ = 0.41) and 20 m (63% overall accuracy, κ = 0.27). The conclusion is that S-band SAR such as from NovaSAR-S is likely to be suitable for monitoring forest cover and its change