A mapping study using remote sensing software called ENVI was conducted utilizing four software algorithms to investigate whether these techniques could accurately classify habitat types and vegetation communities along West Bay of the Galveston Bay Ecosystem from color infra-red (CIR) imagery. The algorithms were used in a small-scale study to investigate which of these techniques could most accurately distinguish habitat types and vegetation communities from the imagery at a site specific location. The most accurate algorithm of the four was used in a large-scale classification study in which entire images were classified utilizing the same data from the small-scale study. Regions of interest (ROIs) were used within ENVI to specify areas of interest within each image that was classified. The locations of ROIs were recorded using a GPS prior to classification, then each was added into ENVI as data points, and each ROI polygon was digitized according to its respective pixel color. Once all of the ROI polygons were completed, each software algorithm was employed. After classification, each habitat type and vegetation community was ground-truthed in order to verify the accuracy of the algorithms. The position points were added as ground truth points within ENVI and an accuracy matrix was assessed. The technique with the greatest averaged accuracy within the smallscale study was selected for the large-scale study. The ROIs and ground truth points used in the smallscale study were used again in the large-scale study. The small-scale study concluded that the Parallelepiped algorithm produced significantly less accurate classifications than the other three. Although the Mahalanobis algorithm was not significantly different from the other two algorithms, it yielded the highest overall average accuracy and was used in the large-scale study. In both the small-scale and large-scale studies there was no significant difference in the two different years of aerial imagery and there were no significant differences in accuracy for locations. None of the software algorithms were accurate at classifying habitat types and vegetation communities using the imagery. The accuracy for the Mahalanobis algorithm was less than 60%. Inaccuracies were largely due to overlapping spectral signatures among habitat types and vegetation communities.

The aim of this study was to determine if the occurrence, thalli length and cover of the epiphytic lichens Alectoria sarmentosa, Bryoria capillaris and Usnea dasypogea differ between sites with inland and coastal climate in Norrbotten county, Sweden. The trunk and branch diameter of the Picea abies trees, on which the lichens were inventoried, were measured as well. B. capillaris was the most common lichen species in both habitats. B. capillaris and A. sarmentosa had significant higher percentage occurrence in inland sites, while the occurrence of U. dasypogea was higher in the coastal sites. For B. capillaris, the percentage cover per branch was also higher in inland than in coastal sites. No significant difference in thallus lengths were found for any species. Obtained climate data suggest that humidity is higher inland, which is favourable for B. capillaris. Litterateur suggest that of all studied species, B. capillaris is the most common species in colder climates while A. sarmentosa is an intermediate and U.dasypogea is a lichen species normally occurring in warmer climates, which might explain their observed occurrence pattern outcome of the study. Linear regression was executed as well where only A.sarmentosa had significant and positive relationships to branch diameter. Other studies support the correlation with branch diameter but not with trunk diameter.

Coastal habitats are highly sensitive to change and highly diverse. Degrading environmental conditions have led to a global decline in biodiversity through loss, modification and fragmentation of habitats, triggering an increased effort to conserve these ecosystems. Remote sensing is important tool for filling in critical information gaps for monitoring habitats, yet significant barriers exist for operational use within the ecological and conservation communities. Reporting on both extent and condition of habitats are critical to fulfil policy requirements, specifically the ECs Habitat’s Directive. This study focuses on the use of Very High Resolution (VHR) optical imagery for retrieving parameters to identifyassociations that can separate habitat boundaries for extent mapping down to species level for indicators of condition, with a focus on operational use. The Earth Observation Data for Habitat Monitoring (EODHaM) system was implemented using Worldview-2 data from two periods (July and September), in situ data and local ecological knowledge for two sites in Wales, Kenfig Burrows SAC and Castlemartin SSSI. The system utilises the Food and Agricultural Organisation’s (FAO) Land Cover Classification System (LCCS) but translations between land cover and habitat schemes are not straight forward and need special consideration that are likely to be site specific. Limitations within therule-based method of the EODHaM system were identified and therefore augmented with machine learning based classification algorithms creating a hybrid method of classification generating accurate (>80% overall accuracy) baseline maps with a more automated and repeatable method. Quantitative methods of validation traditionally used within the remote sensing community do not consider spatial aspects of maps. Therefore, qualitative assessments carried out in the field were used in addition to error matrices, overall accuracy and the kappa coefficient. This required input from ecologists and site specialists, enhancing communication and understanding between the different communities. Generating baseline maps required significant amount of training data and updating baselines through change detection methods is recommended for monitoring. An automated, novel map-to-image change detection was therefore implemented. Natural and anthropogenic changes were successfully detected from Worldview-2 and Sentinel-2 data at Kenfig Burrows. An innovative component of this research was the development of methods, which were demonstrated to be transferable between both sites and increased understanding between remote sensing scientist and ecologist. Through this approach, a more operational method for monitoring site specific habitats through satellite data is proposed, with direct benefits for conservation, environment and policy.