A globally relevant change taxonomy and evidence-based change framework for land monitoring

A globally relevant and standardized taxonomy and framework for consistently describing land cover change based on evidence is presented, which makes use of structured land cover taxonomies and is underpinned by the Driver-Pressure-State�Impact-Response (DPSIR) framework. The Global Change Taxonomy currently lists 246 classes based on the notation ‘impact (pressure)’, with this encompassing the consequence of observed change and associated reason(s), and uses scale-independent terms that factor in time. Evidence for different impacts is gathered through temporal comparison (e.g., days, decades apart) of land cover classes constructed and described from Environmental Descriptors (EDs; state indicators) with pre-defined measurement units (e.g., m, %) or categories (e.g., species type). Evidence for pressures, whether abiotic, biotic or human-influenced, is similarly accumulated, but EDs often differ from those used to determine impacts. Each impact and pressure term is defined separately, allowing flexible combination into ‘impact (pressure)’ categories, and all are listed in an openly accessible glossary to ensure consistent use and common understanding. The taxonomy and framework are globally relevant and can reference EDs quantified on the ground, retrieved/classified remotely (from groundbased, airborne or spaceborne sensors) or predicted through modelling. By providing capacity to more consistently describe change processes—including land degradation, desertification and ecosystem restoration—the overall framework addresses a wide and diverse range of local to international needs including those relevant to policy, socioeconomics and land management. Actions in response to impacts and pressures and monitoring towards targets are also supported to assist future planning, including impact mitigation actions

A globally relevant change taxonomy and evidence-based change framework for land monitoring

A globally relevant and standardized taxonomy and framework for consistently describing land cover change based on evidence is presented, which makes use of structured land cover taxonomies and is underpinned by the Driver-Pressure-State-Impact-Response (DPSIR) framework. The Global Change Taxonomy currently lists 246 classes based on the notation 'impact (pressure)', with this encompassing the consequence of observed change and associated reason(s), and uses scale-independent terms that factor in time. Evidence for different impacts is gathered through temporal comparison (e.g., days, decades apart) of land cover classes constructed and described from Environmental Descriptors (EDs; state indicators) with pre-defined measurement units (e.g., m, %) or categories (e.g., species type). Evidence for pressures, whether abiotic, biotic or human-influenced, is similarly accumulated, but EDs often differ from those used to determine impacts. Each impact and pressure term is defined separately, allowing flexible combination into 'impact (pressure)' categories, and all are listed in an openly accessible glossary to ensure consistent use and common understanding. The taxonomy and framework are globally relevant and can reference EDs quantified on the ground, retrieved/classified remotely (from ground-based, airborne or spaceborne sensors) or predicted through modelling. By providing capacity to more consistently describe change processes-including land degradation, desertification and ecosystem restoration-the overall framework addresses a wide and diverse range of local to international needs including those relevant to policy, socioeconomics and land management. Actions in response to impacts and pressures and monitoring towards targets are also supported to assist future planning, including impact mitigation actions

Mapping Coastal Habitats in Wales

Many areas across Europe are mapped and monitored using a large range of different data types, sources and classification schemes leading to gaps in the knowledge required to fulfill the European Council?s Habitats Directive (1992). The Earth Observation Data for Habitat Monitoring (EODHaM) system, developed during the EU FP7 BioSOS project, introduces a systematic, hierarchical approach that is applicable to all sites and available as a standard, providing classifications of high value for conservation and biodiversity purposes (Lucas et al. Int J Appl Earth Observ Geoinf 37:17?28, 2015). The system is built on the Land Cover Classification System (LCCS) developed by the FAO for use in the field. The aim of this project is to generate accurate maps of the location, extent and condition of coastal Annex I habitats at Kenfig Burrows Special Area of Conservation (SAC), using VHR Worldview-2 data. Indices, such as Normalized Difference Vegetation Index (NDVI) allow straightforward visual threshold determination in the rule base, classifying LCCS Level 3 with accuracies of 90% and above. Beyond Level 3, in situ data is key for training and validating EO data to determine if (a) lifeforms/habitats are separable with the available EO data, and (b) suitable thresholds can be determined for classification. Numerous indices can be calculated, and using the GPS point training data, a separability analysis based on Analysis of Variance (ANOVA) allows those with the highest separation scores to be chosen as layers for classification. By plotting the training data sets into boxplots, suitable thresholds are determined. The appropriateness of LCCS here varies with specific sites; for example, slack habitat in sand dune ecosystems can be accurately mapped from contextual information derived from slope (calculated using VHR LiDAR data) and can therefore be translated to habitat from LCCS Level 3. Classifications are therefore translated from land cover to habitat after LCCS Level 3 instead of following the hierarchy to Level 4 and beyond. Once the broad habitat baseline is mapped, thresholds become restricting as they set clear straight lines in the feature space when classifying, therefore machine learning techniques such as random forest and/or support vector machines are more suitable for determining whether dominant species within broad habitat classes can be separated and classified accurately. By classifying dominant species, condition of habitats can be inferred. With accuracies of classifying some habitats higher than others when implementing EO data into a monitoring system, field surveying can never be ruled out to attain the knowledge required for the habitats directive. However, surveying can be applied specifically to those habitats that EO data cannot sufficiently classif