Biodiversity is under threat from anthropogenic pressures, in particular in biodiversity-rich developing countries. Development cooperation actors, who traditionally focus on the improvement of socio-economic conditions in the South, are increasingly acknowledging the linkages between poverty and biodiversity, e.g. by referring to the ecosystem services framework. However, there are many different framings which stress the need for biodiversity integration and which influence how biodiversity and development are and/or should be linked. Moreover, there is a gap between the lip service paid to biodiversity integration and the reality of development cooperation interventions. This study analyses how biodiversity framings are reflected in environmental impact assessment (EIA) practice, and how these framings influence EIA and decision-making. The findings, based on an in-depth qualitative analysis of World Bank EIAs undertaken in West Africa, indicate the incoherent quality but also the dominance of the‘utilitarian’ and‘corrective’ framings, which respectively stress human use of nature and mitigation of negative unintended development impacts. Identifying and highlighting these discursive trends leads to increased awareness of the importance of biodiversity among all development actors in North and South. However, some framings may lead to an overly narrow human-centred approach which downplays the intrinsic value of biodiversity. This study proposes recommendations for an improved integration of biodiversity in development cooperation, including the need for more systematic baseline studies in EIAs.
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RBINS Staff Publications 2017
Nearshore coastal waters are highly dynamic in both space and time. They can be difficult to sample using conventional methods due to their shallow depth, tidal variability, and the presence of strong currents and breaking waves. High resolution satellite sensors can be used to provide synoptic views of Surface Temperature (ST), but the performance of such ST products in the nearshore zone is poorly understood. Close to the shoreline, the ST pixels can be influenced by mixed composition of water and land, as a result of the sensor’s spatial resolution. This can cause thermal adjacency effects due to the highly different diurnal temperature cycles of water bodies and land. Previously, temperature data collected during surfing sessions has been proposed for validation of moderate resolution (1 km pixel size) satellite ST products. In this paper we use surfing temperature data to validate three high resolution (100 m resampled to 30 m pixel size) ST products derived from the Thermal InfraRed Sensor (TIRS) on board Landsat 8 (L8). ST was derived from Collection 1 and 2 Level 1 data (C1L1 and C2L1) using the Thermal Atmospheric Correction Tool (TACT), and was obtained from the standard Collection 2 Level 2 product (USGS C2L2). This study represents one of the first evaluations of the new C2 products, both L1 and L2, released by USGS at the end of 2020. Using automated matchup and image quality control, 88 matchups between L8/TIRS and surfers were identified, distributed across the North-Western semihemisphere. The unbiased Root Mean Squared Difference (uRMSD) between satellite and in situ measurements was generally ¡ 2 K, with warm biases (Mean Average Difference, MAD) of 1.7 K (USGS C2L2), 1.3 K (TACT C1L1) and 0.8 K (TACT C2L1). Large interquartile ranges of ST in 5 × 5 satellite pixels around the matchup location were found for several images, especially for the summer matchups around the Californian coast. By filtering on target stability the number of matchups reduced to 31, which halved the uRMSD across the three methods (to around 1.1K), MAD were much lower, i.e. 1.1 K (USGS C2L2), 0.6 K (TACT C1L1), and 0.2 K (TACT C2L1). The larger biases of the C2L2 product compared to TACT C2L1 are caused as a result of: (1) a lower emissivity value for water targets used in USGS C2L2, and (2) differences in atmospheric parameter retrieval, mainly from differences in upwelling atmospheric radiance and lower atmospheric transmittance retrieved by USGS C2L2. Additionally, tiling artefacts are present in the C2L2 product, which originate from a coarser atmospheric correction process. Overall, the L8/TIRS derived ST product compares well with in situ measurements made while surfing, and we found the best performing ST product for nearshore coastal waters to be the Collection 2 Level 1 data processed with TACT.
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RBINS Staff Publications 2022
