High-resolution benthic habitat data fill an important knowledge gap for many areas of the world and are essential for strategic marine conservation planning and implementing effective resource management. Many countries lack the resources and capacity to create these products, which has hindered the development of accurate ecological baselines for assessing protection needs for coastal and marine habitats and monitoring change to guide adaptive management actions. The PlanetScope (PS) Dove Classic SmallSat constellation delivers high-resolution imagery (4 m) and near-daily global coverage that facilitates the compilation of a cloud-free and optimal water column image composite of the Caribbean’s nearshore environment. These data were used to develop a first-of-its-kind regional thirteen-class benthic habitat map to 30 m water depth using an object-based image analysis (OBIA) approach. A total of 203,676 km2 of shallow benthic habitat across the Insular Caribbean was mapped, representing 5% coral reef, 43% seagrass, 15% hardbottom, and 37% other habitats. Results from a combined major class accuracy assessment yielded an overall accuracy of 80% with a standard error of less than 1% yielding a confidence interval of 78%–82%. Of the total area mapped, 15% of these habitats (31,311.7 km2) are within a marine protected or managed area. This information provides a baseline of ecological data for developing and executing more strategic conservation actions, including implementing more effective marine spatial plans, prioritizing and improving marine protected area design, monitoring condition and change for post-storm damage assessments, and providing more accurate habitat data for ecosystem service models.

Automated and visual approaches for the monitoring of refugee or IDP camps based on satellite data are very important as independent information sources, especially for insecure and remote areas. Nevertheless, monitoring based on satellite data always has a certain degree of uncertainty, e.g. due to data quality, complexity of the area of investigation, seasonal pheonological problems, or algorithmic limitations. Within this paper, we aim to quantify one of these limiting aspects: the factor of vegetation (i.e. tree) growth and its effect on multi-temporal dwelling monitoring, hampering the identification of dwellings on the ground. For the refugee camp Djabal, Chad, we found that 2506 dwellings (25 %) of 2010 are at least partly affected by tree growth three years later (2013), which is influencing automated extraction methods, as well as visual interpretations. 395 of these dwellings were completely covered by vegetation and vegetation shadow, and were therefore not detectable anymore. Taking this factor into account, the decrease of dwellings between 2010 and 2013 is potentially lowered from 10 % to 5 %. SPRÖHNLE et al. (2014) showed a comparison of different algorithms and visual interpretations and their robustness in regard of camp complexity. Within this paper, we aim to

This study deals with the long-term monitoring of the environmental impact of the refugee camp Lukole, Tanzania. Based on high resolution (HR) satellite time series of different Landsat sensors, the whole lifespan of the camp is covered, starting before the camp was established (1994) until seven years after the dismantling (2015). A fully automated preclassification approach for different Landsat sensors was applied, and the results were integrated into a post-classification object-specific change comparison to quantify de-and reforestation processes during this time. The second part of the study will focus on different visualization methods to display the changes throughout the whole study area over time.

The use of HR and VHR (high/very high spatial resolution) imagery and OBIA (objectbased image analysis) offers new possibilities for monitoring activities in and around refugee camps to manage, understand, and assess developments and impacts of the camp on its environment (see for example TIEDE et al. 2013, HAGENLOCHER et al. 2012). Here we demonstrate how VHR imagery in combination with OBIA can be used to retrieve and create valuable information about a remote refugee camp and its surroundings before, during, and after the dismantling and the repatriation process. Feature extraction approaches for single dwellings and further information retrieval, as well as land cover classification for the refugee camp Lukole in Tanzania were combined for an integrated monitoring approach.