Capturing the scope and trajectory of changes in land use and land cover (LULC) is critical to urban and regional planning, natural resource sustainability and the overall information needs of policy makers. Studies on LULC change are generally conducted within peaceful environments and seldom incorporate areas that are politically volatile. Consequently, the role of civil conflict on LULC change remains elusive. Using a dense time stack of Landsat Thematic Mapper images and a hybrid classification approach, this study analysed LULC changes in Kono District between 1986–1991, 1991–2002 and 2002–2007 with the overarching goal of elucidating deviations from typical changes in LULC caused by Sierra Leone's civil war (1991–2002). Informed by social survey and secondary data, this study engaged the drivers that facilitated LULC changes during war and non-war periods in a series of spatial regression models in exploring the interface between civil conflict and LULC change.

Involuntary migration triggered by war has the capacity to generate substantial socioeconomic and environmental changes in cities of developing countries, resulting in aberrant alterations to land use and land cover (LULC). This scenario has the potential to diminish the quality of life of inhabitants of a city and present administrative challenges for government and other officials. Gauging the scope and trajectory of urban LULC changes in a war-related environment is pivotal for urban and regional planning, the sustainability of natural resources, and information needs of policy makers. Scholarships that link remote sensing to social science mostly focus on a non-conflict environment, resulting in little or no information on the ramifications of conflict-induced forced migration on changes in LULC. As a result, the role of civil conflict-induced forced migration on the composition and configuration of urban landscape in developing countries remains elusive. This study employs a dense time stack of Landsat-5 Thematic Mapper (TM) images and a hybrid classification approach that integrates linear spectral unmixing and an ensemble decision-tree classifier to characterize LULC in a primate city and two lower-ranked cities in Sierra Leone. The study examined three time-steps which span 1986–1991, 1991–2002, and 2002–2010 with the overarching goal of elucidating changes in LULC conditioned by civil conflict. Image classification accuracy (overall accuracy) ranged between 84.0% and 90.2%. The study demonstrated that civil conflict has the capacity to trigger notable growth in urban agricultural land (37.4%) in a primate city, while the expansion of residential (112.7%) and industrial/commercial (18.7%) lands is more prominent in a lower-ranked city. The study further revealed that population expansion does not necessarily result in significant growth in residential area in a primate city that has experienced civil conflict.

Watershed planning is a pivotal exercise for all jurisdictions irrespective of size, landscape complexity, or other nuances. As a result of the intricate relationship between land use/land cover (LULC) and water resources, it becomes prudent to not only develop historical and contemporary LULC data for watershed planning purposes, but more importantly, the production of future LULC datasets has the potential to better inform watershed planners. This study explored an optimal workflow that can be adopted for the production of baseline LULC input images from a moderate spatial resolution sensor such as Landsat, and the identification, translation, and configuration of land change drivers and regional comprehensive plan prescriptions in the creation of future LULC data for a regional watershed. The study conducted in the Lower Chippewa River Watershed, Wisconsin, USA demonstrated that an object-based hybrid classification approach resulted in the generation of improved projected images with a 15% increase in area under the curve (AUC) value compared to a pixel-based hybrid classification method even though both methods displayed comparable overall image classification accuracies (≤ 1.8%). Results further displayed that configuring anthropogenic drivers in a trend format rather than individual year values can result in a more efficient training of a multi-layer perceptron neural network – Markov Chain model. The calibrated and validated model demonstrated that on average, residential, commercial, institutional, green vegetation/shrub, and industrial LULC are expected to grow through 2050, though at a slower rate (12%) compared to contemporary period (39%), while forest and agricultural lands are slated to decline (−2%).

Soil erosion monitoring is a pivotal exercise at macro through micro landscape levels, which directly informs environmental management at diverse spatial and temporal scales. The monitoring of soil erosion can be an arduous task when completed through ground-based surveys and there are uncertainties associated with the use of large-scale medium resolution image-based digital elevation models for estimating erosion rates. LiDAR derived elevation models have proven effective in modeling erosion, but such data proves costly to obtain, process, and analyze. The proliferation of images and other geospatial datasets generated by unmanned aerial systems (UAS) is increasingly able to reveal additional nuances that traditional geospatial datasets were not able to obtain due to the former’s higher spatial resolution. This study evaluated the efficacy of a UAS derived digital terrain model (DTM) to estimate surface flow and sediment loading in a fluvial aggregate excavation operation in Waukesha County, Wisconsin. A nested scale distributed hydrologic flow and sediment loading model was constructed for the UAS point cloud derived DTM. To evaluate the effectiveness of flow and sediment loading generated by the UAS point cloud derived DTM, a LiDAR derived DTM was used for comparison in consonance with several statistical measures of model efficiency. Results demonstrate that the UAS derived DTM can be used in modeling flow and sediment erosion estimation across space in the absence of a LiDAR-based derived DTM.