State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

Ghana has retained a substantial area of tropical forests in an extensive network of protected reserves. These forests are impacted by land uses such as logging, mining, and agriculture as well as wildfires. We studied forest disturbance and recovery from 2013 to 2020 using annual maps of forest cover derived from Landsat imagery. Fire-associated disturbance was distinguished using VIIRS active fire data. We used boosted regression trees to model disturbances in closed and open forests as a function of climate variability, human accessibility, and landscape structure. A total of 3562 km² of forest reserves were disturbed, of which 17% (615 km²) were fire disturbances and 83% (2946 km²) were non-fire disturbances. Of the total disturbed area, 68% was degradation (change from closed to open forest), 28% was open forest loss, and only 4% was closed forest loss. Over the same period, 2702 km² of forest reserves recovered, with 1948 km² of these recovering to closed-canopy forests. Fire disturbances were strongly associated with precipitation anomalies and occurred mostly in drier years, whereas non-fire disturbances had weaker relationships with precipitation. Disturbances in closed forests occurred in landscapes where closed forest cover was already low. In contrast, disturbances in open forests were most common in locations with intermediate levels of population pressure from nearby cities and proximity to non-forest land cover. The results support the idea that forest disturbance in Ghana is a multi-stage process involving degradation of closed forests followed by loss of the resulting open forests. Although non-fire disturbance rates are consistent from year to year, sharp increases in fire disturbance occur in drought years. Locations with the highest disturbance risk are associated with measurable indicators of climate, human pressure, and fragmentation, which can be used to identify these areas for conservation and forest restoration activities.

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning and development. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by rapid population growth. Over the past decade, multiple projects have been undertaken to produce regional and national land cover maps. However, using different classification systems and legends has made updating and sharing land cover information challenging. This has resulted in the inefficient use of human and financial resources. The development of the Land Cover Meta Language (LCML) based on International Organization for Standardization (ISO) standards offers an opportunity to create a standardized classification system. This system would enable easier integration of regional and national data, efficient management of information, and better resource utilization in West Africa. This article emphasizes the process and the need for multistakeholder collaboration in developing a standardized land cover classification system for West Africa, which is currently nonexistent. It presents the survey data collected to evaluate historical, current, and future land cover mapping projects in the region and provides relevant use cases as examples for operationalizing a standardized land cover classification legend for West Africa.