Madagascar, one of Earth’s biodiversity hotpots, is characterized by heterogeneous landscapes and huge land cover change. To date, fine, reliable and timely land cover information is scarce in Madagascar. However, mapping high-resolution land cover map in the tropics has been challenging due to limitations associated with heterogeneous landscapes, the volume of satellite data used, and the design of methodology. In this study, we proposed an automatic approach in which the tile-based model was used on each tile (defining an extent of 1° × 1° as a tile) for mapping land cover in Madagascar. We combined spectral-temporal, textural and topographical features derived from all available Sentinel-2 observations (i.e., 11,083 images) on Google Earth Engine (GEE). We generated a 10-m land cover map for Madagascar, with an overall accuracy of 89.2% based on independent validation samples obtained from a field survey and visual interpretation of very high-resolution (0.5–5 m) images. Compared with the conventional approach (i.e., the overall model used in the entire study area), our method enables reduce the misclassifications between several land cover types, including impervious land, grassland and wetland. The proposed approach demonstrates a great potential for mapping land cover in other tropical or subtropical regions.

Remote-sensing products provide opportunities to monitor complex phenomena on the Earth from space for good decision-making. An example is land-cover maps, which are useful for monitoring both human- and naturally induced environmental changes to enhance environmental management for the benefit of the society. Yet there are numerous critical areas of the world such as biodiversity hotspots where the inaccessibility of quality satellite images has hindered progress in our understanding and management of the natural environment. The West African biodiversity hotspot is one such area where persistent thick clouds in freely available satellite images has been a great discouragement to the production of local land-cover maps to monitor the ongoing deforestation and rapid environmental change. Ghana is a country in West Africa where no land-cover map (except global ones) has been published for over a decade although large tracts of land have been converted from their natural states to agricultural land. In this article, we present 30 m land-cover maps of the country using Landsat 8 images and three popular classifiers – Maximum Likelihood Classifier (MLC), Support Vector Machines (SVMs), and random forest (RF) – for the year 2015. We produced these maps for use in our future biodiversity assessment in the region. An overall accuracy (OA) of 85.5% (kappa = 0.77) was achieved for MLC, with similar but slightly lower accuracies for RF and SVM, which indicates that advanced classification algorithms may not have many advantages when applied to process only multispectral optical data. We observe that shrublands, croplands, and orchards are the dominant classes occupying 35.6%, 31.9%, and 18.4% of the country’s land area, respectively. This further indicates that 50.0% of the country’s land area is under agriculture. Thick forests occupy only 8.0% of the land and are almost entirely confined to forest reserves, highlighting the usefulness of these nature reserves. Our maps will provide input for research advancement, policy formulation, and environmental management at both national and subnational levels.

High-quality training and validation samples are critical components of land-cover and land-use mapping tasks in remote sensing. For large area mapping it is much more difficult to build such sample sets due to the huge amount of work involved in sample collection and image processing. As more and more satellite data become available, a new trend emerges in land-cover mapping that takes advantage of images acquired beyond the greenest season. This has created the need for constructing sample sets that can be used in classifying images of multiple seasons. On the other hand, seasonal land-cover information is also becoming a new demand in land and climate change studies. Here we produce the first training and validation data sets with seasonal labels in order to support the production of seasonal land-cover data for entire Africa. Nonetheless, for the first time, two classification systems were created for the same set of samples. We adapted the finer resolution observation and monitoring of global land cover (FROM-GLC) and the Food and Agriculture Organization (FAO) Land Cover Classification System legends. Locations of training-sample units of FROM-GLC were repurposed here. Then we designed a process to enlarge the training-sample units to increase the density of samples in the feature space of spectral characteristics of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series and Landsat imagery. Finally, we obtained 15,799 training-sample units and 7430 validation-sample units. The land-cover type at each point was recorded at the time of maximum greenness in addition to four seasons in a year. Nearly half of the sample units were also suitable for 500 m resolution MODIS data. We analysed the representativeness of the training and validation sets and then provided some suggestions about their use in improving classification accuracies of Africa.

Global oil palm plantations have expanded in the last few decades, resulting in negative impacts on the environment. Satellite remote sensing plays an important role in monitoring the expansion of oil palm plantations, but requires high-quality ground samples for training and validation. To facilitate the monitoring of oil palm plantations on a large scale, we propose an oil palm sample database that includes the five countries with the largest areas of oil palm plantations: Indonesia, Malaysia, Nigeria, Thailand, and Ghana. In total, 45,896 samples were collected using a hexagonal sampling design. High-resolution images from Google Earth, the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, and Landsat optical images were used to identify oil palm plantations and other types of land cover (croplands, forests, grasslands, shrublands, water, hard surfaces, and bare land). The characteristics of oil palm cover and its environment, including PALSAR backscattering coefficients, terrain, and climate recorded in this database are also discussed. The results indicate that using the PALSAR band algebra threshold alone is not recommended to distinguish oil palm from other land-cover/use types.

This study presents PlanteSaine, a novel mobile application powered by Artificial Intelligence (AI) models explicitly designed for maize, tomato, and onion farmers in Burkina Faso. Agriculture in Burkina Faso, like many developing nations, faces substantial challenges from plant pests and diseases, posing threats to both food security and economic stability. PlanteSaine addresses these challenges by offering a comprehensive solution that provides farmers with real-time identification of pests and diseases. Farmers capture images of affected plants with their smartphones, and PlanteSaine’s AI system analyzes these images to provide accurate diagnoses. The application’s offline functionality ensures accessibility even in remote areas with limited Internet connectivity, while its messaging feature facilitates communication with agricultural authorities for guidance and support. Additionally, PlanteSaine includes an emergency alert mechanism to notify farmers about pest and disease outbreaks, enhancing their preparedness to deal with these threats. An AI-driven framework, featuring an image feature extraction phase with EfficientNetB3 and an artificial neural network (ANN) classifier, was developed and integrated into PlanteSaine. The evaluation of PlanteSaine demonstrates its superior performance compared to baseline models, showcasing its effectiveness in accurately detecting diseases and pests across maize, tomato, and onion crops. Overall, this study highlights the potential of PlanteSaine to revolutionize agricultural technology in Burkina Faso and beyond. Leveraging AI and mobile computing, PlanteSaine provides farmers with accessible and reliable pest and disease management tools, ultimately contributing to sustainable farming practices and enhancing food security. The success of PlanteSaine underscores the importance of interdisciplinary approaches in addressing pressing challenges in global agriculture