The research, published in the journal Discover Geoscience, was carried out by Dereje Ketema and Addisu Bekele of Bule Hora University together with Barsisa Tola of Salale University. Their starting point was a familiar problem in land-change science: most studies of land use and land cover in Ethiopia have been retrospective, describing what has already happened at regional or national scales, while the most intense transformations are unfolding in small and medium-sized towns that rarely receive dedicated analysis. Fiche, administratively divided into four kebeles and expanding rapidly under population pressure and infrastructure growth, offered precisely the kind of case where predictive evidence could shift planning from reactive to proactive.

To build the historical record, the team turned to the Landsat archive, the workhorse of long-term land-cover mapping. They acquired cloud-free scenes from Landsat 7 ETM+ for 2002 and Landsat 8 OLI/TIRS for 2013 and 2022, keeping cloud cover below ten percent to minimize atmospheric interference. After radiometric calibration to correct for sensor and illumination differences, the images were classified in ENVI 5.3 using a supervised Support Vector Machine approach, an algorithm favored for its ability to deliver high accuracy even with relatively modest training datasets. Following the AFRICOVER classification scheme widely used in East Africa, the landscape was divided into four classes: built-up area, farmland, open space, and shrubland, with training signatures drawn from 50 to 90 sites per class informed by ground control points, historical Google Earth imagery, and expert knowledge of the terrain.

The classification proved remarkably reliable. Validation against reference data, including 117, 147, and 100 randomly sampled testing points for 2002, 2013, and 2022 respectively, plus 55 field-collected ground control points for 2022, produced overall accuracies of 94.9 percent, 96.6 percent, and 98.7 percent, with Kappa coefficients of 0.932, 0.955, and 0.983. All values comfortably exceeded the 85 percent accuracy threshold conventionally required for dependable change detection, giving the researchers a solid foundation for the transition analysis that followed.

What those maps revealed is a landscape in the grip of an accelerating regime shift. In 2002, farmland dominated Fiche’s surroundings, covering 66.4 percent of the study area, or about 2,300 hectares, while built-up surfaces occupied just 10.4 percent, roughly 361 hectares. By 2013 the urban footprint had grown to 16.9 percent, and by 2022 it had surged to 25.8 percent, or 891.89 hectares, an increase of nearly 147 percent over two decades. The pace quickened dramatically in the second decade: built-up expansion ran at 20.4 hectares per year between 2002 and 2013, then jumped to 33.9 hectares per year between 2013 and 2022. Farmland loss followed the same pattern, doubling from 36.1 hectares per year in the first interval to 71.2 hectares per year in the second, culminating in the disappearance of more than 640 hectares of cropland in under ten years. Shrubland fared even worse in relative terms, collapsing by more than half, from 100 hectares to just 44.79.

Transition matrices generated with the MOLUSCE plugin in QGIS added a probabilistic layer to the story, quantifying the likelihood of pixels shifting from one class to another across each interval. Farmland proved to be the primary donor for urban growth: over the full 2002 to 2022 period, its persistence probability fell to just 0.452, with substantial contributions flowing to open space and built-up areas. Built-up land, by contrast, showed deep stability at 0.833, meaning that once land is urbanized in Fiche, it almost never reverts. Shrubland was the most volatile category of all, with a persistence of only 0.180, its area dispersed across farmland, built-up surfaces, and open space. The spatial pattern of conversion followed a proximity-based logic, with peripheral agricultural belts progressively absorbed by the advancing urban fringe rather than being lost at random.

The predictive engine at the heart of the study is a hybrid Cellular Automata-Artificial Neural Network framework, implemented through MOLUSCE’s multilayer perceptron option. The researchers selected the ANN approach over alternatives such as logistic regression and weights of evidence because of its strength in capturing complex, non-linear relationships between land change and its drivers. Those drivers included a digital elevation model, slope, aspect, and distance from roads and existing built-up areas, all standardized to a uniform 30-meter resolution in the UTM Zone 37N coordinate system and screened for multicollinearity using Pearson’s correlation, Cramér’s V, and joint information uncertainty. The network was trained on the 2002 to 2013 transition dynamics with 1,000 iterations, ten hidden layers, a momentum value of 0.06, and a learning rate of 0.001, then coupled with cellular automata allocation rules to simulate the 2022 landscape.

That simulation passed a demanding test. When compared against the actual satellite-derived 2022 classification, the modeled map achieved an overall Kappa of 0.83 and a correctness rate of 88.8 percent, with component Kappa statistics as high as 0.93 for the spatial allocation of change. Confident in the model, the team ran it forward to 2030, 2040, and 2050 under a business-as-usual assumption that historical dynamics and driving factors will persist. The projections show built-up area climbing from 891.89 hectares in 2022 to 1,043.6 hectares by 2030, 1,140.5 by 2040, and 1,268.7 by 2050, a net gain of 376.81 hectares, or 42.2 percent, over 28 years. Farmland, meanwhile, is forecast to fall from 1,261.2 hectares to 762.2 hectares by 2050, a loss of 499 hectares, or 39.6 percent. In proportional terms, built-up land would reach 29.76 percent of the study area in 2030, 32.31 percent in 2040, and 35.74 percent in 2050, while agriculture slides to 29.74, 26.56, and finally 22.01 percent. Open space is projected to keep growing as well, a signal of ongoing land clearing and fragmentation, while the last remnants of shrubland thin further.

The authors frame these numbers as more than a cartographic exercise. Ethiopia, Africa’s second most populous country with over 120 million people, remains one of the world’s least urbanized nations but is urbanizing at unprecedented speed, with the national urban population projected to nearly triple from 15.2 million in 2012 to 42.3 million by 2037. In peri-urban zones like Fiche’s surroundings, that demographic surge translates directly into competition between farms and houses, threatening local food security and the livelihoods of farming communities that the urban expansion progressively marginalizes. The conversion of productive land into impervious surfaces also degrades ecosystem services, alters hydrological processes, and reshapes microclimates, compounding the socioeconomic stakes.

The study’s prescription is correspondingly practical. Ketema and his colleagues argue that Fiche’s trajectory can be bent only through deliberate intervention: strict zoning to protect high-value agricultural land, compact and vertical development strategies that discourage horizontal sprawl, green infrastructure, improved urban mobility, and continuous geospatial monitoring embedded in municipal governance. They also point to the next generation of models, suggesting that integrating higher-resolution Sentinel imagery and socioeconomic variables such as population growth and policy shifts would sharpen future projections beyond what purely spatial drivers can explain. For now, the message from the highlands of Central Ethiopia is unambiguous: without timely action, the satellite record and the neural network agree that by 2050 the town’s fields will have been reduced to a fragile remnant, and the landscape fragmentation that follows may prove irreversible.

Subject of Research: Spatiotemporal analysis and neural network modeling of land use and land cover change and urban expansion in Fiche Town, Central Ethiopia

Article Title: Spatiotemporal analysis and modeling of land use and land cover change in Fiche Town, Central Ethiopia using geospatial techniques and MOLUSCE

Article References: Ketema, D., Bekele, A., & Tola, B. (2026). Spatiotemporal analysis and modeling of land use and land cover change in Fiche Town, Central Ethiopia using geospatial techniques and MOLUSCE. Discover Geoscience, 4(1), Article 309. https://doi.org/10.1007/s44288-026-00689-x

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00689-x

Keywords: land use land cover change, urban expansion, Ethiopia, remote sensing, Landsat, MOLUSCE, artificial neural network, cellular automata, support vector machine, GIS, peri-urban agriculture, food security