The research, published in the journal Environmental Monitoring and Assessment, was led by Farzaneh Razmju of the University of Aberdeen together with colleagues in the United Kingdom, Hong Kong, India, Afghanistan and the United States. The team set out to answer a deceptively simple question: has tree cover on agricultural land in eastern Afghanistan changed over a period spanning one of the most dramatic political transitions in the country’s recent history? The study window, from 2016 to 2024, encompasses the collapse of the Afghan republican government in August 2021, the withdrawal of much international development assistance, and the restructuring of the institutions that had previously supported rural livelihoods and tree planting programmes.

To track change on the ground without setting foot in the field, the researchers turned to satellites. They used imagery from PlanetScope, a commercial constellation of small satellites that images nearly the entire land surface of the Earth at a resolution of 3 metres per pixel. That level of detail is fine enough to distinguish individual orchards, scattered farm trees and field boundaries, which is essential when the objects of interest are small patches of woody vegetation embedded in a mosaic of crops, settlements and river corridors. Scenes were selected from the mid-to-late growing season, in August and September, when orchard crops such as grapes and pomegranates have fully developed canopies and the spectral signatures of cropland, tree cover and other land classes are most clearly separated.

The analytical pipeline combined object-based image analysis with a machine learning classifier. Rather than classifying each pixel independently, which tends to produce speckled salt-and-pepper maps in high-resolution imagery, the team first segmented each image into homogeneous objects using the Segment Mean Shift algorithm, applying identical parameters across all four districts and all three study years. A support vector machine classifier, chosen for its robustness when training data are limited and landscapes are heterogeneous, then assigned each object to one of five classes: tree cover, cropland, water, built-up areas and barren land. The classifier’s two key hyperparameters, C and gamma, were optimised automatically through a two-dimensional grid search for each district-year combination, allowing the model to adapt to local spectral conditions.

Accuracy was assessed rigorously. For each classified map, 500 stratified random validation points were compared against reference data derived from visual interpretation of high-resolution historical imagery in Google Earth Pro, following established good practices in remote sensing. Every map achieved a Cohen’s kappa coefficient above 0.80, the threshold generally considered to indicate strong to almost perfect agreement with reference data. The highest value, 0.93, was recorded in Dehbala in 2020, while the lowest, 0.80, was recorded in Dara Noor in the same year. Post-classification change detection and transition matrices then quantified exactly which land classes had converted into which, and Sankey diagrams, in which flow widths are proportional to converted area, made the magnitude and direction of those transitions visually intuitive.

The headline result is unambiguous: tree cover increased in all four districts. The study focused on Dara Noor, Nazyan, Dehbala and Pachir Aw Agam, four predominantly mountainous districts whose cultivated inter-montane valleys collectively represent the irrigated, rain-fed and mixed cropland-orchard farming systems typical of eastern Afghanistan. In 2016, Dehbala held the largest tree cover extent at 216.96 hectares, or 10.21 percent of the analysed area, while Nazyan and Dara Noor started from very low baselines of around 3 to 4.6 percent. By 2024, tree cover had expanded everywhere, but the pace and pattern varied considerably. Nazyan recorded the most dramatic relative increase, with tree cover rising from 3.10 percent of the analysed area in 2016 to 17.03 percent in 2024, while Dara Noor showed a gradual but steady climb and Dehbala and Pachir Aw Agam displayed more moderate, fluctuating trends.

The transition analysis revealed where the new trees came from. Cropland was by far the most persistent land class throughout the study period, and built-up areas and water bodies also showed high stability. The dominant dynamic pathway was the direct conversion of cropland to tree cover, with smaller contributions from barren land and minimal transitions from built-up areas. Notably, the magnitude of cropland-to-tree conversion was more pronounced in the 2020 to 2024 sub-period than in the years before, indicating increased land cover dynamism in the later period, precisely the years following the political transition of 2021. This timing is intriguing, because it coincides with the withdrawal of many donor-supported agricultural development programmes, including the National Horticulture and Livestock Project, which had promoted orchard establishment, fruit tree sapling distribution and farmer training during the Republic period from 2012 to 2021.

What explains the greening of Afghan farmland amid national deforestation? The authors are careful to lay out several plausible mechanisms without claiming that any single one is confirmed. Deliberate tree planting by farmers, encouraged by the legacy of pre-2021 government and donor programmes promoting fruit trees, poplar and other multipurpose species, is one candidate. Natural regeneration of woody vegetation on land where cultivation has been reduced or temporarily abandoned is another; such processes are well documented in conflict-affected settings, meaning that cropland-to-tree transitions do not necessarily signal active agroforestry adoption. The satellite data alone cannot distinguish between newly established orchards and passive regrowth, and the relative contributions of these mechanisms remain an open question that would require field-based validation, which is difficult and dangerous in insecure environments.

The Nazyan district illustrates both the promise and the puzzle of the findings. Between 2016 and 2024, tree cover there rose from 3.10 to 17.03 percent of the analysed area, while the area classified as water increased from 9.75 to 21.29 percent, from 40.26 to 87.96 hectares, and cropland declined substantially from 50.88 to 32.77 percent. These concurrent changes point to a substantial reorganisation of land cover, but the study lacked the hydrological, precipitation and irrigation data needed to determine whether the expansion of mapped water was connected to the expansion of trees. Visual inspection of the high-resolution imagery shows both scattered trees within and along field boundaries and larger patches consistent with orchard systems, but the imagery cannot reliably separate deliberate planting from natural recovery.

Whatever the mechanism, the practical significance is considerable. Trees on farms provide fruit, fuelwood, fodder, shade and income, reduce soil erosion, enhance water retention and improve soil fertility, and can buffer remaining forest fragments while contributing to carbon storage. In a country where fuelwood scarcity remains a persistent challenge for rural households and where forests in the eastern provinces have suffered severe losses from illegal logging and conflict-related disturbance, farm-grown tree products can substitute for resources that would otherwise be extracted from natural forests. Studies from other regions, including Indonesia, have found that farmers practising agroforestry are less involved in forest clearing and forest product collection than those relying on swidden agriculture, lending weight to the hypothesis that Afghanistan’s on-farm tree expansion partly reflects greater reliance on farm-based resources, although the authors stress that this remains a hypothesis requiring household-level investigation.

The study also carries a methodological message for the world’s fragile states. Ground-based monitoring, reporting and verification of agroforestry investments are often impractical where security concerns and mountainous terrain block access, as demonstrated by evidence from Tigray in northern Ethiopia, where armed conflict was associated with vegetation degradation and disrupted restoration efforts. High-resolution satellite platforms such as PlanetScope, Sentinel-2 and Landsat offer consistent, repeatable and objective data even where researchers cannot go, and this study shows that they can be extended beyond natural forests to the trees outside forests that sustain rural livelihoods. The authors caution that their results describe medium-term change within four delineated agricultural areas and should not be generalised to all of Afghanistan, and that future work integrating field observations, household surveys and multi-season imagery will be needed to determine whether the observed gains represent a persistent trajectory or a shorter-term fluctuation. But as a demonstration that substantial tree cover change can occur within agricultural landscapes even amid continued landscape-level deforestation, the findings offer a rare note of measured optimism for post-conflict environmental recovery.

Subject of Research: Tree cover change on agricultural land in conflict-affected eastern Afghanistan monitored with high-resolution satellite imagery

Article Title: Tree cover increases on agricultural land amid continued landscape-level deforestation: a case study from eastern Afghanistan

Article References: Razmju, F., Gui, B., Sam, L., Bhardwaj, A., Haider, M. W., Khurram, S., Groninger, J. W., & Burslem, D. F. R. P. (2026). Tree cover increases on agricultural land amid continued landscape-level deforestation: a case study from eastern Afghanistan. Environmental Monitoring and Assessment, 198(11), Article 1203. https://doi.org/10.1007/s10661-026-16018-1

Image Credits: AI Generated

DOI: 10.1007/s10661-026-16018-1

Keywords: Afghanistan, tree cover, agroforestry, remote sensing, land use change, PlanetScope, Nangarhar, conflict, deforestation, satellite imagery, object-based image analysis, support vector machine