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Chile is a global biodiversity hotspot and hosts a large proportion of the southern hemisphere’s temperate forests. The Chilean Valdivian temperate forest is a vulnerable ecosystem containing a highly ecologically valuable species assemblage. Productive forest plantations have involved deforestation of, and alien species introduction into, this ecosystem. This process has already severely impacted the western part of Chile (the Coastal Range) and is now occurring in the eastern part (the Andes), with forestry plantations promoted by government subsidies between 1974 and 2012. Archive Landsat satellite imagery classification and Google Earth Engine are used to assess land cover change over a 31-year period with a focus on alien species (Pinaceae and Eucalyptus spp.) spread and native (Nothofagus spp. and Araucaria araucana) deforestation. Results show a clear land cover pattern based on elevation: a higher altitude, relatively undisturbed area dominated by native forest (the Andes), and a lower altitude area where most human activity and related land covers are located (the valley area). The valley is highly dynamic because of constant land cover change due to forestry. Overall, Araucaria araucana cover has decreased over the study period, while Nothofagus spp. has remained relatively stable. Alien Pinaceae has decreased, while Eucalyptus spp. has remained stable. However, the results indicate that change analysis over long periods conceal dynamism. For example, Eucalyptus spp. sharply decreased between the 1980s and the 1990s and surged afterwards. Also, even though Nothofagus spp. cover dominates throughout the study period, change analysis shows a high degree of change in the valley area, indicating newly established Nothofagus spp. patches. Over the study period, long rotation Pinaceae plantations for timber have given way to shorter rotation forestry (alien Eucalyptus spp., native Nothofagus spp.) for pulp and local uses resulting from discontinuation of forest

Anopheles mosquitoes are the vectors of human malaria, a disease responsible for a significant burden of global disease and over half a million deaths in 2020. Here, methods using a time series of cost-free Earth Observation (EO) data, 45,844 in situ mosquito monitoring captures, and the cloud processing platform Google Earth Engine are developed to identify the biogeographical variables driving the abundance and distribution of three malaria vectors—Anopheles gambiae s.l., An. funestus, and An. paludis—in two highly endemic areas in the Democratic Republic of the Congo. EO-derived topographical and time series land surface temperature and rainfall data sets are analysed using Random Forests (RFs) to identify their relative importance in relation to the abundance of the three mosquito species, and they show how spatial and temporal distributions vary by site, by mosquito species, and by month. The observed relationships differed between species and study areas, with the overall number of biogeographical variables identified as important in relation to species abundance, being 30 for An. gambiae s.l. and An. funestus and 26 for An. paludis. Results indicate rainfall and land surface temperature to consistently be the variables of highest importance, with higher rainfall resulting in greater mosquito abundance through the creation of pools acting as mosquito larval habitats; however, proportional coverage of forest and grassland, as well as proximity to forests, are also consistently identified as important. Predictive application of the RF models generated monthly abundance maps for each species, identifying both spatial and temporal hot-spots of high abundance and, by proxy, increased malaria infection risk. Results indicate greater temporal variability in An. gambiae s.l. and An. paludis abundances in response to seasonal rainfall, whereas An. funestus is generally more temporally stable, with maximum predicted abundances of 122 for An. gambiae s.l., 283 for An. fu

Tropical peatlands such as Ghana’s Greater Amanzule peatland are highly valuable ecosystems and under great pressure from anthropogenic land use activities. Accurate measurement of their occurrence and extent is required to facilitate sustainable management. A key challenge, however, is the high cloud cover in the tropics that limits optical remote sensing data acquisition. In this work we combine optical imagery with radar and elevation data to optimise land cover classification for the Greater Amanzule tropical peatland. Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission (SRTM) imagery were acquired and integrated to drive a machine learning land cover classification using a random forest classifier. Recursive feature elimination was used to optimize high-dimensional and correlated feature space and determine the optimal features for the classification. Six datasets were compared, comprising different combinations of optical, radar and elevation features. Results showed that the best overall accuracy (OA) was found for the integrated Sentinel-2, Sentinel-1 and SRTM dataset (S2+S1+DEM), significantly outperforming all the other classifications with an OA of 94%. Assessment of the sensitivity of land cover classes to image features indicated that elevation and the original Sentinel-1 bands contributed the most to separating tropical peatlands from other land cover types. The integration of more features and the removal of redundant features systematically increased classification accuracy. We estimate Ghana’s Greater Amanzule peatland covers 60,187 ha. Our proposed methodological framework contributes a robust workflow for accurate and detailed landscape-scale monitoring of tropical peatlands, while our findings provide timely information critical for the sustainable management of the Greater Amanzule peatland.

Urbanization is a global phenomenon, but its negative effects are most pronounced in developing countries. While much urbanization in the global South is unplanned, there have been occasional attempts at strategic, large-scale urban planning. One example is Abuja, Nigeria, a new city with origins in a 1970s Master Plan. Here, we use multi-temporal remote sensing to investigate four decades of urbanization in Abuja, showing the extent to which urban development has matched original intentions. Seven Landsat images from 1975 to 2014 were selected to correspond with Master Plan milestones and turning points in Nigeria’s socio-political development. Land cover classification and change detection results show built-up land increasing rapidly, from 1,167 ha in 1975 to 18,623 ha in 2014, mostly converted from grassland, often via a pioneer bare soil class. Comparing image analysis against the Master Plan shows that, in the early years, Abuja’s development matched broad planning intentions fairly closely. Later, though, unplanned development proliferated, and the city’s resemblance to the Master Plan diminished progressively. Level of adherence to the Master Plan varied widely according to the system of government. Notably, after long-term military rule was replaced by a democratic government around the turn of the millennium, unplanned development increased sharply.

Savannah grazing lawns are a key food resource for large herbivores such as blue wildebeest (Connochaetes taurinus), hippopotamus (Hippopotamus amphibius) and white rhino (Ceratotherium simum), and impact herbivore densities, movement and recruitment rates. They also exert a strong influence on fire behaviour including frequency, intensity and spread. Thus, variation in grazing lawn cover can have a profound impact on broader savannah ecosystem dynamics. However, knowledge of their present cover and distribution is limited. Importantly, we lack a robust, broad-scale approach for detecting and monitoring grazing lawns, which is critical to enhancing understanding of the ecology of these vital grassland systems. We selected two sites in the Lower Sabie and Satara regions of Kruger National Park, South Africa with mesic and semiarid conditions, respectively. Using spectral and texture features derived from WorldView-3 imagery, we (i) parameterised and assessed the quality of Random Forest (RF), Support Vector Machines (SVM), Classification and Regression Trees (CART) and Multilayer Perceptron (MLP) models for general discrimination of plant functional types (PFTs) within a sub-area of the Lower Sabie landscape, and (ii) compared model performance for probabilistic mapping of grazing lawns in the broader Lower Sabie and Satara landscapes. Further, we used spatial metrics to analyse spatial patterns in grazing lawn distribution in both landscapes along a gradient of distance from waterbodies. All machine learning models achieved high F-scores (F1) and overall accuracy (OA) scores in general savannah PFTs classification, with RF (F1 = 95.73 0.004%, OA = 94.16 0.004%), SVM (F1 = 95.64 0.002%, OA = 94.02 0.002% ) and MLP (F1 = 95.71 0.003%, OA = 94.27 0.003% ) forming a cluster of the better performing models and marginally outperforming CART (F1 = 92.74 0.006%, OA = 90.93 0.003%). Grazing lawn

Chile’s temperate forest is a global biodiversity hotspot. An upsurge in alien forest plantations has disturbed and fragmented the landscape, promoting biological invasions. The invasion process is not fully understood since monitoring large and inaccessible areas can be prohibitively expensive and logistically challenging using field-based methods alone. Here, a remote sensing approach using Sentinel-2 satellite imagery, fragmentation analysis, and random forest modelling is applied to detect alien tree stands and describe their extent in relation to fragmentation and landscape structure in study areas around Malalcahuello National Reserve and Villarrica National Park. Detailed vegetation maps are produced, with classification accuracies >81% and including four forest classes, two native and two alien. An altitudinal pattern was observed in both sites. At lower altitudes, there was greater total area covered by alien trees and more fragmented native forests than at higher altitudes. However, Villarrica had less alien tree cover than Malalcahuello, but was a more fragmented landscape. Random forest modelling identified that alien pine tree mean patch area was positively correlated with both land cover diversity and Araucaria araucana forest mean patch area in both sites. Given their conservation and cultural relevance, the locations of protected areas need reconsidering to strengthen the protection of A. araucana, which could be outcompeted by alien trees in a context of increasing productive forestry. This is especially urgent in Villarrica, where protected areas already have a substantial presence of alien trees, with most A. araucana found outside protected areas.

Distinguishing between natural forests from exotic tree plantations is essential to get an accurate picture of the world's state of forests. Most exotic tree plantations support lower levels of biodiversity and have less potential for ecosystem services supply than natural forests, and differencing them is still a challenge using standard tools. We use a novel approach in south-central of Chile to differentiate tree cover dynamics among natural forests and exotic tree plantations. Chile has one of the world's most competitive forestry industry and the region is a global biodiversity hotspot. Our collaborative visual interpretation method combined a global database of tree cover change, remote sensing from high-resolution satellite images and expert knowledge. By distinguishing exotic tree plantation and natural forest loss, we fit spatially explicit models to estimate tree-cover loss across 40 millions of ha between 2000 and 2016. We were able to distinguish natural forests from exotic tree plantations with an overall accuracy of 99% and predicted forest loss. Total tree cover loss was continuous over time, and the disaggregation revealed that 1 549 909 ha of tree plantations were lost (mean = 96 869 ha year−1), while 206 142 ha corresponded to natural forest loss (mean = 12 884 ha year−1). Mostly of tree plantations lost returned to be plantation (51%). Natural forests were converted mainly (75%) to transitional land covers (e.g. shrubland, bare land, grassland), and an important proportion of these may finish as tree plantation. This replacement may undermine objectives of increasedcarbon storage and biodiversity. Tree planting as a solution has gained increased attention in recen years with ambitious commitments to mitigate the effects of climate change. However, negative outcomes for the environment could result if strategies incentivize the replacement of natural forests into other land covers. Initiatives to reduce carbon emissions should encourage differentia

Chile’s temperate forest is a global biodiversity hotspot. An upsurge in alien forest plantations has disturbed and fragmented the landscape, promoting biological invasions. The invasion process is not fully understood since monitoring large and inaccessible areas can be prohibitively expensive and logistically challenging using field-based methods alone. Here, a remote sensing approach using Sentinel-2 satellite imagery, fragmentation analysis, and random forest modelling is applied to detect alien tree stands and describe their extent in relation to fragmentation and landscape structure in study areas around Malalcahuello National Reserve and Villarrica National Park. Detailed vegetation maps are produced, with classification accuracies >81% and including four forest classes, two native and two alien. An altitudinal pattern was observed in both sites. At lower altitudes, there was greater total area covered by alien trees and more fragmented native forests than at higher altitudes. However, Villarrica had less alien tree cover than Malalcahuello, but was a more fragmented landscape. Random forest modelling identified that alien pine tree mean patch area was positively correlated with both land cover diversity and Araucaria araucana forest mean patch area in both sites. Given their conservation and cultural relevance, the locations of protected areas need reconsidering to strengthen the protection of A. araucana, which could be outcompeted by alien trees in a context of increasing productive forestry. This is especially urgent in Villarrica, where protected areas already have a substantial presence of alien trees, with most A. araucana found outside protected areas.

The rise of palm oil as the world’s most consumed vegetable oil has coincided with exponential growth in palm oil research activity. Bibliometric analysis of research outputs reveals a distinct imbalance in the type of research being undertaken, notably a disproportionate focus on biofuel and engineering topics. Recognising the expansion of oil palm agriculture across the tropics and the increasing awareness of environmental, social and economic impacts, we seek to re-orient the existing research agenda towards one that addresses the most fundamental and urgent questions defined by the palm oil stakeholder community. Following consultation with 659 stakeholders from 38 countries, including palm oil growers, government agencies, non-governmental organisations and researchers, the highest priority research questions were identified within 13 themes. The resulting 279 questions, including 26 ranked as top priority, reveal a diversity of environmental and social research challenges facing the industry, ranging from the ecological and ecosystem impacts of production, to the livelihoods of plantation workers and smallholder communities. Analysis of the knowledge type produced from these questions underscores a clear need for fundamental science programmes, and studies that involve the consultation of non-academic stakeholders to develop ‘transformative’ solutions to the oil palm sector. Stakeholders were most aligned in their choice of priority questions across the themes of policy and certification related themes, and differed the most in environmental feedback, technology and smallholder related themes. Our recommendations include improved regional academic leadership and coordination, greater engagement with private and public stakeholders of Africa, and Central and South America, and enhanced collaborative efforts with researchers in the major consuming countries of India and China.

Tropical deforestation remains one of the major global challenges of the 21st Century driven to a large extent by the conversion of land for agricultural purposes, such as palm oil production. Malaysia is one of the world’s largest palm oil producers and has seen widespread conversion to oil palm from primary forest, including peat swamp forest (PSF). This study investigates the rate and extent of pervasive oil palm expansion in and around North Selangor Peat Swamp Forest (NSPSF) over the last three decades, exploring how land conversion has affected the region’s tropical forests, and assessing the relative success of PSF conservation measures. Time-series Landsat imagery was used to assess thematic land cover change and improvement in vegetation condition since NSPSF was given protected status in 1990. The results show a near tripling in oil palm cover throughout North Selangor, from 24,930 ha in 1989 to 70,070 ha in 2016; while at the same time tropical forest cover shrank from 145,570 ha to 88,400 ha. Despite concerns over the sustainability and environmental impact of such rapid oil palm conversion at a regional level, at the local scale NSPSF represents a relative conservation success story. Effective land stewardship by government and non-governmental organisation (NGO) management actors has limited illegal encroachment of oil palm around the reserve boundary. PSF rehabilitation measures have also markedly improved vegetation condition in NSPFS’s interior. These findings have broad significance for how oil palm agriculture is managed and especially for PSF stewardship and conservation, and the approaches described here may be usefully adopted elsewhere in Southeast Asia and around the world.

Accurate assessment of tropical peatland carbon dynamics is important to (a) determine the size of the active carbon pool, (b) estimate the scale of transfers of peat-derived greenhouse gases (GHGs) to the atmosphere resulting from land use change, and (c) support carbon emissions reduction policies. To date, information on the quality of tropical peatland organic matter and its sensitivity to increases in global temperatures is limited, particularly in the context of land conversion. The aim of this work is therefore to determine peat quality and temperature response of potential GHG emissions under flooded conditions from tropical peatland sites. Whilst reflecting the process of conversion from forest to oil palm plantation. Four land use types that represent the stages of conversion from peat swamp forest to oil palm were chosen: (i) secondary ‘forest’, (ii) recently ‘drained’ but not cleared forest (iii) cleared and recently planted ‘young oil palm’ plantation and (iv) ‘mature oil palm’ plantation. Overall, surface peat carbon was more labile than deeper peats. The largest labile pool measured at forest sites. In the later stages of land conversion, the labile carbon had been lost and the relative abundance of recalcitrant organic material increased. Potential GHG fluxes were greatest in surface peats compared to deeper peats and declined as labile carbon was depleted following land conversion. Higher temperatures resulted in higher potential GHG emissions at all stages of conversion, but the magnitude of the temperature response depended on organic matter lability. For CO2 fluxes, the temperature response was most pronounced at forest sites. This reflects the greater peat lability at this land use. In contrast, for CH4 emissions, there were increased emissions both at forest and converted land types with higher temperatures. This suggests that increasing temperatures in response to climate warming may drive higher CH4 emissions from sites dominated by degraded