Oil and coconut palm trees are important crops in many tropical countries, which are either planted as plantations or scattered in the landscape. Monitoring in terms of counting provides useful information for various stakeholders. Most of the existing monitoring methods are based on spectral profiles or simple neural networks and either fall short in terms of accuracy or speed. We use a neural network of the U-Net type in order to detect oil and coconut palms on very high resolution satellite images. The method is applied to two different study areas: (1) large monoculture oil palm plantations in Jambi, Indonesia, and (2) coconut palms in the Bengaluru Metropolitan Region in India. The results show that the proposed method reaches a performance comparable to state of the art approaches, while being about one order of magnitude faster. We reach a maximum throughput of 235 ha/s with a spatial image resolution of 40 cm. The proposed method proves to be reliable even under difficult conditions, such as shadows or urban areas, and can easily be transferred from one region to another. The method detected palms with accuracies between 89% and 92%.

The difficulty of defining and quantifying forest degradation is a major constraint in the implementation of the international mitigation mechanism Reduced Emissions from Deforestation and Forest Degradation (REDD+). Our aim is to develop an operational framework for defining and quantifying forest degradation at a local level for early REDD+ projects and for national REDD+ programmes, through a ground level approach. We critically review and discuss national and international definitions of forest and of forest degradation, and then analyze the main difficulties in making these operational, evaluating the key elements and threshold values that are used, and contextualizing them using Mexico as a case study. We conclude that, given the lack of historical biomass data and the limited capability for monitoring degradation using remote sensing, forest degradation is best measured against a local benchmark that represents areas of low or no degradation that have comparable biophysical characteristics. Use of benchmarks of this type could offer a quick-start option for local assessment and construction of reference levels for forest degradation. These could be refined as more data become available and could eventually be integrated into national monitoring systems.

Trees are natural capital assets, especially for cities, as they provide immense environmental benefits and improve urban biodiversity and ecology. However, urbanization has largely destroyed the original native ecosystems and has caused a homogenization where frequently native species are replaced by non-native species. When attempting to understand the role of trees in urban settings, it is important to generate science-based data on the spatial distribution of trees, their species composition and tree species diversity as a function of the degree of urbanization. Such information may specifically inform the planning of effective long-term management of trees across urban and rural gradients. A total of 23 of 1 ha each were surveyed along a Northern research transect laid out along the urban–rural gradient of the metropolitan area of Bengaluru, India. Plots were randomly selected from the stratum “settlement areas”, where WorldView-3 imagery supported both stratification and plot selection. The plots were fully mapped for trees, where a total of eleven variables had been observed for each tree. In addition, the basal area and wood volume was calculated to understand the biomass potential of the trees in the plots. The diversity indices such as the Shannon index, Simpson index, Pielou’s evenness and Margalef’s richness were considered for comparing the species diversity, composition and distribution along the gradient of Bengaluru. A total of 1128 individuals of 93 tree species were recorded. Among 92 species identified along the northern gradient, 53 are exotic, and 39 are native species. The Shannon–Wiener index varied from 1.33 to 2.72; Simpson’s index varied from 0.65 to 0.90; Pielou’s index varied from 0.66 to 0.90, and Margalef’s index ranged from 1.41 to 5.20 along the gradient. The basal area increased from 96.39 m² to 102.76 m² from 2017 to 2019 along the transect, with a net gain of 6.37 m². Similarly, the wood volume increased from 1819.57 m³ to 1926.23 m³ with a net gain of 106.66 m³. The present study reports on tree distribution, species composition and tree species diversity along a gradient from the city center to the rural surroundings of northern parts of Bengaluru city. The information generated may support the city planners/administrators by providing a holistic understanding of the species composition and abundance for a further selection of adaptive species and appropriate tree and vegetation management practices to conserve the existing green spaces and contribute towards sustainable urban planning. The sample plots laid out may also serve as permanent observation plots for monitoring the dynamics of tree cover in the city.