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%.

We present a deep learning-based framework for individual tree crown delineation in aerial and satellite images. This is an important task, e.g., for forest yield or carbon stock estimation. In contrast to earlier work, the presented method creates irregular polygons instead of bounding boxes and also provides a tree cover mask for areas that are not separable. Furthermore, it is trainable with low amounts of training data and does not need 3D height information from, e.g., laser sensors. We tested the approach in two scenarios: (1) with 30 cm WorldView-3 satellite imagery from an urban region in Bengaluru, India, and (2) with 5 cm aerial imagery of a densely forested area near Gartow, Germany. The intersection over union between the reference and predicted tree cover mask is 71.2% for the satellite imagery and 81.9% for the aerial images. On the polygon level, the method reaches an accuracy of 46.3% and a recall of 63.7% in the satellite images and an accuracy of 52% and recall of 66.2% in the aerial images, which is comparable to previous works that only predicted bounding boxes. Depending on the image resolution, limitations to separate individual tree crowns occur in situations where trees are hardly separable even for human image interpreters (e.g., homogeneous canopies, very small trees). The results indicate that the presented approach can efficiently delineate individual tree crowns in high-resolution optical images. Given the high availability of such imagery, the framework provides a powerful tool for tree monitoring. The source code and pretrained weights are publicly available at https://github.com/AWF-GAUG/TreeCrownDelineation.

Urbanization is a major driver of land use change and biodiversity decline. While most of the ongoing and future urbanization hotspots are located in the Global South, the impact of urban expansion on agricultural biodiversity and associated functions and services in these regions has widely been neglected. Additionally, most studies assess biodiversity responses at local scale (α‐diversity), however, ecosystem functioning is strongly determined by compositional and functional turnover of communities (β‐diversity) at regional scales. We investigated taxonomic and functional β‐diversity of farmland birds across three seasons on 36 vegetable farms spread along a continuous urbanization gradient in Bangalore, a South Indian megacity. Increasing amount of grey area in the farm surroundings was the dominant driver affecting β‐diversity and resulting in taxonomic and functional homogenization of farmland bird communities. Functional diversity losses were higher than expected from species declines (i.e., urbanization acts as an environmental filter), with particular losses of functionally important groups such as insectivores of crop pests. Moreover, urbanization reduced functional redundancy of bird communities, which may further weaken ecosystems resilience to future perturbations. Our study underscores urbanization as a major driver of taxonomic and functional homogenization of species communities in agricultural systems, potentially threatening crucial ecosystem services for food production.

Tree crown volume is a fundamental tree characteristic. It correlates to forest biomass production and most relevant ecosystem and environmental functions, such as carbon sequestration and air pollution reduction. When researching these relationships, it is necessary to clearly define and then quantify tree crown variables in a both accurate and operational manner. In this paper, we review the reported literature on the assessment of tree crown volume. First, we compile the varying definitions of crown volume and other tree crown variables that may be used as inputs to quantify crown volume. Then, we examine the data sources for quantifying these variables, including field measurements, terrestrial photographs, aerial photographs and laser scanning. Furthermore, we compare the published approaches on translating these crown variable measurements into tree crown volume. These approaches include the approximation of simple geometric solids, approaches of computational geometry and voxelization. We also compare the reported accuracies and major challenges of these approaches. From this literature review, the reader may craft a suitable approach for the assessment of crown volume.

Urbanisation is a global trend rapidly transforming the biophysical and socioeconomic structures of metropolitan areas. To better understand (and perhaps control) these processes, more interdisciplinary research must be dedicated to the rural–urban interface. This also calls for a common reference system describing intermediate stages along a rural–urban gradient. The present paper constructs a simple index of urbanisation for villages in the Greater Bangalore Area, using GIS analysis of satellite images, and combining basic measures of building density and distance. The correlation of the two parameters and discontinuities in the frequency distribution of the combined index indicate highly dynamic stages of transformation, spatially clustered in the rural–urban interface. This analysis is substantiated by a qualitative assessment of village morphologies. The index presented here serves as a starting point in a large, coordinated study of rural–urban transitions. It was used to stratify villages for random sampling in order to perform a representative socioeconomic household survey, along with agricultural experiments and environmental assessments in various subsamples. Later on, it will also provide a matrix against which the results can be aligned and evaluated. In this process, the measures and classification systems themselves can be further refined and elaborated.

IForest is an Open Access, peer-reviewed online journal published by the Italian Society of Silviculture and Forest Ecology (SISEF). The journal encompasses a broad range of research aspects concerning forest science: forest ecology, biodiversity/genetics and ecophysiology, silviculture, forest inventory and planning, forest protection and monitoring, forest harvesting, landscape ecology, forest history, wood technology. Of special interest are studies addressing the biological and ecological bases for the sustainable management of forest ecosystems.