Historical meteorological data is necessary for modeling climate scenarios, yet there remain significant challenges in archiving, extracting relevant data, organization and provision. Data rescue efforts play an important role in collecting relevant information, yet the real challenge remains in the implementation of accurate and efficient methods for extracting information from historical handwritten tabular records which are largely recorded in handwritten logbooks. The diverse tabular structures, layouts, and writing styles in these documents, along with the accessibility and varying quality of source preservation, make it impractical to rely on a single solution. In an effort to fill this gap, we propose a human-in-the-loop workflow with benchmarks of open- and closed-source methods for extracting and processing handwritten tabular data. We explain through the case of climatological data from the Congo region (1907–1960) how HIL workflows can be implemented and their trade-offs for implementing solely computationally steered models. In addition, we outline how generalized large Vision Language models have changed how HIL workflows can be implemented as to increase accuracy and precision not only with semi-automatic solutions for data provision but also prompt engineering and the necessary (historical) context to achieve optimal results.

In the context of global climate change, understanding forest dynamics and formulating sustainable forest development strategies require accurate quantification of forest structural changes over time. However, the majority of studies have focused on exploring forest structure at one specific time point, lacking repeated observations of forest structure. This limitation hinders the comprehensive understanding of structural dynamics in forests, particularly its link with changes in forest ecosystem functionality. To address this issue, this study proposes a method for quantifying forest structural dynamics using bi-temporal terrestrial laser scanning (TLS) data. We utilized bi-temporal TLS data in combination with a quantitative structure model (QSM) algorithm, to reconstruct 3D models of individual trees. Subsequently, tree parameters including diameter at breast height (DBH), tree height, crown projection area (CPA), crown volume (CV) and biomass were extracted. By quantifying the changes in these parameters and analyzing their relationships with the 3D spatial structure of trees, a comprehensive analysis was conducted to quantitatively assess forest dynamics. The results indicate that bi-temporal TLS data possesses unprecedented advantages and tremendous potential in quantifying forest dynamics.

In the past five years, an international team has been working towards improved detection, characterization and modeling of abrupt changes in dryland ecosystem functioning, EF. This paper collects the recent advances and lessons learnt from the U-TURN project (Belspo SR/00/339, SR/00/366). Specifically abrupt changes in EF were mapped and categorized over global drylands; new 30m resolution time series of land cover maps and cover fractions were created, validated and released open access for the Sahel region; and new physically-based insights into dryland vegetation response to extreme rainfall were derived based on dryland optimized LPJ-GUESS simulations.

Despite the relevance of historical land cover maps for scientists and policy makers, an accurate high resolution record is currently lacking over the Sudano-Sahel. In this study, 30m resolution historically consistent land cover and cover fraction maps are provided over the Sudano-Sahel for the period 1986–2015. These land cover/cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification/regression model, while historical consistency is achieved using the hidden Markov model. Using these historical maps, a multitude of variability in the dynamic Sudano-Sahel region over the past 30 years is revealed. These include cropland expansion and the re-greening of the Sahel, forest degradation & the detection of fine-scale changes, such as smallholder or subsistence farming. The historical land cover / cover fraction maps are made available via an open-access platform.

Obtaining accurate and detailed structural forest information has been revolutionized with the emergence of laser scanning. The sampling limitations and potential of the different laser scanning platforms (e.g. TLS, UAV -LS) have, however, not been fully explored for dense tropical forests. We fused laser scanning data from the terrestrial (TLS) and drone (UA V -LS) platform for two dense tropical forest plots and calculated their vertical point density profiles to gain insight in their sampling abilities. Our results reveal the limitations of TLS to fully sample the top of the canopy of a dense tropical rainforest. We also demonstrate how multiple returns but also cheaper single returns UAV -LS systems can be applied to sample the forest structure.

Drylands are considered to be hotspots for climate change impacts and the Sahel in particular has been the subject of several ecological studies. Using dynamic vegetation models we aim to quantify the vulnerability of woody vegetation to climatic variability and soil properties. In this study we present the first results of our vegetation model optimization for a focus area centered on Senegal. We activated a shrub plant functional type (PFT) in the model and by comparing model simulations against Landsat cover fractions and MODIS leaf area index (LAI) data, we constrained two model parameters related to shrub rooting depth and maximum evapotranspiration rate. We found that the model is able to reproduce the observed cover fractions and LAI reasonably well, given that the model is restricted to simulating potential vegetation cover.

Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package.

Increased liana abundance results in reduced tree growth and increased tree mortality in tropical forest. The impact of lianas on forest-wide carbon storage has been a special interest for many researchers. The vertical and horizontal spatial distribution of lianas in tropical forest will determine the interaction with trees and the forest carbon cycle. In this study, we will introduce an algorithm to extract lianas from terrestrial laser scanning (TLS) data of a tropical forest. We developed a classification method for separating liana points from other points in a point cloud under canopy. We used a Random Forests machine learning algorithm for the classification of liana points from the other points. The leaf-wood and liana-tree classification accuracies are 90.69% and 94.42%, respectively. The results show the potential of TLS data for analysis the spatial distribution of lianas in forest stands and we explore the potential of extracting lianas from TLS point clouds.