An R-based geospatial analysis workflow for mapping and monitoring ash dieback disease (Hymenoscyphus fraxineus) impacts on forest stands. This repository contains reproducible code for processing field survey data, creating stem distribution maps, and analyzing spatial patterns of disease progression in ash tree populations. The tool integrates forest inventory methods with spatial analysis techniques to support forest health monitoring and management decisions in response to this invasive fungal pathogen affecting European ash forests.

The mapping of ecosystem dynamics of wetland landscapes within conservation areas tends to rely solely on remote sensing studies. This approach may inadvertently neglect the perspectives and adaptations of local inhabitants on the ground, including individuals such as local leaders, gatekeepers, migrant groups, and remote communities.] [In our investigation of the endorheic Lake Chilwa basin, an ecologically productive watershed characterized by rapid fluctuations and high population density, we utilized a socio-ecological system (SES) framework. This framework, conducted over two stages of analysis, facilitated the integration of time-series multispectral data with ethnographic and historical insights, which thereby enabled the generation of a refined mapping of the lake's ecosystem services at both regional and local scales. In stage one, a remote sensing analysis was designed to evaluate various water-extraction indices derived from Landsat collections in distinguishing among three cover classes: open water, flooded vegetation, and non-flooded grassland. Model diagnostics with subclass precision metrics were employed to assess the performances of multiple datasets, including the Multispectral Scanner (MSS1-4), Thematic Mapper (TM5), Enhanced Thematic Mapper Plus (ETM+7), and the Operational Land Imager dataset (OLI8).] [In stage two, multiple data collection approaches were utilized to comprehensively capture the social aspects of the landscape's ecosystem dynamics. These approaches included key informant interviews, focus group discussions, and rapid participatory appraisals.] [Additionally, Sentinel-1 InSAR processing was integrated with the Landsat time series data (1994-2015) using spectral mixture analysis and soft classification techniques, with particular focus on migrant fishing communities and seasonal resource use patterns. Results reveal significant spatiotemporal variations in water levels and surface area, with major recession events documented in historical records (1879, 1900, 1914-15, 1922, 1931-32, 1934, 1954, 1960-61, 1967, 1973, 1995, 2012).] [By adopting a locally grounded approach to ecosystem mapping that integrates both biophysical and social dimensions, our research sheds light on several key questions related to the spatiotemporal details of fishing regulations and the lake's seasonal and peak patterns. Additionally, our findings offer methodological insights into the enforcement and monitoring operations of specific provisions across distinct territories outlined in the lake management plan. The SES methodology, in this case, targeted to the Lake Chilwa context, serves more generally as a guidepost for future conservation initiatives, delivering a nuanced understanding aligned with the diverse perceptions, practices, and expectations of local communities. By advocating for conservation actions tailored to the local scale, our research advocates for an approach that is more detailed and responsive in safeguarding these diverse and fluctuating landscapes. This, in turn, strengthens the adaptability of regional conservation programs in response to the ever-changing global context.

This comprehensive R-based Monte Carlo simulation framework quantifies uncertainty in REDD+ emission factors and activity data for ART-TREES Standard V2.0 compliance, featuring allometric modeling, biomass estimation uncertainty, cross-validation techniques, and automated uncertainty deduction calculations with 90% confidence intervals for jurisdictional and nested carbon verification projects.

We apply the “Height Variation Hypothesis” and associated methods to estimate tree height heterogeneity (MacArthur and MacArthu, 1961). Due to varied factors regarding yield class, soil moisture, browsing, species diversity, stocking density, line of sight and clinometer errors, tree height variability presents a key challenge to upholding accuracy targets in forest inventory operations. The following tool aims to assist inventory crews to identify areas of high tree height heterogeneity, where there may be need for an increase in number of sample plots or for a decrease in the size of sampling units. Using LiDAR derived tree metrics, we classify forest areas according simple standard deviation values of tree height to produce maps of Height Heterogeneity Areas, or HHA’s.