Made 4 Geek - YouTube

My YouTube channel aims to share tutorial videos, to make it easy to understand.

YouTube

Online Course: Statistical Methods in R

The Ultimate Course to Quickly Master Statistical Methods in R - Instructor: Joachim Schork - Statistics Globe

Statistics Globe

High-resolution sensors and deep learning models for tree resource monitoring | Nature Reviews Electrical Engineering

Trees contribute to carbon dioxide absorption through biomass, regulate the climate, support biodiversity, enhance soil, air and water quality, and offer economic and health benefits. Traditionally, tree monitoring on continental and global scales has focused on forest cover, whereas assessing biomass and species diversity, as well as trees outside closed-canopy forests, has been challenging. A new generation of commercial and public satellites and sensors provide high-resolution spatial and temporal optical data that can be used to identify trees as objects. Technologies from the field of artificial intelligence, such as convolutional neural networks and vision transformers, can go beyond detecting these objects as two-dimensional representations, and support characterization of the three-dimensional structure of objects, such as canopy height and wood volume, via contextual learning from two-dimensional images. These advancements enable reliable characterization of trees, their structure, biomass and diversity both inside and outside forests. Furthermore, self-supervision and foundation models facilitate large-scale applications without requiring extensive amounts of labels. Here, we summarize these advances, highlighting their application towards consistent tree monitoring systems that can assess carbon stocks, attribute losses and gains to underlying drivers and, ultimately, contribute to climate change mitigation. Trees are crucial for Earth’s ecosystems, aiding in carbon absorption, climate regulation and biodiversity support. High-resolution satellite sensors and artificial intelligence enable detailed tree monitoring at national and continental levels, simplifying biomass assessment, national reporting and climate change mitigation efforts.

Nature

Workbook: forestry_gbq_version9

GIS Day | November 19, 2025

Celebrate GIS excellence on GIS Day. Use our free resources to plan activities that teach, motivate, and inspire others to explore all that GIS can do.

[2111.08872] TorchGeo: Deep Learning With Geospatial Data

Abstract page for arXiv paper 2111.08872: TorchGeo: Deep Learning With Geospatial Data

arXiv.org

OBSERVER: How the EU Space Programme supports conservation efforts | Copernicus

Assessing the multidimensional complexity of biodiversity using a globally standardized approach

Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon | PLOS One

Monitoring is essential to ensure that environmental goals are being achieved, including those of sustainable agriculture. Growing interest in environmental monitoring provides an opportunity to improve monitoring practices. Approaches that directly monitor land cover change and biodiversity annually by coupling the wall-to-wall coverage from remote sensing and the site-specific community composition from environmental DNA (eDNA) can provide timely, relevant results for parties interested in the success of sustainable agricultural practices. To ensure that the measured impacts are due to the environmental projects and not exogenous factors, sites where projects have been implemented should be benchmarked against counterfactuals (no project) and control (natural habitat) sites. Results can then be used to calculate diverse sets of indicators customized to monitor different projects. Here, we report on our experience developing and applying one such approach to assess the impact of shaded cocoa projects implemented by the Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA) near São Félix do Xingu, in Pará, Brazil. We used the Continuous Degradation Detection (CODED) and LandTrendr algorithms to create a remote sensing-based assessment of forest disturbance and regeneration, estimate carbon sequestration, and changes in essential habitats. We coupled these remote sensing methods with eDNA analyses using arthropod-targeted primers by collecting soil samples from intervention and counterfactual pasture field sites and a control secondary forest. We used a custom set of indicators from the pilot application of a coupled monitoring framework called TerraBio. Our results suggest that, due to IMAFLORA’s shaded cocoa projects, over 400 acres were restored in the intervention area and the community composition of arthropods in shaded cocoa is closer to second-growth forests than that of pastures. In reviewing the coupled approach, we found multiple aspects worked well, and we conclude by presenting multiple lessons learned.

SEED Index

The SEED Index can be used by any organisation to measure and report on biodiversity impacts.

SEED Biocomplexity