With healthy populations of animals that disperse seeds, tropical forests can absorb up to four times more carbon. Deforestation, hunting, and wildlife trade threaten the hornbill's ability to disperse seeds throughout Asian tropical forests. A lot of attention has been paid to how climate change can reduce biodiversity. Now MIT researchers have shown that the reverse is also true: Loss of biodiversity can jeopardize regrowth of tropical forests, one of Earth's most powerful tools for mitigating climate change. Combining data from thousands of previous studies and using new tools for quantifying interconnected ecological processes, the researchers analyzed numerous tropical sites where deforestation was being followed by natural regrowth, focusing on the role of animals such as birds and monkeys that spread plant seeds by eating them in one place and then defecating someplace else. Evan Fricke, a research scientist in the MIT Department of Civil and Environmental Engineering and the lead author of a paper on the work, has studied such animals for 15 years, showing that without their role, trees have lower survival rates and a harder time keeping up with environmental changes.

Understanding the spatial distribution of palms within tropical forests is essential for effective ecological monitoring, conservation strategies, and the sustainable integration of natural forest products into local and global supply chains. However, the analysis of remotely sensed data in these environments faces significant challenges, such as overlapping palm and tree crowns, uneven shading across the canopy surface, and the heterogeneous nature of the forest landscapes, which often affect the performance of palm detection and segmentation algorithms. To overcome these issues, we introduce PalmDSNet, a deep learning framework for real-time detection, segmentation, and counting of canopy palms. Additionally, we employ a bimodal reproduction algorithm that simulates palm spatial propagation to further enhance the understanding of these point patterns using PalmDSNet's results. We used UAV-captured imagery to create orthomosaics from 21 sites across western Ecuadorian tropical forests, covering a gradient from the everwet Choc\'o forests near Colombia to the drier forests of southwestern Ecuador. Expert annotations were used to create a comprehensive dataset, including 7,356 bounding boxes on image patches and 7,603 palm centers across five orthomosaics, encompassing a total area of 449 hectares. By combining PalmDSNet with the bimodal reproduction algorithm, which optimizes parameters for both local and global spatial variability, we effectively simulate the spatial distribution of palms in diverse and dense tropical environments, validating its utility for advanced applications in tropical forest monitoring and remote sensing analysis.

Conserving tropical forests is highly relevant socially and ecologically because of their critical role in the global ecosystem. However, the ongoing deforestation and degradation affect millions of hectares each year, necessitating government or private initiatives to ensure effective forest monitoring. In April 2019, a project based on Citizen Science and Machine Learning models called ForestEyes (FE) was launched with the aim of providing supplementary data to assist experts from government and non-profit organizations in their deforestation monitoring efforts. Recent research has shown that labeling FE project volunteers/citizen scientists helps tailor machine learning models. In this sense, we adopt the FE project to create different sampling strategies based on the wisdom of crowds to select the most suitable samples from the training set to learn an SVM technique and obtain better classification results in deforestation detection tasks. In our experiments, we can show that our strategy based on user entropy-increasing achieved the best classification results in the deforestation detection task when compared with the random sampling strategies, as well as, reducing the convergence time of the SVM technique.

Citizen Science (CS) leverages the collective efforts of non-specialist/ordinary volunteers in different research tasks, such as collecting, analyzing, and classifying data to solve technical and scientific challenges. CS applications have attracted the attention of academic researchers due to the abundance of data created with high quality at low cost. According to an article in CERN Courier Magazine,3 CS is beneficial for the scientific community, the volunteers involved in the projects, and society as a whole. On the researcher's side, CS helps to achieve scientific data/metadata quickly, obtaining large amounts of valuable information that can contribute to advancing research.3 On the other hand, volunteers become aware of a scientific methodology, are recognized for their contributions, and feel satisfied for being part of a project with scientific and social relevance.2

It was not long before James and Skyler became close friends. The paternal-like connection between the two previous strangers surprised Acharya. James would often brag about Skyler in the mannerism of a proud father. Anchor was fond of James as well. She would run to him and leap in his arms while Data would bark frantically for her attention, hoping for those magic words: "Don't get me." After some coaxing, James agreed to fly with Skyler. Even with his limited flying time, James could tell Skyler was gifted. Moreover, Skyler enjoyed flying again since the death of his best friend. It was pouring all day on the North Shore as James and Acharya sat in James' office at the forest research center, watching the rain through the large window. The downpour was relaxing, and James loved the sound.

Logged forests cover four million square kilometres of the tropics and restoring these forests is essential if we are to avoid the worst impacts of climate change, yet monitoring recovery is challenging. Tracking the abundance of visually identifiable, early-successional species enables successional status and thereby restoration progress to be evaluated. Here we present a new pipeline, SLIC-UAV, for processing Unmanned Aerial Vehicle (UAV) imagery to map early-successional species in tropical forests. The pipeline is novel because it comprises: (a) a time-efficient approach for labelling crowns from UAV imagery; (b) machine learning of species based on spectral and textural features within individual tree crowns, and (c) automatic segmentation of orthomosaiced UAV imagery into 'superpixels', using Simple Linear Iterative Clustering (SLIC). Creating superpixels reduces the dataset's dimensionality and focuses prediction onto clusters of pixels, greatly improving accuracy. To demonstrate SLIC-UAV, support vector machines and random forests were used to predict the species of hand-labelled crowns in a restoration concession in Indonesia. Random forests were most accurate at discriminating species for whole crowns, with accuracy ranging from 79.3% when mapping five common species, to 90.5% when mapping the three most visually-distinctive species. In contrast, support vector machines proved better for labelling automatically segmented superpixels, with accuracy ranging from 74.3% to 91.7% for the same species. Models were extended to map species across 100 hectares of forest. The study demonstrates the power of SLIC-UAV for mapping characteristic early-successional tree species as an indicator of successional stage within tropical forest restoration areas. Continued effort is needed to develop easy-to-implement and low-cost technology to improve the affordability of project management.

An underreported aspect of the climate crisis is that archaeological sites, cultural landscapes, biodiversity, and distributions of flora and fauna--much of which modern people will never even know about--are disappearing at an alarming rate. I'm an archaeologist, and while I don't know how to solve the climate crisis, I do know what I want to contribute to our shared legacy: a comprehensive digital map of the surface of the planet and everything on it. Such a project will serve both as a record of the state of the planet as it exists now, to help scientists better understand how it is changing, and as a "virtual planet" that can serve as a precious gift for future generations. In June, I and other like-minded scientists launched the Earth Archive: a massive scientific effort aiming to scan the entire solid surface of the planet, starting with the areas most threatened, at a resolution smaller than a meter. This effort aims to use lidar technology, or light detection and ranging technology, which can map both the vegetation and the ground beneath it in three dimensions from the vantage point of a plane, helicopter, or drone.

Science needs to understand and predict how climate change--and the growing onslaught of hurricanes, fires, and floods it's bringing--affects tropical forests. Will the forests respond to the assault with shorter trees? Will they store less carbon, or support less tree and plant diversity and fewer wildlife species? To better understand the effects a changing climate will have on tropical forests, Maria Uriarte, Columbia University professor of ecology, evolution, and environmental biology, needs to analyze images of forests. These bird's-eye view images are the size of a postage stamp.

Science needs to understand and predict how climate change--and the growing onslaught of hurricanes, fires, and floods it's bringing--affects tropical forests. Will the forests respond to the assault with shorter trees? Will they store less carbon, or support less tree and plant diversity and fewer wildlife species? To better understand the effects a changing climate will have on tropical forests, Maria Uriarte, Columbia University professor of ecology, evolution, and environmental biology, needs to analyze images of forests. These bird's-eye view images are the size of a postage stamp.

Nonetheless, properly constructed comparisons designed to reconcile the two datasets yield up to 90% agreement (e.g., in South America). The Comment by Hansen et al. (1) provides the opportunity to distinguish our research, which quantifies dynamics in carbon density, from studies focused on the binary classification of changes in forest area (2). We use a multisensor (ICESat/MODIS), multistage approach combined with field measurements to map net change (i.e., losses and gains) in carbon density for the period 2003–2014 for each 463 m 463 m (21.4 ha) pixel in our dataset. Within each pixel, dynamic processes occurring at both the tree and stand level are necessarily considered in aggregate, meaning that losses and gains are happening always and concurrently wherever woody biomass is present. A loss is registered when losses are greater than gains, and vice versa.