High-resolution planet satellite imagery and multi-temporal surveys to predict risk of tree mortality in tropical eucalypt forestry

Enhancing spatio-temporal environmental analyses: A machine learning superpixel-based approach

The progressive evolution of the spatial and temporal resolutions of Earth observation satellites has brought multiple benefits to scientific research. The increasing volume of data with higher frequencies and spatial resolutions offers precise and timely information, making it an invaluable tool for environmental analysis and enhanced decision-making. However, this presents a formidable challenge for large-scale environmental analyses and socioeconomic applications based on spatial time series, often compelling researchers to resort to lower-resolution imagery, which can introduce uncertainty and impact results. In response to this, our key contribution is a novel machine learning approach for dense geospatial time series rooted in superpixel segmentation, which serves as a preliminary step in mitigating the high dimensionality of data in large-scale applications. This approach, while effectively reducing dimensionality, preserves valuable information to the maximum extent, thereby substantially enhancing data accuracy and subsequent environmental analyses. This method was empirically applied within the context of a comprehensive case study encompassing the 2002-2022 period with 8-d-frequency-normalized difference vegetation index data at 250-m resolution in an area spanning 43,470 km2. The efficacy of this methodology was assessed through a comparative analysis, comparing our results with those derived from 1000-m-resolution satellite data and an existing superpixel algorithm for time series data. An evaluation of the time-series deviations revealed that using coarser-resolution pixels introduced an error that exceeded that of the proposed algorithm by 25 % and that the proposed methodology outperformed other algorithms by more than 9 %. Notably, this methodological innovation concurrently facilitates the aggregation of pixels sharing similar land-cover classifications, thus mitigating subpixel heterogeneity within the dataset. Further, the proposed methodology, which is used as a preprocessing step, improves the clustering of pixels according to their time series and can enhance large-scale environmental analyses across a wide range of applications.

Scattered tree death contributes to substantial forest loss in California

In recent years, large-scale tree mortality events linked to global change have occurred around the world. Current forest monitoring methods are crucial for identifying mortality hotspots, but systematic assessments of isolated or scattered dead trees over large areas are needed to reduce uncertainty on the actual extent of tree mortality. Here, we mapped individual dead trees in California using sub-meter resolution aerial photographs from 2020 and deep learning-based dead tree detection. We identified 91.4 million dead trees over 27.8 million hectares of vegetated areas (16.7-24.7% underestimation bias when compared to field data). Among these, a total of 19.5 million dead trees appeared isolated, and 60% of all dead trees occurred in small groups ( ≤ 3 dead trees within a 30 × 30 m grid), which is largely undetected by other state-level monitoring methods. The widespread mortality of individual trees impacts the carbon budget and sequestration capacity of California forests and can be considered a threat to forest health and a fuel source for future wildfires.

Auctioning approaches for ecosystem services - Evidence and applications

Auctions have attracted growing attention as bidding mechanisms for soliciting or allocating payments for a wide range of ecosystem services (ES). This paper reviews the latest scientific knowledge on ES auctioning approaches. Using systematically selected academic articles, we trace and discuss the development of ES auction literature across space, time, target ecosystem, and mechanism type. We integrate previous attempts to organize this body of work to produce a composite factor map of entry points to more specialized sub-literatures engaging with current issues in auction design and implementation. The results show that most academic work focuses on reverse auctions, where landowners bid their willingness to accept contracts to protect or promote ES provisioning, but we also locate several forward (i.e. beneficiaries bid their willingness to pay for ES) and mixed mechanisms. We critically analyze major advantages and challenges for each approach, emphasizing issues related to transaction costs and accessibility for participants and agencies. Overall, our findings suggest that ES auctions have a robust track record but remain administratively and logistically challenging. Further investment in open-source tools, shared infrastructure, and other efforts to make auctions more accessible to researchers, agencies, and participants alike is strongly indicated.

The Interplay of the Tree and Stand-Level Processes Mediate Drought-Induced Forest Dieback: Evidence from Complementary Remote Sensing and Tree-Ring Approaches

Drought-induced forest dieback can lead to a tipping point in community dominance, but the coupled response at the tree and stand-level response has not been properly addressed. New spatially and temporally integrated monitoring approaches that target different biological organization levels are needed. Here, we compared the temporal responses of dendrochronological and spectral indices from 1984 to 2020 at both tree and stand levels, respectively, of a drought-prone Mediterranean Pinus pinea forest currently suffering strong dieback. We test the influence of climate on temporal patterns of tree radial growth, greenness and wetness spectral indices; and we address the influence of major drought episodes on resilience metrics. Tree-ring data and spectral indices followed different spatio-temporal patterns over the study period (1984–2020). Combined information from tree growth and spectral trajectories suggests that a reduction in tree density during the mid-1990s could have promoted tree growth and reduced dieback risk. Additionally, over the last decade, extreme and recurrent droughts have resulted in crown defoliation greater than 40% in most plots since 2019. We found that tree growth and the greenness spectral index were positively related to annual precipitation, while the wetness index was positively related to mean annual temperature. The response to drought, however, was stronger for tree growth than for spectral indices. Our study demonstrates the value of long-term retrospective multiscale analyses including tree and stand-level scales to disentangle mechanisms triggering and driving forest dieback.