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NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Tropical peatlands in Southeast Asia have experienced widespread subsidence due to forest clearance and drainage for agriculture, oil palm and pulp wood production, causing concerns about their function as a long-term carbon store. Peatland drainage leads to subsidence (lowering of peatland surface), an indicator of degraded peatlands, while stability/uplift indicates peatland accumulation and ecosystem health. We used the Advanced Pixel System using the Intermittent SBAS (ASPIS-DInSAR) technique with biophysical and geographical data to investigate the impact of peatland drainage and agriculture on spatial patterns of subsidence in Selangor, Malaysia. Results showed pronounced subsidence in areas subjected to drainage for agricultural and oil palm plantations, while stable areas were associated with intact forests. The most powerful predictors of subsidence rates were the distance from the drainage canal or peat boundary; however, other drivers such as soil properties and water table levels were also important. The maximum subsidence rate detected was lower than that documented by ground-based methods. Therefore, whilst the APSIS-DInSAR technique may underestimate absolute subsidence rates, it gives valuable information on the direction of motion and spatial variability of subsidence. The study confirms widespread and severe peatland degradation in Selangor, highlighting the value of DInSAR for identifying priority zones for restoration and emphasising the need for conservation and restoration efforts to preserve Selangor peatlands and prevent further environmental impacts. [ABSTRACT FROM AUTHOR]

The new Domestic Workers Dataset is the largest single set of surveys (n = 11,759) of domestic workers to date. Our analysis of this dataset reveals features about the lives and work of this "hard-to-find" population in India—a country estimated to have the largest number of people living in forms of contemporary slavery (11 million). The data allow us to identify child labour, indicators of forced labour, and patterns of exploitation—including labour paid below the minimum wage—using bivariate analysis, factor analysis, and spatial analysis. The dataset also helps to advance our understanding of how to measure labour exploitation and modern slavery by showing the value of "found data" and participatory and citizen science approaches. [ABSTRACT FROM AUTHOR]

Permafrost thaw in Arctic regions is increasing methane (CH4) emissions to the atmosphere but quantification of such emissions is difficult given the large and remote areas impacted. Hence, Earth Observation (EO) data are critical for assessing both permafrost thaw, associated ecosystem change, and increased CH4 emissions. Often extrapolation from field measurements using EO is the approach employed. However, there are key challenges to consider - that landscape CH4 emissions result from a complex local-scale mixture of micro-topographies and vegetation types that support widely differing CH4 emissions and the difficulty in detecting the initial stages of permafrost degradation before vegetation transitions have occurred. This study considers the use of a combination of ultra-high resolution unoccupied aerial vehicle (UAV) data, together with Sentinel-1 and -2 data to extrapolate field measurements of CH4 emissions from a set of vegetation types which capture the local variation in vegetation on degrading palsa wetlands. We show that the ultra-high resolution UAV data can map spatial variation in vegetation relevant to variation in CH4 emissions and extrapolate these across the wider landscape. We further show how Sentinel-1 and Sentinel-2 can be used. By way of a soft classification, and simple correction of misclassification bias of a hard classification, the output vegetation mapping and subsequent extrapolation of CH4 emissions matched closely that generated using the UAV data. InSAR assessment of subsidence together with the vegetation classification suggested that high subsidence rates of palsa wetland can be used to quantify areas at risk of increased CH4 emissions. We estimate that a transition of an area currently experiencing subsidence to fen type vegetation are estimated to increase emissions from 116 kg CH4 season-1 from the 50 ha study area, to emissions as high as 6500 to 13000 kg CH4 season-1. The key outcome from this study is that a fusion of EO data types provides the ability to estimate CH4 emissions from large geographies covered by a fine mixture of vegetation types and vulnerable to transitioning to CH4 emitters in the near future. This points to an opportunity to measure and monitor CH4 from the Arctic over space and time with confidence. [ABSTRACT FROM AUTHOR]

Lianas (woody vines) are a key component of tropical forests, known to reduce forest carbon storage and sequestration and to be increasing in abundance. Analysing how and why lianas are distributed in forest canopies at landscape scales will help us determine the mechanisms driving changes in lianas over time. This will improve our understanding of liana ecology and projections of tropical forest carbon storage now and into the future. Despite competing hypotheses on the mechanisms driving spatial patterning of lianas, few studies have integrated multiple tree‐level biotic and abiotic factors in an analytical framework. None have done so in the Palaeotropics, which are biogeographically and evolutionarily distinct from the Neotropics, where most research on lianas has been conducted.We used an unoccupied aerial system (UAS; drone) to assess liana load in 50‐ha of Palaeotropical forest canopy in Southeast Asia. We obtained data on hypothesised drivers of liana spatial distribution in the forest canopy, including disturbance, tree characteristics, soil chemistry and topography, from the UAS, from airborne LiDAR and from ground surveys. We integrated these in a comprehensive analytical framework to extract variables at an individual‐tree level and evaluated the relative strengths of the hypothesised drivers and their ability to predict liana distributions through boosted regression tree (BRT) modelling.Tree height and distance to canopy gaps were the two most important predictors of liana load, with relative contribution values in BRT models of 34.60%–45.39% and 7.93%–10.19%, respectively. Our results suggest that taller trees were less often and less heavily infested by lianas than shorter trees, opposite to Neotropical findings. Lianas also occurred more often, and to a greater extent, in tree crowns close to canopy gaps and to neighbouring trees with lianas in their crown.Synthesis. Despite their known importance and prevalence in tropical forests, lianas are not well understood, particularly in the Palaeotropics. Examining 2428 trees across 50‐ha of Palaeotropical forest canopy in Southeast Asia, we find support for the hypothesis that canopy gaps promote liana infestation. However, we also found that liana presence and load declined with tree height, which is opposite to well‐established Neotropical findings. This suggests a fundamental difference between Neotropical and Southeast Asian forests. Considering that most liana literature has focused on the Neotropics, this highlights the need for additional studies in other biogeographic regions to clarify potential differences and enable us to better understand liana impacts on tropical forest ecology, carbon storage and sequestration. [ABSTRACT FROM AUTHOR]