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The ATLAS sensor onboard the ICESat-2 satellite is a photon-counting lidar (PCL) with a primary mission to map Earth's ice sheets. A secondary goal of the mission is to provide vegetation and terrain elevations, which are essential for calculating the planet's biomass carbon reserves. A drawback of ATLAS is that the sensor does not provide reliable terrain height estimates in dense, high-closure forests because only a few photons reach the ground through the canopy and return to the detector. This low penetration translates into lower accuracy for the resultant terrain model. Tropical forest measurements with ATLAS have an additional problem estimating top of canopy because of frequent atmospheric phenomena such as fog and low clouds that can be misinterpreted as top of the canopy. To alleviate these issues, we propose using a ConvPoint neural network for 3D point clouds and high-density airborne lidar as training data to classify vegetation and terrain returns from ATLAS. The semantic segmentation network provides excellent results and could be used in parallel with the current ATL08 noise filtering algorithms, especially in areas with dense vegetation. We use high-density airborne lidar data acquired along ICESat-2 transects in Central American forests as a ground reference for training the neural network to distinguish between noise photons and photons lying between the terrain and the top of the canopy. Each photon event receives a label (noise or signal) in the test phase, providing automated noise-filtering of the ATL03 data. The terrain and top of canopy elevations are subsequently aggregated in 100 m segments using a series of iterative smoothing filters. We demonstrate improved estimates for both terrain and top of canopy elevations compared to the ATL08 100 m segment estimates. The neural network (NN) noise filtering reliably eliminated outlier top of canopy estimates caused by low clouds, and aggregated root mean square error (RMSE) decreased from 7.7 m for ATL08 to 3.7 m for NN prediction (18 test profiles aggregated). For terrain elevations, RMSE decreased from 5.2 m for ATL08 to 3.3 m for the NN prediction, compared to airborne lidar reference profiles.ICESat-2LidarPoint cloudNoise filtering 

Research at the Selin Farm site in northeastern Honduras examined changing cultural landscapes in a region whose prehistory is poorly understood. Low-impact field methods and radiocarbon dates reveal how this cultural landscape changed in response to shifting priorities among its inhabitants from a.d. 300–1000. We found evidence for rapid accumulation of deposits beginning around a.d. 600, when the site nearly doubled in size over the span of just decades, before retracting again within a few centuries. Although it was caught up in some of the broader social and political changes that began around a.d. 600 throughout northern Honduras and southern Mesoamerica, the longevity of this site suggests stability of the cultural and ecological systems in which it was embedded until the final centuries of occupation. Well-preserved, long-term deposits make Selin Farm an ideal location in which to explore entangled processes of environmental and social change in the little-known small-scale societies of Central America. 

We present results from the archaeological analysis of 331 km2 of high-resolution airborne lidar data collected in the Upper Usumacinta River basin of Mexico and Guatemala. Multiple visualizations of the DEM and multi-spectral data from four lidar transects crossing the Classic period (AD 350–900) Maya kingdoms centered on the sites of Piedras Negras, La Mar, and Lacanja Tzeltal permitted the identification of ancient settlement and associated features of agricultural infrastructure. HDBSCAN (hierarchical density-based clustering of applications with noise) cluster analysis was applied to the distribution of ancient structures to define urban, peri-urban, sub-urban, and rural settlement zones. Interpretations of these remotely sensed data are informed by decades of ground-based archaeological survey and excavations, as well as a rich historical record drawn from inscribed stone monuments. Our results demonstrate that these neighboring kingdoms in three adjacent valleys exhibit divergent patterns of structure clustering and low-density urbanism, distributions of agricultural infrastructure, and economic practices during the Classic period. Beyond meeting basic subsistence needs, agricultural production in multiple areas permitted surpluses likely for the purposes of tribute, taxation, and marketing. More broadly, this research highlights the strengths of HDBSCAN to the archaeological study of settlement distributions when compared to more commonly applied methods of density-based cluster analysis. 

We report on a large area of ancient Maya wetland field systems in Belize, Central America, based on airborne lidar survey coupled with multiple proxies and radiocarbon dates that reveal ancient field uses and chronology. The lidar survey indicated four main areas of wetland complexes, including the Birds of Paradise wetland field complex that is five times larger than earlier remote and ground survey had indicated, and revealed a previously unknown wetland field complex that is even larger. The field systems date mainly to the Maya Late and Terminal Classic (∼1,400–1,000 y ago), but with evidence from as early as the Late Preclassic (∼1,800 y ago) and as late as the Early Postclassic (∼900 y ago). Previous study showed that these were polycultural systems that grew typical ancient Maya crops including maize, arrowroot, squash, avocado, and other fruits and harvested fauna. The wetland fields were active at a time of population expansion, landscape alteration, and droughts and could have been adaptations to all of these major shifts in Maya civilization. These wetland-farming systems add to the evidence for early and extensive human impacts on the global tropics. Broader evidence suggests a wide distribution of wetland agroecosystems across the Maya Lowlands and Americas, and we hypothesize the increase of atmospheric carbon dioxide and methane from burning, preparing, and maintaining these field systems contributed to the Early Anthropocene.