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The LandTrendr (LT) algorithm has been used widely for analysis of change in Landsat spectral time series data, but requires significant pre-processing, data management, and computational resources, and is only accessible to the community in a proprietary programming language (IDL). Here, we introduce LT for the Google Earth Engine (GEE) platform. The GEE platform simplifies pre-processing steps, allowing focus on the translation of the core temporal segmentation algorithm. Temporal segmentation involved a series of repeated random access calls to each pixel’s time series, resulting in a set of breakpoints (“vertices”) that bound straight-line segments. The translation of the algorithm into GEE included both transliteration and code analysis, resulting in improvement and logic error fixes. At six study areas representing diverse land cover types across the U.S., we conducted a direct comparison of the new LT-GEE code against the heritage code (LT-IDL). The algorithms agreed in most cases, and where disagreements occurred, they were largely attributable to logic error fixes in the code translation process. The practical impact of these changes is minimal, as shown by an example of forest disturbance mapping. We conclude that the LT-GEE algorithm represents a faithful translation of the LT code into a platform easily accessible by the broader user community.

Illicit cattle ranching and coca farming have serious negative consequences on the Colombian Amazon’s land systems. The underlying causes of these land activities include historical processes of colonization, armed conflict, and narco-trafficking. We aim to examine how illicit cattle ranching and coca farming are driving forest cover change over the last 34 years (1985–2019). To achieve this aim, we combine two pixel-based approaches to differentiate between coca farming and cattle ranching using hypothetical observed patterns of illicit activities and a deep learning algorithm. We found evidence that cattle ranching, not coca, is the main driver of forest loss outside the legal agricultural frontier. There is evidence of a recent, explosive conversion of forests to cattle ranching outside the agricultural frontier and within protected areas since the negotiation phase of the peace agreement. In contrast, coca is remarkably persistent, suggesting that crop substitution programs have been ineffective at stopping the expansion of coca farming deeper into protected areas. Countering common narratives, we found very little evidence that coca farming precedes cattle ranching. The spatiotemporal dynamics of the expansion of illicit land uses reflect the cumulative outcome of agrarian policies, Colombia’s War on Drugs, and the 2016 peace accord. Our study enables the differentiation of illicit land activities, which can be transferred to other regions where these activities have been documented but poorly distinguished spatiotemporally. We provide an applied framework that could be used elsewhere to disentangle other illicit land uses, track their causes, and develop management options for forested land systems and people who depend on them.

Aboveground biomass (AGB) estimates derived from Landsat’s spectral bands are limited by spectral saturation when AGB densities exceed 150–300 Mg ha−1. Statistical features that characterize image texture have been proposed as a means to alleviate spectral saturation. However, apart from Gray Level Co-occurrence Matrix (GLCM) statistics, many spatial feature engineering techniques (e.g., morphological operations or edge detectors) have not been evaluated in the context of forest AGB estimation. Moreover, many prior investigations have been constrained by limited geographic domains and sample sizes. We utilize 176 lidar-derived AGB maps covering ∼9.3 million ha of forests in the Pacific Northwest of the United States to construct an expansive AGB modeling dataset that spans numerous biophysical gradients and contains AGB densities exceeding 1000 Mg ha−1. We conduct a large-scale inter-comparison of multiple spatial feature engineering techniques, including GLCMs, edge detectors, morphological operations, spatial buffers, neighborhood vectorization, and neighborhood similarity features. Our numerical experiments indicate that statistical features derived from GLCMs and spatial buffers yield the greatest improvement in AGB model performance out of the spatial feature engineering strategies considered. Including spatial features in Random Forest AGB models reduces the root mean squared error (RMSE) by 9.97 Mg ha−1. We contextualize this improvement model performance by comparing to AGB models developed with multi-temporal features derived from the LandTrendr and Continuous Change Detection and Classification algorithms. The inclusion of temporal features reduces the model RMSE by 18.41 Mg ha−1. When spatial and temporal features are both included in the model’s feature set, the RMSE decreases by 21.71 Mg ha−1. We conclude that spatial feature engineering strategies can yield nominal gains in model performance. However, this improvement came at the cost of increased model prediction bias.

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, long-term trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

Fire is an important disturbance in many forest landscapes, but there is heightened concern regarding recent wildfire activity in western North America. Several regional‐scale studies focus on high‐severity fire, but a comprehensive examination at all levels of burn severity (i.e., low, moderate, and high) is needed to inform our understanding of the ecological effects of contemporary fires and how they vary among vegetation zones at sub‐regional scales. We integrate Landsat time series data with field measurements of tree mortality to map burn severity in forests of the Pacific Northwest, USA, from 1985 to 2010. We then examine temporal trends in fire extent and spatial patterns of burn severity in relation to drought and annual fire extent. Finally, we compare results among vegetation zones and with expectations based on studies of historical landscape dynamics and fire regimes. Small increases in fire extent over time were associated with drought in all vegetation zones, but fire cumulatively affected <3% of wet vegetation zones, and most dry vegetation zones experienced less fire than expectations from fire history studies. Although the proportion of fire at any level of severity did not increase over time, temporal trends toward larger patches of high‐severity fire were related to drought and annual fire extent, depending on vegetation zone. In vegetation zones with historically high‐severity regimes, high‐severity fire accounted for a large proportion of recent fire extent (43–48%) and occurred primarily in patches ≥100 ha. In vegetation zones with historically low‐ and mixed‐severity regimes, low (45–54%)‐ and moderate‐severity (24–36%) fires were prevalent, but proportions of high‐severity fire (23–26%), almost half of which occurred in patches ≥100 ha, were much greater than expectations from most fire history studies. Our results support concerns about large patches of high‐severity fire in some dry forests but also suggest that spatial patterns of burn severity across much of the extent burned are generally consistent with current understanding of historical landscape dynamics in the region. This study highlights the importance of considering the ecological effects of fire at all levels of severity in management and policy initiatives intended to promote forest biodiversity and resilience to future fire activity.

This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R2 = 0.8, RMSE = 115 Mg ha−1, Bias = 2 Mg ha−1). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R2 = 0.8, RMSE = 152 Mg ha−1, Bias = 9 Mg ha−1), including higher AGB values (>400 Mg ha−1) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders. Social media abstract Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps.

Glaciers are important sentinels of a changing climate, crucial components of the global cryosphere and integral to their local landscapes. However, many of the commonly used methods for mapping glacier change are labor-intensive and limit the temporal and spatial scope of existing research. This study addresses some of the limitations of prior approaches by developing a novel deep-learning-based method called GlacierCoverNet. GlacierCoverNet is a deep neural network that relies on an extensive, purpose-built training dataset. Using this model, we created a record of over three decades long at a fine temporal cadence (every two years) for the state of Alaska. We conducted a robust error analysis of this dataset and then used the dataset to characterize changes in debris-free glaciers and supraglacial debris over the last ~35 years. We found that our deep learning model could produce maps comparable to existing approaches in the capture of areal extent, but without manual editing required. The model captured the area covered with glaciers that was ~97% of the Randolph Glacier Inventory 6.0 with ~6% and ~9% omission and commission rates in the southern portion of Alaska, respectively. The overall model area capture was lower and omission and commission rates were significantly higher in the northern Brooks Range. Overall, the glacier-covered area retreated by 8425 km2 (−13%) between 1985 and 2020, and supraglacial debris expanded by 2799 km2 (64%) during the same period across the state of Alaska.

Understanding the causes and consequences of rapid environmental change is an essential scientific frontier, particularly given the threat of climate- and land use-induced changes in disturbance regimes. In western North America, recent widespread insect outbreaks and wildfires have sparked acute concerns about potential insect-fire interactions. Although previous research shows that insect activity typically does not increase wildfire likelihood, key uncertainties remain regarding insect effects on wildfire severity (i.e., ecological impact). Recent assessments indicate that outbreak severity and burn severity are not strongly associated, but these studies have been limited to specific insect or fire events. Here, we present a regional census of large wildfire severity following outbreaks of two prevalent bark beetle and defoliator species, mountain pine beetle (Dendroctonus ponderosae) and western spruce budworm (Choristoneura freemani), across the US Pacific Northwest. We first quantify insect effects on burn severity with spatial modeling at the fire event scale and then evaluate how these effects vary across the full population of insect-fire events (n = 81 spanning 1987-2011). In contrast to common assumptions of positive feedbacks, we find that insects generally reduce the severity of subsequent wildfires. Specific effects vary with insect type and timing, but both insects decrease the abundance of live vegetation susceptible to wildfire at multiple time lags. By dampening subsequent burn severity, native insects could buffer rather than exacerbate fire regime changes expected due to land use and climate change. In light of these findings, we recommend a precautionary approach when designing and implementing forest management policies intended to reduce wildfire hazard and increase resilience to global change.

The fate of live forest biomass is largely controlled by growth and disturbance processes, both natural and anthropogenic. Thus, biomass monitoring strategies must characterize both the biomass of the forests at a given point in time and the dynamic processes that change it. Here, we describe and test an empirical monitoring system designed to meet those needs. Our system uses a mix of field data, statistical modeling, remotely-sensed time-series imagery, and small-footprint lidar data to build and evaluate maps of forest biomass. It ascribes biomass change to specific change agents, and attempts to capture the impact of uncertainty in methodology. We find that: * A common image framework for biomass estimation and for change detection allows for consistent comparison of both state and change processes controlling biomass dynamics. * Regional estimates of total biomass agree well with those from plot data alone. * The system tracks biomass densities up to 450-500 Mg ha−1 with little bias, but begins underestimating true biomass as densities increase further. * Scale considerations are important. Estimates at the 30 m grain size are noisy, but agreement at broad scales is good. Further investigation to determine the appropriate scales is underway. * Uncertainty from methodological choices is evident, but much smaller than uncertainty based on choice of allometric equation used to estimate biomass from tree data. * In this forest-dominated study area, growth and loss processes largely balance in most years, with loss processes dominated by human removal through harvest. In years with substantial fire activity, however, overall biomass loss greatly outpaces growth. Taken together, our methods represent a unique combination of elements foundational to an operational landscape-scale forest biomass monitoring program.

In the Willcox Groundwater Basin (WGB), increasing rates of agricultural groundwater withdrawal have led to significant regional groundwater level decline, threatening the basin’s long-term water resource security. Updated characterization of the basin’s water resource conditions and agricultural water demand is critically important for informing groundwater resource management efforts. We developed the hydrogeologic framework of the WGB and linked groundwater level data with land cover classification data to provide a spatiotemporal assessment of water resource conditions and agricultural development in the WGB. A correlation analysis evaluated the degree of association between the basin’s mean annual depth-to-groundwater and agricultural land cover extent. Results of this study indicate that between 2008 and 2021, agricultural land cover in the WGB increased by 29%. The average rate of groundwater level change in the basin’s measured wells was calculated at −13.8 m between 2006 and 2021. We found a strong correlation between the basin’s mean annual measured depth-to-groundwater and the annual agricultural land cover extent, further reinforcing the understanding of agricultural water use in the basin as a principal driver of groundwater level decline. The methodological framework employed proved a simple and effective way to assess groundwater resources as influenced by geology and land use. The outcomes of this study provide critical information toward improved water resources management by providing an integrated understanding of local hydrogeology, groundwater level variability, and changes in agricultural land cover in arid inland basins such as those found in Arizona, USA.