Forest Types Research Articles

The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current systems. First, alerts often require multiple observations to confirm deforestation, which does not help enforcement if deforestation events are short-lived. Second, the methods can be computationally intensive and often focus on presenting binary detection alerts. Third, results are often reported as static accuracies or temporal lags, with relatively little discussion about how either could be improved for different use-cases. We therefore propose a novel, multisource NRT monitoring algorithm that addresses these issues by quickly achieving high-accuracy detections, using simple data harmonization to combine data sources while yielding probabilities of deforestation, and can be customized to meet management goals where users can explicitly change the algorithm to prioritize latency or accuracy in their detections based on their chosen data inputs. To demonstrate the algorithm's potential, we used combined Landsat-8, Sentinel-2, and Sentinel-1 SAR data in northern Myanmar to monitor areas with contrasting forest types. When testing all possible parameterizations, we achieved comparable results to previous studies, with median false positive rates and false negative rates under 10% and 25%, respectively, and median F1 scores consistently above 75% across the sensor combinations. Median post-data acquisition detection times were faster than other studies and ranged from 2 to 8 days, though the majority were between 2 and 5 days. In attempting to optimize the algorithm for our demonstrated region, we discovered there was no optimal parameterization that simultaneously provided the best accuracy and detection. In addition, we found that including Sentinel-1 data did not improve latency or accuracy, most likely due to relatively high levels of noise in our monsoonal study system that experienced high seasonal precipitation variation. We finally chose an example parameterization to create daily landscape maps of deforestation at 10 m resolution in two regions within the study area (∼60,000 ha). We assert this new, customizable method with the quantification of accuracy and latency trade-offs represents a clear advance for NRT change detection and can improve current operational systems.

The high mountain forests on Earth are characterized by sharp environmental heterogeneity, high species endemism, and unique phenotypic adaptations. Yet, global warming is jeopardizing this ecosystem as no other as some forests may have nowhere to go beyond the mountain’s summits. One of the most human-endangered high-elevation ecosystems is the Polylepis forest. Despite its vulnerability, forecasting climate reactions in this distinctive high mountain forest type remains a formidable challenge. Therefore, in this study, we modeled climate change responses of high-elevation allopatric Polylepis sericea and P. quadrijuga (Rosaceae) in the northern Andes. The analysis took into account VIF-prioritized bioclimatic variables for near-to-present and future (2081–2100 MPI-ESM1-2-HR) conditions. Model selection was carried out following the AUC scores of 12 MaxEnt and six machine learning algorithms. Predictive power reached 0.97 for MaxEnt’s model fc.H_rm.1 (H-1). Results indicate a moderate vulnerability of P. sericea, with a 29% loss of area in the trailing edge, due to climate change for the period 2081–2100. On the other hand, P. quadrijuga is likely to experience even larger distribution losses, up to 99%, for the same period. The findings of this study suggest that P. quadrijuga, as compared to P. sericea, exhibits a restricted ability to maintain the corresponding habitat requirements in the face of climatic change. Higher niche specialization of P. quadrijuga in the environmentally heterogeneous Eastern Cordillera contrasts with the more generalist nature of P. sericea in the topographically less complex Central Cordillera. In addition to climate change, this trend may be exacerbated by the detrimental effects of agriculture, mining, and an expanding rural population, which represent significant human-driven pressures yet to be modeled in the northern Andean highlands. Yet, based on previous studies, the historical population dynamics during the past glacial cycles suggests that range shifts could play a more significant role at deeper time scales than previously forecasted in the species’ reaction to climate change. Additionally, Polylepis forests may be able to endure at the current locations via local adaptation and plasticity, major drivers of the phenotypic variation in long-lived trees, counteracting the vulnerability scenario under a niche conservatism hypothesis. Ultimately, the modeling procedure employed in this study provides insights into the potential effects of climate change on Polylepis forests 70 years from now. Oncoming studies may consider alternative responses inherent to the gene pool of the species and the interaction with edaphic and biotic agents. We advocate for the application of comparable estimations in other high-elevation tree communities found at the tree line.

Forest Types Research Articles

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