Design and Implementation of an Interactive Web-Based Near Real-Time Forest Monitoring System

Technology-enhanced community forest management in tropical regions: A state of the art

Tropical forests provide ecosystem services to around 2.7 billion people. Yet they are reaching tipping points due to social, economic, and environmental pressures. Technology is increasingly being leveraged to expand Community Forest Management (CFM) monitoring capabilities and to potentially increase its effectiveness, but a systematic accounting of this is lacking in the scientific literature. This study employed a mixed-methods approach combining a systematic literature review (SLR) with semi-structured interviews of technology-enhanced CFM (tech-CFM) case studies in tropical forests. From the SLR, evaluation criteria were identified and applied to 23 case studies that employed one or more novel technologies, 8 on the African continent, 9 in the Asia Pacific region, 5 in Latin America, and 1 in multiple regions. The results include classifying 22 monitoring technologies, with satellite remote sensing technology being the most common (17 case studies), followed by mobile devices (10 case studies), which are often integrated with geographic information system (8 case studies) analysis and data platforms. These technologies tend to be deployed in packages that augment each technology's capabilities, beyond their individual uses. Nonetheless, they are limited by poor internet coverage in remote regions, impeding the ability to develop real-time integrated monitoring systems. Tech-CFM shows potential for complementing and integrating with national monitoring system when adequate data collection protocols are in place. Practical social-cultural, technical, and project design recommendations are made for the integration of technology into CFM. Finally, a multi-criteria decision-making framework is developed from the literature-based evaluation criteria to assist practitioners in selecting appropriate technology suites.

A novel GIS-based multicriteria analysis approach for ascertaining the catchment-scale degradation of a Himalayan wetland

Wetland degradation through a diverse spectrum of anthropogenic stressors worldwide has taken a heavy toll on the health of wetlands. This study examined the health of a semi-urban wetland Bodsar, located in the Kashmir Himalaya using multicriteria analysis approach assimilating data on land use land cover (LULC), landscape fragmentation, soil loss, and demography. Wetland and catchment-scale land system changes from 1980 to 2022 were assessed using high-resolution imagery. Fragmentation of the natural landscape was assessed using the Landscape Fragmentation Tool (LFT) and soil loss was assessed using the Revised Universal Soil Loss Equation (RUSLE). In addition, the water quality was examined at 12 sites distributed across the wetland using standard methods. Satellite data revealed 12 categories of land use with areas under exposed rock, orchards, built-up and sparse forest having increased by 1005%, 623%, 274%, and 37% respectively. LFT indicated that the core (>500 acres) and core (<250 acres) zones decreased by approximately 16% and 64%, respectively, whereas the areas under the perforated, edge and patch classes increased significantly. RUSLE estimates show a ∼77% increase in soil erosion from 116.26 Mg a^-1 in 1980 to 205.68 Mg a^-1 in 2022, mostly due to changes in LULC. Total phosphorus (0.195-2.04 mg L ^-1), nitrate nitrogen (0.306-2.79 mg L ^-1), and total dissolved solids (543-774 mg L^-1) indicated nutrient enrichment of the wetland influenced by anthropogenically-driven land system changes. The wetland degradation index revealed that 21% of the wetland experienced high-to-severe degradation, 62% experienced moderate degradation, and 17% did not face any significant degradation pressure. The novel GIS-based approach adopted in this study can act as a prototype for ascertaining the catchment-scale degradation of wetlands worldwide.

Connecting Top-Down and Bottom-Up Approaches in Environmental Observing

Effective responses to rapid environmental change rely on observations to inform planning and decision-making. Reviewing literature from 124 programs across the globe and analyzing survey data for 30 Arctic community-based monitoring programs, we compare top-down, large-scale program driven approaches with bottom-up approaches initiated and steered at the community level. Connecting these two approaches and linking to Indigenous and local knowledge yields benefits including improved information products and enhanced observing program efficiency and sustainability. We identify core principles central to such improved links: matching observing program aims, scales, and ability to act on information; matching observing program and community priorities; fostering compatibility in observing methodology and data management; respect of Indigenous intellectual property rights and the implementation of free, prior, and informed consent; creating sufficient organizational support structures; and ensuring sustained community members’ commitment. Interventions to overcome challenges in adhering to these principles are discussed.

Research challenges and opportunities for using big data in global change biology

Global change biology has been entering a big data era due to the vast increase in availability of both environmental and biological data. Big data refers to large data volume, complex data sets, and multiple data sources. The recent use of such big data is improving our understanding of interactions between biological systems and global environmental changes. In this review, we first explore how big data has been analyzed to identify the general patterns of biological responses to global changes at scales from gene to ecosystem. After that, we investigate how observational networks and space-based big data have facilitated the discovery of emergent mechanisms and phenomena on the regional and global scales. Then, we evaluate the predictions of terrestrial biosphere under global changes by big modeling data. Finally, we introduce some methods to extract knowledge from big data, such as meta-analysis, machine learning, traceability analysis, and data assimilation. The big data has opened new research opportunities, especially for developing new data-driven theories for improving biological predictions in Earth system models, tracing global change impacts across different organismic levels, and constructing cyberinfrastructure tools to accelerate the pace of model-data integrations. These efforts will uncork the bottleneck of using big data to understand biological responses and adaptations to future global changes.

Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor

Forest cover and vegetation degradation was monitored across the Kavango-Zambezi Transfrontier Conservation Area (KAZA) in southern Africa and the performance of three different methods in detecting degradation was assessed using reference data. Breaks for Additive Season and Trend (BFAST) Monitor was used to identify potential forest cover and vegetation degradation using Landsat Normalized Difference Moisture Index (NDMI) time series data. Parametric probability-based magnitude thresholds, non-parametric random forest in conjunction with Soil-Adjusted Vegetation Index (SAVI) time series, and the combination of both methods were evaluated for their suitability to detect degradation for six land cover classes ranging from closed canopy forest to open grassland. The performance of degradation detection was largely dependent on tree cover and vegetation density. Satisfactory accuracies were obtained for closed woodland (user’s accuracy 87%, producer’s accuracy 71%) and closed forest (user’s accuracy 92%, producer’s accuracy 90%), with lower accuracies for open canopies. The performance of the three methods was more similar for closed canopies and differed for land cover classes with open canopies. Highest user’s accuracy was achieved when methods were combined, and the best performance for producer’s accuracy was obtained when random forest was used.

Tropical Protected Areas Under Increasing Threats from Climate Change and Deforestation

Identifying protected areas most susceptible to climate change and deforestation represents critical information for determining conservation investments. Development of effective landscape interventions is required to ensure the preservation and protection of these areas essential to ecosystem service provision, provide high biodiversity value, and serve a critical habitat connectivity role. We identified vulnerable protected areas in the humid tropical forest biome using climate metrics for 2050 and future deforestation risk for 2024 modeled from historical deforestation and global drivers of deforestation. Results show distinct continental and regional patterns of combined threats to protected areas. Eleven Mha (2%) of global humid tropical protected area was exposed to the highest combined threats and should be prioritized for investments in landscape interventions focused on adaptation to climate stressors. Global tropical protected area exposed to the lowest deforestation risk but highest climate risks totaled 135 Mha (26%). Thirty-five percent of South America’s protected area fell into this risk category and should be prioritized for increasing protected area size and connectivity to facilitate species movement. Global humid tropical protected area exposed to a combination of the lowest deforestation and lowest climate risks totaled 89 Mha (17%), and were disproportionately located in Africa (34%) and Asia (17%), indicating opportunities for low-risk conservation investments for improved connectivity to these potential climate refugia. This type of biome-scale, protected area analysis, combining both climate change and deforestation threats, is critical to informing policies and landscape interventions to maximize investments for environmental conservation and increase ecosystem resilience to climate change.

Using Space-Time Features to Improve Detection of Forest Disturbances from Landsat Time Series

Current research on forest change monitoring using medium spatial resolution Landsat satellite data aims for accurate and timely detection of forest disturbances. However, producing forest disturbance maps that have both high spatial and temporal accuracy is still challenging because of the trade-off between spatial and temporal accuracy. Timely detection of forest disturbance is often accompanied by many false detections, and existing approaches for reducing false detections either compromise the temporal accuracy or amplify the omission error for forest disturbances. Here, we propose to use a set of space-time features to reduce false detections. We first detect potential forest disturbances in the Landsat time series based on two consecutive negative anomalies, and subsequently use space-time features to confirm forest disturbances. A probability threshold is used to discriminate false detections from forest disturbances. We demonstrated this approach in the UNESCO Kafa Biosphere Reserve located in the southwest of Ethiopia by detecting forest disturbances between 2014 and 2016. Our results show that false detections are reduced significantly without compromising temporal accuracy. The user’s accuracy was at least 26% higher than the user’s accuracies obtained when using only temporal information (e.g., two consecutive negative anomalies) to confirm forest disturbances. We found the space-time features related to change in spatio-temporal variability, and spatio-temporal association with non-forest areas, to be the main predictors for forest disturbance. The magnitude of change and two consecutive negative anomalies, which are widely used to distinguish real changes from false detections, were not the main predictors for forest disturbance. Overall, our findings indicate that using a set of space-time features to confirm forest disturbances increases the capacity to reject many false detections, without compromising the temporal accuracy.

The feasibility of local participation in Measuring, Reporting and Verification (PMRV) for REDD

The studies in this PLOS ONE collection investigated the feasibility of community participation in Measuring, Reporting and Verifying (Participatory MRV-PMRV) initiatives in the context of national programs to reduce emissions from deforestation and forest degradation (REDD+). While such participation is desirable, its feasibility has been uncertain. This collection builds the empirical foundations for putting PMRV into practice. The authors of this article identified five crucial considerations: (1) clarify the stakeholders, (2) understand their motivation to participate, (3) integrate knowledge and information from multiple disciplines and sources, (4) convey knowledge and information across multiple levels of governance, and (5) clarify and enable the links to REDD+ safeguards. We conclude that local communities and other local actors can play a major role in achieving REDD+ MRV, however, this requires attention to their needs and motivations. Future activities should include assessment of past PMRV experiences, costs and benefits, operationalization of reporting and verification, formalization of PMRV and full scale testing on the ground.