•Protected areas (PAs) in Tanzania are effective at preserving forest carbon stock.•A scalable method is developed to assess PAs’ efficacy in preserving forest structure.•GEDI’s 3D forest structure ...measurements can be used to support forest conservation.•Community-governed PAs had the largest positive influence on forest structure.•Small PAs are effective at preserving forest structure in well-connected PA networks.
Protected areas (PAs) serve as a critical strategy for protecting natural resources, conserving biodiversity, and mitigating climate change. While there is a critical need to guide area-based conservation efforts, a systematic assessment of PA effectiveness for storing carbon stocks has not been possible due to the lack of globally consistent forest biomass data. In this study, we present a new methodology utilizing forest structural information and aboveground biomass density (AGBD) obtained from the Global Ecosystem Dynamics Investigation (GEDI) mission. We compare PAs with similar, unprotected forests obtained through statistical matching to assess differences in carbon storage and forest structure. We also assess matching outcomes for a robust and minimally biased way to quantify PA efficacy. We find that all analyzed PAs in Tanzania possess higher biomass densities than their unprotected counterfactuals (24.4% higher on average). This is also true for other forest structure metrics, including tree height, canopy cover, and plant area index (PAI). We also find that community-governed PAs are the most effective category of PAs at preserving forest structure and AGBD – often outperforming those managed by international or national entities. In addition, PAs designated under more than one entity perform better than the PAs with a single designation, especially those with multiple international designations. Finally, our findings suggest that smaller PAs may be more effective for conservation, depending on levels of connectivity. Taken together, these findings support the designation of PAs as an effective means for forest management with considerable potential to protect forest ecosystems and achieve long-term climate goals.
Deforestation continues across the tropics at alarming rates, with repercussions for ecosystem processes, carbon storage and long term sustainability. Taking advantage of recent fine-scale ...measurement of deforestation, this analysis aims to improve our understanding of the scale of deforestation drivers in the tropics. We examined trends in forest clearings of different sizes from 2000-2012 by country, region and development level. As tropical deforestation increased from approximately 6900 kha yr−1 in the first half of the study period, to >7900 kha yr−1 in the second half of the study period, >50% of this increase was attributable to the proliferation of medium and large clearings (>10 ha). This trend was most pronounced in Southeast Asia and in South America. Outside of Brazil >60% of the observed increase in deforestation in South America was due to an upsurge in medium- and large-scale clearings; Brazil had a divergent trend of decreasing deforestation, >90% of which was attributable to a reduction in medium and large clearings. The emerging prominence of large-scale drivers of forest loss in many regions and countries suggests the growing need for policy interventions which target industrial-scale agricultural commodity producers. The experience in Brazil suggests that there are promising policy solutions to mitigate large-scale deforestation, but that these policy initiatives do not adequately address small-scale drivers. By providing up-to-date and spatially explicit information on the scale of deforestation, and the trends in these patterns over time, this study contributes valuable information for monitoring, and designing effective interventions to address deforestation.
Globally, trees are increasingly dying from extreme drought, a trend that is expected to increase with climate change. Loss of trees has significant ecological, biophysical, and biogeochemical ...consequences. In 2011, a record drought caused widespread tree mortality in Texas. Using remotely sensed imagery, we quantified canopy loss during and after the drought across the state at 30‐m spatial resolution, from the eastern pine/hardwood forests to the western shrublands, a region that includes the boundaries of many species ranges. Canopy loss observations in ~200 multitemporal fine‐scale orthophotos (1‐m) were used to train coarser Landsat imagery (30‐m) to create 30‐m binary statewide canopy loss maps. We found that canopy loss occurred across all major ecoregions of Texas, with an average loss of 9.5%. The drought had the highest impact in post oak woodlands, pinyon‐juniper shrublands and Ashe juniper woodlands. Focusing on a 100‐km by ~1,000‐km transect spanning the State's fivefold east–west precipitation gradient (~1,500 to ~300 mm), we compared spatially explicit 2011 climatic anomalies to our canopy loss maps. Much of the canopy loss occurred in areas that passed specific climatic thresholds: warm season anomalies in mean temperature (+1.6°C) and vapor pressure deficit (VPD, +0.66 kPa), annual percent deviation in precipitation (−38%), and 2011 difference between precipitation and potential evapotranspiration (−1,206 mm). Although similarly low precipitation occurred during the landmark 1950s drought, the VPD and temperature anomalies observed in 2011 were even greater. Furthermore, future climate data under the representative concentration pathway 8.5 trajectory project that average values will surpass the 2011 VPD anomaly during the 2070–2099 period and the temperature anomaly during the 2040–2099 period. Identifying vulnerable ecological systems to drought stress and climate thresholds associated with canopy loss will aid in predicting how forests will respond to a changing climate and how ecological landscapes will change in the near term.
Using remotely sensed imagery, we quantified canopy loss across Texas due to the 2011 drought; we found a 9.5% loss in canopy and greater mortality of Juniperus ashei, an encroaching tree‐shrub, which suggests that woody‐shrub encroachment may be drought‐limited. Temperature (+1.6°C) and vapor pressure deficit (VPD, +0.66 kPa) anomaly thresholds effectively explained spatial patterns of tree mortality. Furthermore, future climate data (RCP 8.5) project that average values will surpass this VPD anomaly during the 2070–2099 period.
Programs which intend to maintain or enhance carbon (C) stocks in natural ecosystems are promising, but require detailed and spatially explicit C distribution models to monitor the effectiveness of ...management interventions. Savanna ecosystems are significant components of the global C cycle, covering about one fifth of the global land mass, but they have received less attention in C monitoring protocols. Our goal was to estimate C storage across a broad savanna ecosystem using field surveys and freely available satellite images. We first mapped tree canopies at 2.5m resolution with a spatial subset of high resolution panchromatic images to then predict regional wall-to-wall tree percent cover using 30-m Landsat imagery and the Random Forests algorithms. We found that a model with summer and winter spectral indices from Landsat, climate and topography performed best. Using a linear relationship between C and % tree cover, we then predicted tree C stocks across the gradient of tree cover, explaining 87% of the variability. The spatially explicit validation of the tree C model with field-measured C-stocks revealed an RMSE of 8.2tC/ha which represented ~30% of the mean C stock for areas with tree cover, comparable with studies based on more advanced remote sensing methods, such as LiDAR and RADAR. Sample spatial distribution highly affected the performance of the RF models in predicting tree cover, raising concerns regarding the predictive capabilities of the model in areas for which training data is not present. The 50,000km2 has ~41Tg C, which could be released to the atmosphere if agricultural pressure intensifies in this semiarid savanna. In this study, we demonstrated the benefit of using high resolution imagery for regional tree cover and C analysis, increasing available training data when there is paucity of field data.
•We used high resolution spatial imagery to train regional tree cover models.•Used Random Forests algorithms and remotely sensed data to scale estimates•Assessed effect of sampling size and spatial distribution on model performance•Obtained regional tree carbon estimates with a mean error of ~30%.•This method could be used to monitor tree C stocks in savannas at low costs.
Monitoring progress towards the 2030 Development Agenda requires the combination of traditional and new data sources in innovative workflows to maximize the generation of relevant information. We ...present the results of a participatory and data-driven land degradation assessment process at a national scale, which includes use of earth observation (EO) data, cloud computing, and expert knowledge for Argentina. Six different primary productivity trend maps were produced from a time series of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset (2000–2018), including the most widely used trajectory approach and five alternative methods, which include information on the timing and magnitude of the changes. To identify the land productivity trend map which best represented ground conditions, an online application was developed, allowing 190 experts to choose the most representative result for their region of expertise nationwide. Additionally, the ability to detect decreases in land productivity of each method was assessed in 43,614 plots where deforestation had been recorded. The widely used trajectory indicator was the one selected by most experts as better reflecting changes in land condition. When comparing indicators’ performance to identify deforestation-driven reductions in productivity, the Step-Wise Approach Trend Index (SWATI), which integrates short- and long-term trends, was the one which performed the best. On average, decreases of land productivity indicate that 20% of the Argentine territory has experienced degradation processes between 2000 and 2018. The participatory data generation and verification workflow developed and tested here represents an innovative low cost, simple, and fast way to validate maps of vegetation trends and other EO-derived indicators, supporting the monitoring of progress towards land degradation neutrality by 2030.
Changes in habitat use over the course of a biological invasion may influence the fraction of the landscape that is ultimately affected by the invader. However, this intermediate stage of invasion ...has been less studied than the initial or final stages. Here, we investigated the recent invasion by an ecosystem engineer, the American Beaver (Castor canadensis), in an area of the Patagonian steppe. We utilized repeated high resolution satellite images to identify beaver ponds, and used them to study changes in beaver abundance and habitat use over time. The number of beaver ponds increased 85 % between 2005 and 2014. During this period, beavers changed their habitat selection pattern, presumably as a response to increased density. Beavers established on small watercourses in canyons first, but as more canyons became occupied over time, beavers moved to less preferred watercourses in plains and U-shaped valleys. Potential new beaver colonies established close to existing beaver ponds, suggesting proximity to a beaver pond is an important determinant of beaver colonization. Identifying habitat preferred by beavers in the steppe, could help to increase early detection of the invader at the invasion front. Our work highlights the importance of the use of high resolution remote sensing technologies to better understand and control biological invasions.
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.
Woody plant-cover dynamics can alter the provisioning of ecosystem services that humans rely on. However, our understanding of such dynamics today is often limited by the availability of reliable and ...detailed land-cover information in the past, before the onset of remote sensing technologies. In this study, we carefully extracted information from historical maps of the Caldenal savannas of central Argentina in the 1880s to generate a woody cover map that we compared to a 2000s dataset. Over about the last 120 years, woody cover increased across approximately 12,200 km²(14.2% of the area). During the same period, about 5,000 km²of the original woody area was converted to croplands and around 7,000 km²to pastures, about the same total land area as was affected by encroachment. A smaller area, fine-scale analysis between the 1960s and the 2000s revealed that tree cover increased overall by 27%, shifting from open savannas to a mosaic of dense woodlands along with additional agricultural clearings. Statistical models indicate that woody cover dynamics in this region were affected by a combination of environmental and human factors. Over about the last 120 years, increases in woody plant cover have stored significant amounts of C (95.9 TgC), but not enough to compensate for losses from conversions to croplands and pastures (166.7 TgC), generating a regional net loss of 70.9 TgC. C losses could be even larger in the future if, as predicted, energy crops such as switchgrass, would trigger a new land-cover change phase in this region.
Despite global recognition of the social, economic and ecological impacts of deforestation, the world is losing forests at an alarming rate. Global and regional efforts by policymakers and donors to ...reduce deforestation need science-driven information on where forest loss is happening, and where it may happen in the future. We used spatially-explicit globally-consistent variables and global historical tree cover and loss to analyze how global- and regional-scale variables contributed to historical tree cover loss and to model future risks of tree cover loss, based on a business-as-usual scenario. Our results show that (1) some biomes have higher risk of tree cover loss than others; (2) variables related to tree cover loss at the global scale differ from those at the regional scale; and (3) variables related to tree cover loss vary by continent. By mapping both tree cover loss risk and potential future tree cover loss, we aim to provide decision makers and donors with multiple outputs to improve targeting of forest conservation investments. By making the outputs readily accessible, we anticipate they will be used in other modeling analyses, conservation planning exercises, and prioritization activities aimed at conserving forests to meet national and global climate mitigation targets and biodiversity goals.
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