Jennifer J. Swenson

Reference scenarios for deforestation and forest degradation in support of REDD: a review of data and methods

Global climate policy initiatives are now being proposed to compensate tropical forest nations for reducing carbon emissions from deforestation and forest degradation (REDD). These proposals have the potential to include developing countries more actively in international greenhouse gas mitigation and to address a substantial share of the worlds emissions which come from tropical deforestation. For such a policy to be viable it must have a credible benchmark against which emissions reduction can be calculated. This benchmark, sometimes termed a baseline or reference emissions scenario, can be based directly on historical emissions or can use historical emissions as input for business as usual projections. Here, we review existing data and methods that could be used to measure historical deforestation and forest degradation reference scenarios including FAO (Food and Agricultural Organization of the United Nations) national statistics and various remote sensing sources. The freely available and corrected global Landsat imagery for 1990, 2000 and soon to come for 2005 may be the best primary data source for most developing countries with other coarser resolution high frequency or radar data as a valuable complement for addressing problems with cloud cover and for distinguishing larger scale degradation. While sampling of imagery has been effectively useful for pan-tropical and continental estimates of deforestation, wall-to-wall (or full coverage) allows more detailed assessments for measuring national-level reference emissions. It is possible to measure historical deforestation with sufficient certainty for determining reference emissions, but there must be continued calls at the international level for making high-resolution imagery available, and for financial and technical assistance to help countries determine credible reference scenarios. The data available for past years may not be sufficient for assessing all forms of forest degradation, but new data sources will have greater potential in 2007 and after. This paper focuses only on the methods for measuring changes in forest area, but this information must be coupled with estimates of change in forest carbon stocks in order to quantify emissions from deforestation and forest degradation.

Gold Mining in the Peruvian Amazon: Global Prices, Deforestation, and Mercury Imports

Many factors such as poverty, ineffective institutions and environmental regulations may prevent developing countries from managing how natural resources are extracted to meet a strong market demand. Extraction for some resources has reached such proportions that evidence is measurable from space. We present recent evidence of the global demand for a single commodity and the ecosystem destruction resulting from commodity extraction, recorded by satellites for one of the most biodiverse areas of the world. We find that since 2003, recent mining deforestation in Madre de Dios, Peru is increasing nonlinearly alongside a constant annual rate of increase in international gold price (∼18%/yr). We detect that the new pattern of mining deforestation (1915 ha/year, 2006–2009) is outpacing that of nearby settlement deforestation. We show that gold price is linked with exponential increases in Peruvian national mercury imports over time (R2 = 0.93, p = 0.04, 2003–2009). Given the past rates of increase we predict that mercury imports may more than double for 2011 (∼500 t/year). Virtually all of Perus mercury imports are used in artisanal gold mining. Much of the mining increase is unregulated/artisanal in nature, lacking environmental impact analysis or miner education. As a result, large quantities of mercury are being released into the atmosphere, sediments and waterways. Other developing countries endowed with gold deposits are likely experiencing similar environmental destruction in response to recent record high gold prices. The increasing availability of satellite imagery ought to evoke further studies linking economic variables with land use and cover changes on the ground.

Global satellite monitoring of climate-induced vegetation disturbances

Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide.

Plant and animal endemism in the eastern Andean slope: challenges to conservation

BackgroundThe Andes-Amazon basin of Peru and Bolivia is one of the most data-poor, biologically rich, and rapidly changing areas of the world. Conservation scientists agree that this area hosts extremely high endemism, perhaps the highest in the world, yet we know little about the geographic distributions of these species and ecosystems within country boundaries. To address this need, we have developed conservation data on endemic biodiversity (~800 species of birds, mammals, amphibians, and plants) and terrestrial ecological systems (~90; groups of vegetation communities resulting from the action of ecological processes, substrates, and/or environmental gradients) with which we conduct a fine scale conservation prioritization across the Amazon watershed of Peru and Bolivia. We modelled the geographic distributions of 435 endemic plants and all 347 endemic vertebrate species, from existing museum and herbaria specimens at a regional conservation practitioners scale (1:250,000-1:1,000,000), based on the best available tools and geographic data. We mapped ecological systems, endemic species concentrations, and irreplaceable areas with respect to national level protected areas.ResultsWe found that sizes of endemic species distributions ranged widely (< 20 km2 to > 200,000 km2) across the study area. Bird and mammal endemic species richness was greatest within a narrow 2500-3000 m elevation band along the length of the Andes Mountains. Endemic amphibian richness was highest at 1000-1500 m elevation and concentrated in the southern half of the study area. Geographical distribution of plant endemism was highly taxon-dependent. Irreplaceable areas, defined as locations with the highest number of species with narrow ranges, overlapped slightly with areas of high endemism, yet generally exhibited unique patterns across the study area by species group. We found that many endemic species and ecological systems are lacking national-level protection; a third of endemic species have distributions completely outside of national protected areas. Protected areas cover only 20% of areas of high endemism and 20% of irreplaceable areas. Almost 40% of the 91 ecological systems are in serious need of protection (= < 2% of their ranges protected).ConclusionsWe identify for the first time, areas of high endemic species concentrations and high irreplaceability that have only been roughly indicated in the past at the continental scale. We conclude that new complementary protected areas are needed to safeguard these endemics and ecosystems. An expansion in protected areas will be challenged by geographically isolated micro-endemics, varied endemic patterns among taxa, increasing deforestation, resource extraction, and changes in climate. Relying on pre-existing collections, publically accessible datasets and tools, this working framework is exportable to other regions plagued by incomplete conservation data.

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

Satellite measurements of surface water offer promise for understanding wetland habitat availability at broad spatial and temporal scales; reliable habitat is crucial for the persistence of migratory shorebirds that depend on wetland networks. We analyzed water extent dynamics within wetland habitats at a globally important shorebird stopover site for a 1983-2015 Landsat time series, and evaluated the effect of climate on water extent. A range of methods can detect open water from imagery, including supervised classification approaches and thresholds for spectral bands and indices. Thresholds provide a time advantage; however, there is no universally superior index, nor single best threshold for all instances. We used random forest to model the presence or absence of water from >6200 reference pixels, and derived an optimal water probability threshold for our study area using receiver operating characteristic curves. An optimized mid-infrared (1.5-1.7 μm) threshold identified open water in the Sacramento Valley of California at 30-m resolution with an average of 90% producers accuracy, comparable to approaches that require more intensive user input. SLC-off Landsat 7 imagery was integrated by applying a customized interpolation that mapped water in missing data gaps with 99% users accuracy. On average we detected open water on ~26000 ha (~3% of the study area) in early April at the peak of shorebird migration, while water extent increased five-fold after the migration rush. Over the last three decades, late March water extent declined by ~1300 ha per year, primarily due to changes in the extent and timing of agricultural flood-irrigation. Water within shorebird habitats was significantly associated with an index of water availability at the peak of migration. Our approach can be used to optimize thresholds for time series analysis and near-real-time mapping in other regions, and requires only marginally more time than generating a confusion matrix.

Estimating Above-Ground Carbon Biomass in a Newly Restored Coastal Plain Wetland Using Remote Sensing

Developing accurate but inexpensive methods for estimating above-ground carbon biomass is an important technical challenge that must be overcome before a carbon offset market can be successfully implemented in the United States. Previous studies have shown that LiDAR (light detection and ranging) is well-suited for modeling above-ground biomass in mature forests; however, there has been little previous research on the ability of LiDAR to model above-ground biomass in areas with young, aggrading vegetation. This study compared the abilities of discrete-return LiDAR and high resolution optical imagery to model above-ground carbon biomass at a young restored forested wetland site in eastern North Carolina. We found that the optical imagery model explained more of the observed variation in carbon biomass than the LiDAR model (adj-R2 values of 0.34 and 0.18 respectively; root mean squared errors of 0.14 Mg C/ha and 0.17 Mg C/ha respectively). Optical imagery was also better able to predict high and low biomass extremes than the LiDAR model. Combining both the optical and LiDAR improved upon the optical model but only marginally (adj-R2 of 0.37). These results suggest that the ability of discrete-return LiDAR to model above-ground biomass may be rather limited in areas with young, small trees and that high spatial resolution optical imagery may be the better tool in such areas.

Rapid conservation assessment for endangered species using habitat connectivity models

To avoid extinction of rare species in regions of active environmental change, strategic approaches are needed to manage remaining habitat. When observations of dispersal or metapopulation information are not available, habitat connectivity simulations may offer a valuable alternative source of information to assess threats and evaluate conservation options. For the Critically Endangered San Martin titi monkey ( Callicebus oenanthe ) in north central Peru, an updated distribution model was generated and land cover in the heavily deforested northern range of the species was mapped. The value of remaining forest fragments was characterized and threats from future land use change were assessed using complementary connectivity models. It is estimated that the species range is less than 14 000 km 2 . Remote sensing analysis reveals that at least 34% of lowland forest in the northern range has been lost, while nearly 95% of remaining habitat fragments are likely too small to support viable populations and less than 8% of this habitat lies within conservation areas. Areas with the highest modelled connectivity comprise only 10% of the remaining forest in the northern range and small patches may contribute disproportionately to movement; these lands represent opportunities for conservation and reforestation to prevent potentially significant impacts from future mining and urban development. This study prioritized remaining suitable habitat patches using modelled connectivity and local knowledge to gain insight into the status of an understudied species. This approach offers a relatively rapid method to identify potential land use conflicts, and to further focus research and locally appropriate conservation.

Integrating Drone Imagery into High Resolution Satellite Remote Sensing Assessments of Estuarine Environments

Very high-resolution satellite imagery (≤5 m resolution) has become available on a spatial and temporal scale appropriate for dynamic wetland management and conservation across large areas. Estuarine wetlands have the potential to be mapped at a detailed habitat scale with a frequency that allows immediate monitoring after storms, in response to human disturbances, and in the face of sea-level rise. Yet mapping requires significant fieldwork to run modern classification algorithms and estuarine environments can be difficult to access and are environmentally sensitive. Recent advances in unoccupied aircraft systems (UAS, or drones), coupled with their increased availability, present a solution. UAS can cover a study site with ultra-high resolution (<5 cm) imagery allowing visual validation. In this study we used UAS imagery to assist training a Support Vector Machine to classify WorldView-3 and RapidEye satellite imagery of the Rachel Carson Reserve in North Carolina, USA. UAS and field-based accuracy assessments were employed for comparison across validation methods. We created and examined an array of indices and layers including texture, NDVI, and a LiDAR DEM. Our results demonstrate classification accuracy on par with previous extensive fieldwork campaigns (93% UAS and 93% field for WorldView-3; 92% UAS and 87% field for RapidEye). Examining change between 2004 and 2017, we found drastic shoreline change but general stability of emergent wetlands. Both WorldView-3 and RapidEye were found to be valuable sources of imagery for habitat classification with the main tradeoff being WorldView’s fine spatial resolution versus RapidEye’s temporal frequency. We conclude that UAS can be highly effective in training and validating satellite imagery.

Accounting for landscape heterogeneity improves spatial predictions of tree vulnerability to drought

As climate change continues, forest vulnerability to droughts and heatwaves is increasing, but vulnerability varies regionally and locally through landscape position. Also, most models used in forecasting forest responses to heat and drought do not incorporate relevant spatial processes. In order to improve spatial predictions of tree vulnerability, we employed a nonlinear stochastic model of soil moisture dynamics accounting for landscape differences in aspect, topography and soils. Across a watershed in central Texas we modeled dynamic water stress for a dominant tree species, Juniperus ashei, and projected future dynamic water stress through the 21st century. Modeled dynamic water stress tracked spatial patterns of remotely sensed drought-induced canopy loss. Accuracy in predicting drought-impacted stands increased from 60%, accounting for spatially variable soil conditions, to 72% when also including lateral redistribution of water and radiation/temperature effects attributable to aspect. Our analysis also suggests that dynamic water stress will increase through the 21st century, with trees persisting at only selected microsites. Favorable microsites/refugia may exist across a landscape where trees can persist; however, if future droughts are too severe, the buffering capacity of an heterogeneous landscape could be overwhelmed. Incorporating spatial data will improve projections of future tree water stress and identification of potential resilient refugia.

Open Source Remote Sensing and (Some) GIS for Ecologists

Satellite and aircraft-based remote sensing technologies provide essential and continuous monitoring of Earth surface processes that underpin our ecological understanding across broad areas. Remotely sensed data offer an alternative and important complement to field data collection that quickly becomes timeand cost-prohibitive over large areas. As of mid-2016 there were 374 Earth-observing satellites actively recording characteristics of our planet; 25% of these were launched since 2015 (Union of Concerned Scientists, http://www.ucsusa.org/). The use of these datasets for ecological research has been spurred in part by the dramatic increase in free and low-cost remotely sensed data that have become available in recent years. Notably, the US Geological Survey released the Landsat archive in 2009; as of January 1, 2015, the archive contained approximately 5.5 million Landsat images dating back to 1972. Remote Sensing and GIS for Ecologists will be a catalyst in the open source ‘geospatial revolution’ for remote sensing and spatial analysis. The text is concise, direct, and clearly written, providing the essentials and more for processing of remotely sensed imagery with open source tools. With increasing data availability and access, along with a burgeoning growth of open source tools, a reasonably priced guide to image and geospatial processing is very well timed. This book will allow practitioners to enter into the field of advanced analysis and processing of raw remotely sensed data for their own purposes. Much of the content currently exists scattered across the internet in tutorials, help forums, primary literature, etc., but this is the first time that these themes have been united in one text. The text and accompanying RSToolbox package for R (Leutner and Horning 2016) equip readers with the necessary information to obtain, process and analyze remotely sensed data to address ecological questions. The book commences by providing an introduction to spatial data, the open source concept and software options, followed by a pithy foray into the physical principles of remote sensing, space-borne and airborne sensors, and tradeoffs with respect to spatial, temporal and spectral resolutions. The book shifts quickly to an emphasis on applications and access to raster and vector data, progressing into preparing data for analysis, the fundamentals of preprocessing and corrections for multispectral datasets, and the use of field data and ancillary information for validation and analysis. The text then delves into greater depth regarding classification and change detection—some of the most common remote sensing analyses in the field of ecology. Later chapters provide D. Schaffer-Smith (&) J. J. Swenson Nicholas School of the Environment, Duke University, Durham, NC 27705, USA e-mail: danica.schaffer.smith@duke.edu