Accounting for aboveground carbon storage in shrubland and woodland ecosystems in the Great Basin

Improving the accuracy of carbon accounting in terrestrial ecosystems is critical for understanding carbon fluxes associated with land cover change, with significant implications for global carbon cycling and climate change. Semi‐arid ecosystems account for an estimated 45% of global terrestrial ecosystem area and are in many locations experiencing high degrees of degradation. However, aboveground carbon accounting has largely focused on tropical and forested ecosystems, while drylands have been relatively neglected. Here, we used a combination of field estimates, remotely sensed data, and existing land cover maps to create a spatially explicit estimate of aboveground carbon storage within the Great Basin, a semi‐arid region of the western United States encompassing 643,500 km2 of shrubland and woodland vegetation. We classified the region into seven distinct land cover categories: pinyon‐juniper woodland, sagebrush steppe, salt desert shrub, low sagebrush, forest, non‐forest, and other/excluded, each with an associated carbon estimate. Aboveground carbon estimates for pinyon‐juniper woodland were continuous values based on tree canopy cover. Carbon estimates for other land cover categories were based on a mean value for the land cover type. The Great Basin ecosystems contain an estimated 295.4 Tg in aboveground carbon, which is almost double the previous estimates that only accounted for forested ecosystems in the same area. Aboveground carbon was disproportionately stored in pinyon‐juniper woodland (43.7% carbon, 16.9% land area), while the shrubland systems accounted for roughly half of the total land area (49.1%) and one‐third of the total carbon. Our results emphasize the importance of distinguishing and accounting for the distinctive contributions of shrubland and woodland ecosystems when creating carbon storage estimates for dryland regions

Characterizing 3D Vegetation Structure from Space: Mission Requirements

Human and natural forces are rapidly modifying the global distribution and structure of terrestrial ecosystems on which all of life depends, altering the global carbon cycle, affecting our climate now and for the foreseeable future, causing steep reductions in species diversity, and endangering Earth s sustainability. To understand changes and trends in terrestrial ecosystems and their functioning as carbon sources and sinks, and to characterize the impact of their changes on climate, habitat and biodiversity, new space assets are urgently needed to produce high spatial resolution global maps of the three-dimensional (3D) structure of vegetation, its biomass above ground, the carbon stored within and the implications for atmospheric green house gas concentrations and climate. These needs were articulated in a 2007 National Research Council (NRC) report (NRC, 2007) recommending a new satellite mission, DESDynI, carrying an L-band Polarized Synthetic Aperture Radar (Pol-SAR) and a multi-beam lidar (Light RAnging And Detection) operating at 1064 nm. The objectives of this paper are to articulate the importance of these new, multi-year, 3D vegetation structure and biomass measurements, to briefly review the feasibility of radar and lidar remote sensing technology to meet these requirements, to define the data products and measurement requirements, and to consider implications of mission durations. The paper addresses these objectives by synthesizing research results and other input from a broad community of terrestrial ecology, carbon cycle, and remote sensing scientists and working groups. We conclude that: (1) current global biomass and 3-D vegetation structure information is unsuitable for both science and management and policy. The only existing global datasets of biomass are approximations based on combining land cover type and representative carbon values, instead of measurements of actual biomass. Current measurement attempts based on radar and multispectral data have low explanatory power outside low biomass areas. There is no current capability for repeatable disturbance and regrowth estimates. (2) The science and policy needs for information on vegetation 3D structure can be successfully addressed by a mission capable of producing (i) a first global inventory of forest biomass with a spatial resolution 1km or finer and unprecedented accuracy (ii) annual global disturbance maps at a spatial resolution of 1 ha with subsequent biomass accumulation rates at resolutions of 1km or finer, and (iii) transects of vertical and horizontal forest structure with 30 m along-transect measurements globally at 25 m spatial resolution, essential for habitat characterization. We also show from the literature that lidar profile samples together with wall-to53 wall L-band quad-pol-SAR imagery and ecosystem dynamics models can work together to satisfy these vegetation 3D structure and biomass measurement requirements. Finally we argue that the technology readiness levels of combined pol-SAR and lidar instruments are adequate for space flight. Remaining to be worked out, are the particulars of a lidar/pol-SAR mission design that is feasible and at a minimum satisfies the information and measurement requirement articulated herein

Conversion to soy on the Amazonian agricultural frontier increases streamflow without affecting stormflow dynamics

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Global Change Biology 17 (2011): 1821–1833, doi:10.1111/j.1365-2486.2011.02392.x.Large-scale soy agriculture in the southern Brazilian Amazon now rivals deforestation for pasture as the region’s predominant form of land use change. Such landscape level change can have substantial consequences for local and regional hydrology, which remain relatively unstudied. We examined how the conversion to soy agriculture influences water balances and stormflows using stream discharge (water yields) and the timing of discharge (stream hydrographs) in small (2.5 to 13.5 km2) forested and soy headwater watersheds in the Upper Xingu Watershed in the state of Mato Grosso, Brazil. We monitored water yield for one year in three forested and four soy watersheds. Mean daily water yields were approximately four times higher in soy than forested watersheds, and soy watersheds showed greater seasonal variability in discharge. The contribution of stormflows to annual streamflow in all streams was low (< 13% of annual streamflow), and the contribution of stormflow to streamflow did not differ between land uses. If the increases in water yield observed in this study are typical, landscape-scale conversion to soy substantially alters water-balance, potentially altering the regional hydrology over large areas of the southern Amazon.This project was supported by grants from NSF (DEB-0640661) and the Fundaçao de Amparo à Pesquisa do Estado de São Paulo (FAPESP 03/13172-2)

Valuation of ecosystem services to inform management of multiple-use landscapes

Public agencies worldwide are increasingly adopting an ecosystem service framework to manage lands serving multiple uses. Yet, reliable, practical, and well-tailored methods remain a major limitation in moving from conceptual to actionable approaches. Together with one of the largest federal land managing agencies, we co-develop and co-demonstrate an ecosystem services approach tailored to specific decisions, through a process with potentially widespread relevance. With the U.S. Department of Defense (DoD), we focus on balancing military training with biodiversity and resource conservation under both budgetary and land-use pressures at a representative installation. In an iterative process of co-design and application, we define, map, and quantify multiple ecosystem services under realistic management options. Resource management budget emerges as a major determinant of the degree to which managers can sustain both necessary training environments – a DoD-specific ecosystem service – and a prairie ecosystem with species of conservation concern. We also found clear tradeoffs between training intensity and forest-related services. Our co-developed approach brings otherwise hidden values and tradeoffs to the fore in a balanced way that can help public agencies safeguard priority services under potentially conflicting uses and budget limitations

Mapping and monitoring carbon stocks with satellite observations: a comparison of methods

Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions, and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient. Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon monitoring and markets

Digital elevation model validation with no ground control: application to the topodata dem in Brazil

Digital Elevation Model (DEM) validation is often carried out by comparing the data with a set of ground control points. However, the quality of a DEM can also be considered in terms of shape realism. Beyond visual analysis, it can be verified that physical and statistical properties of the terrestrial relief are fulfilled. This approach is applied to an extract of Topodata, a DEM obtained by resampling the SRTM DEM over the Brazilian territory with a geostatistical approach. Several statistical indicators are computed, and they show that the quality of Topodata in terms of shape rendering is improved with regards to SRTM

Relação entre as variáveis morfométricas extraídas de dados SRTM (Shuttle Radar Topography Mission) e a vegetação do Parque Nacional de Brasília

Este trabalho visa ao estudo da relação entre a distribuição de fitofisionomias do Parque Nacional de Brasília (PNB) e variáveis topográficas, para avaliar o potencial de dados SRTM isoladamente, como complemento aos dados tradicionalmente aplicados no sensoriamento remoto da vegetação. Esta relação foi verificada através de análises discriminantes entre o mapa de vegetação referência do PNB e as seguintes variáveis morfométricas: elevação, declividade, orientação de vertente, curvatura vertical e curvatura horizontal. Tais análises indicaram as classes de vegetação que podem ser separadas com base nas condições topográficas do terreno. As variáveis morfométricas mais importantes na distinção entre os tipos vegetacionais foram a elevação, a declividade e a orientação de vertente. Apesar de os dados morfométricos mostrarem potencial indicativo das classes de vegetação, as análises resultaram em discriminação em um nível aquém do detalhamento temático do mapa referência. Tal desempenho pode ser explicado pela incompatibilidade das escalas de variação exibidas entre os dados morfométricos em relação ao tamanho das unidades de mapeamento da vegetação. Além disso, a variação de tipos de vegetação do cerrado pode ser explicada por uma série de outros fatores além da topografia. Com base nas análises discriminantes das variáveis morfométricas, foi possível o mapeamento experimental da vegetação ao nível de subfisionomias.This paper aims to study the relationship between the distribution of vegetation in Brasilia National Park and topographic variables, to evaluate the potential of SRTM data alone, in addition to data traditionally used in remote sensing of vegetation. A map of vegetation of the area was used as a reference and the morphometric variables (elevation, slope, aspect and profile and plane curvatures) were compared to the mapped units. Analyses indicated vegetation types easily discriminated depending on topographic position. The variables elevation, slope and aspect were shown to be the most important for their high discrimination power of the vegetation types. Although morphometric data are recognized as having strong potential for characterizing vegetation, this was not shown in the results, due to the mismatching of variability scales between the two sources of data, where large units tend to exhibit similar distribution patterns of morphometry, and comprise classes with different responses for morphometric constraints. Discriminant analyses of morphometric variables allowed vegetation mapping up to sub-physiognomy levels