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

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

A Range of Earth Observation Techniques for Assessing Plant Diversity

AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS

Synergies of multiple remote sensing data sources for REDD+ monitoring

Remote sensing technologies can provide objective, practical and cost-effective solutions for developing and maintaining REDD+ monitoring systems. This paper reviews the potential and status of available remote sensing data sources with a focus on different forest information products and synergies among various approaches and evolving technologies. There is significant technical capability of remote sensing technologies but operational usefulness is constrained by lack of consistent and continuous coverage, data availability in developing countries, appropriate methodologies for national-scale use and available capacities in developing countries. Coordinated international efforts, regional cooperation and continued research efforts are essential to further develop national approaches and capacities to fully explore and use the potential remote sensing has to offer for REDD+ forest monitorin