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

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

Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data

Urban and peri-urban environments are composed of a wide variety of materials, making land cover classification challenging. The objective of this research is to determine how effectively multi-season Landsat Enhanced Thematic Mapper Plus (ETM+) and single-season Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data can be combined to map 17 land cover categories in the Greater Boston area of, Massachusetts, USA. The key goal of this work is to test the integration of radar and optical data. The contribution of different dimensions of input data to a random forest classifier was evaluated with map accuracy statistics. PALSAR data produced a 30.99% overall classification accuracy. Higher classification accuracy (72.24%) was achieved by adding texture variables derived from the PALSAR data. A September Landsat image produced a map accuracy of 77.96%. The inclusion of Landsat images from other three seasons increased map accuracy to 86.86% and Landsat derived texture variables further increased the map accuracy to 92.69%. The highest map accuracy (93.82%) was achieved by combining Landsat and PALSAR. Though combining PALSAR and Landsat only increased the overall accuracy by 1.1%, it was a statistically significant increase, whose magnitude was limited by the high accuracy already achieved with Landsat data. Moreover, confusion matrices and land cover maps indicated that most of this increase was from three urban land cover types (low density residential, high density residential, and commercial/industrial). The results demonstrate the value of combining multitemporal Landsat imagery, ALOS PALSAR data, and texture variables for land cover classification in urban and peri-urban environments. © 2011 Elsevier Inc

The Influence of Vertical and Horizontal Habitat Structure on Nationwide Patterns of Avian Biodiversity

With limited resources for habitat conservation, the accurate identification of high-value avian habitat is crucial. Habitat structure affects avian biodiversity but is difficult to quantify over broad extents. Our goal was to identify which measures of vertical and horizontal habitat structure are most strongly related to patterns of avian biodiversity across the conterminous United States and to determine whether new measures of vertical structure are complementary to existing, primarily horizontal, measures. For 2,546 North American Breeding Bird Survey routes across the conterminous United States, we calculated canopy height and biomass from the National Biomass and Carbon Dataset (NBCD) as measures of vertical habitat structure and used land-cover composition and configuration metrics from the 2001 National Land Cover Database (NLCD) as measures of horizontal habitat structure. Avian species richness was calculated for each route for all birds and three habitat guilds. Avian species richness was significantly related to measures derived from both the NBCD and NLCD. The combination of horizontal and vertical habitat structure measures was most powerful, yielding high R2 values for nationwide models of forest (0.70) and grassland (0.48) bird species richness. New measures of vertical structure proved complementary to measures of horizontal structure. These data allow the efficient quantification of habitat structure over broad scales, thus informing better land management and bird conservation. Con recursos limitados para la conservación, la identificación precisa de los hábitats de alto valor para la aves es crucial. La estructura del hábitat afecta la diversidad de aves pero es difícil de cuantificar en grandes extensiones de terreno. Nuestra meta fue identificar qué medidas de la estructura vertical y horizontal del hábitat están más fuertemente relacionadas con los patrones de diversidad de aves dentro de los límites de los Estados Unidos, y determinar si las nuevas medidas de la estructura vertical se complementan con las medidas existentes y principalmente de la estructura horizontal. Calculamos la altura del dosel y la biomasa para 2546 rutas del Censo Norteamericano de Aves Reproductivas a partir del Conjunto Nacional de Datos de Biomasa y Carbono (NBCD, por sus siglas en inglés) como medidas de la estructura vertical del hábitat, y usamos las medidas de composición y configuración de la cobertura del terreno de la Base de Datos Nacional de Cobertura del Terreno (NLCD) como medidas de la estructura horizontal del hábitat. La riqueza de especies de aves fue calculada para cada ruta, para todas las aves y tres tipos de hábitat. Las medidas derivadas de el NCBD y el NLCD estuvieron significativamente relacionadas con la riqueza de especies de aves. La combinación de las medidas de estructura horizontal y vertical del hábitat fue más poderosa, derivando mayores valores de R2 para los modelos a escala nacional de riqueza de especies de aves de bosques (0.70) y praderas (0.48). Las nuevas medidas de la estructura vertical se establecieron como medidas complementarias de la estructura horizontal. Estos datos permiten la cuantificación eficiente de la estructura del hábitat en grandes escalas, de manera que informan mejores prácticas de manejo de la tierra y de conservación de las aves

Global sentinel-1 insar coherence. Opportunities for model-based estimation of land parameters

In this paper, we assess the estimation of bio-physical land parameters from time-series of interferometric SAR coher- ence supported by a physical model. The random-motion- over-ground model (RMoG) is revisited to partially capture the short- and long-term temporal variability of the coherence caused by motion of the scatterers and changes in their di- electric properties. The recently-published global Sentinel-1 interferometric coherence dataset is used to compare model predictions with observations and evaluate the need for ad- ditional model assumptions or ancillary data sets. Space- borne lidar data acquired by GEDI are also considered to fur- ther constrain the parameter estimation. This work is partic- ularly relevant to upcoming SAR missions such as NISAR and ROSE-L that will generate global and dense time-series of interferometric temporal coherence at L-band