A Macroecological Analysis of SERA Derived Forest Heights and Implications for Forest Volume Remote Sensing

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 102–106 plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100

Canopy reconstruction from interferometric SAR

Interferometric Synthetic Aperture Radar (InSAR) is investigated as a method for 3D tree mapping. When operational, the method may be important for monitoring forests with a persistent cloud cover such as tropical rain forests. The problem of crown displacement due to lay-over in a vegetation with a large vertical variation in scatterer height is studied. It is shown that crown displacements can be corrected for, by using coherence. An analytical expression for the coherence is derived which can be used for crown position corrections. The model is validated and shown to be consistent with observations. The water cloud model has been extended with interferometric phase, coherence and crown geometry. This model is used to simulate realistic InSAR images of a 3D forest canopy using measurements of 1064 trees. It is validated by comparing observed with simulated image statistics. The normalised second intensity moment, coherence histogram and coherence autocorrelation in range direction are used for this purpose.Results indicate a good agreement at C-band for an extinction coefficient of 0.3 per meter. Two image transforms are derived based on Gaussian scattering assumptions. These transforms may be used for automated canopy reconstruction. Finally, the simulation method and the intensity image transform are used to study the effect of InSAR system parameters on the position accuracy

Radar polarimetry and interferometry for remote sensing of boreal forest

Forest biomass is a key parameter of the global biosphere which is linked to many fields of research. Modeling addressing climate, ecology, and economics as well as many other prediction frameworks require an accurate assessment of global forest biomass. Methods for producing forest information are rapidly developing and traditional forest inventory by visual estimation has been gradually replaced by the use of airborne and spaceborne instruments. Nevertheless, the estimation of biomass on a global basis including boreal, temperate, and tropical forests, is still a major challenge. Among other spaceborne sensors, synthetic aperture radar (SAR) is one of the most suitable tools for large scale mapping and it has also been often used for forest mapping. However, commonly used backscattering intensity based methods do not provide a satisfactory accuracy for biomass estimation; hence, the scientific radar community has been developing more accurate means based on advanced SAR imaging and analyzing techniques, such as SAR polarimetry and interferometry. The work within this thesis contributes to this effort specifically in the field of remote sensing with the emphasis on SAR polarimetry and interferometry for boreal forest applications. The study concentrates on three main topics: polarimetric SAR image analysis, retrieval of forest height by means of SAR interferometry, and modeling of radar backscattering from trees. The main contributions of this work include a new effective approach in polarimetric target decomposition, novel polarimetric visualization schemes, an improved interferometric tree height estimation method suitable for boreal forest, interferometric tree height estimation capability demonstration for X-band, a novel method for relating SAR measurements to single tree scattering modeling, and taking the scattering modeling from a pine tree to the single needle level with accurate field models. Furthermore, the forest height estimation scheme proposed in this work potentially enables tree height estimation with existing spaceborne interferometric X-band SAR systems. The proposed method uses an interferometric coherence model and a ground elevation model to produce accurate tree height maps from single polarization interferometric SAR data. The method is demonstrated with airborne SAR measurements and will be tested in the near future with satellite data. Since tree height is related to forest biomass through tree allometry, tree height measurements from space would enable more accurate global forest biomass maps

Review Article: Global Monitoring of Snow Water Equivalent Using High-Frequency Radar Remote Sensing

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth\u27s surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth\u27s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world\u27s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth\u27s cold regions\u27 ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

Synergism of optical and radar data for forest structure and biomassSinergismo entre dados ópticos e de radar da estrutura da floresta e biomassa

AbstractThe structure of forests, the three-dimensional arrangement of individual trees, has a profound effect on how ecosystems function and carbon cycle, water and nutrients. Repeated optical satellite observations of vegetation patterns in two-dimensions have made significant contributions to our understanding of the state and dynamics of the global biosphere. Recent advances in Remote Sensing technology allow us to view the biosphere in three-dimensions and provide us with refined measurements of horizontal as well as vertical structure of forests. This paper provides an overview of the recent advances in fusion of optical and radar imagery in assessing terrestrial ecosystem structure and aboveground biomass. In particular, the paper will focus on radar and LIDAR sensors from recent and planned spaceborne missions and provide theoretical and practical applications of the measurements. Finally, the relevance of these measurements for reducing the uncertainties of terrestrial carbon cycle and the response of ecosystems to future climate will be discussed in details. ResumoA estrutura de florestas, o arranjo tridimensional de árvores individuais, tem um efeito profundo sobre o funcionamento dos ecossistemas e do ciclo do carbono, água e nutrientes. Repetidas observações de satélite óptico de padrões de vegetação em duas dimensões trouxeram contribuições significativas para a nossa compreensão do estado e da dinâmica da biosfera global. Recentes avanços na tecnologia de Sensoriamento Remoto nos permitem ver a biosfera em três dimensões e nos fornecer medições apuradas da estrutura horizontal, bem como a vertical das florestas. Esse artigo fornece uma visão geral dos recentes avanços na fusão de imagens ópticas e de radar para avaliar a estrutura do ecossistema terrestre e biomassa. Em particular, o trabalho concentra-se em sensores radar e LIDAR de recentes missões espaciais planejadas e fornece aplicações teóricas e praticas das medições. Por fim, a relevância dessas medidas para reduzir as incertezas do ciclo de carbono terrestre e de resposta dos ecossistemas ao clima no futuro será discutida em detalhes

Earth resources: A continuing bibliography with indexes (issue 51)

This bibliography lists 382 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Growing stock volume estimation in temperate forsted areas using a fusion approach with SAR Satellites Imagery

Forest monitoring plays a central role in the context of global warming mitigation and in the assessment of forest resources. To meet these challenges, significant efforts have been made by scientists to develop new feasible remote sensing techniques for the retrieval of forest parameters. However, much work remains to be done in this area, in particular in establishing global assessments of forest biomass. In this context, this Ph.D. Thesis presents a complete methodology for estimating Growing Stock Volume (GSV) in temperate forested areas using a fusion approach based on Synthetic-Aperture Radar (SAR) satellite imagery. The investigations which were performed focused on the Thuringian Forest, which is located in Central Germany. The satellite data used are composed of an extensive set of L-band (ALOS PALSAR) and X-band (TerraSAR-X, TanDEM-X, Cosmo-SkyMed) images, which were acquired in various sensor configurations (acquisition modes, polarisations, incidence angles). The available ground data consists of a forest inventory delivered by the local forest offices. Weather measurements and a LiDAR DEM complete the datasets. The research showed that together with the topography, the forest structure and weather conditions generally limited the sensitivity of the SAR signal to GSV. The best correlations were obtained with ALOS PALSAR (R2 = 0.61) and TanDEM-X (R2 = 0.72) interferometric coherences. These datasets were chosen for the retrieval of GSV in the Thuringian Forest and led with regressions to an root-mean-square error (RMSE) in the range of 100─200 m3ha-1. As a final achievement of this thesis, a methodology for combining the SAR information was developed. Assuming that there are sufficient and adequate remote sensing data, the proposed fusion approach may increase the biomass maps accuracy, their spatial extension and their updated frequency. These characteristics are essential for the future derivation of accurate, global and robust forest biomass maps