Spatial Distribution and Quantification of Forest Treatment Residues for Bioenergy Production

The availability and spatial distribution of forest treatment residues are prerequisites to supply chain development for bioenergy production. To accurately estimate potential residue quantities, data must be provided to simulate stand-level silviculture across the landscape of interest. However, biomass utilization assessments often consider broad regions where adequate data are not supplied. At present, these measures are addressed using strategic level assessments and broad-based management that may not be applicable to all areas of the landscape. This thesis introduces a new methodology for spatially describing stand-level treatment residue quantities based on detailed silvicultural prescriptions and site specific management. Using National Agricultural Imagery Program (NAIP) imagery, the forest is segmented into treatment units based on user defined size constraints. Using a remote sensing model based on NAIP imagery and Forest Inventory and Analysis plot data, these units are attributed with stand-level descriptions of basal area, tree density, above ground biomass, and quadratic mean diameter . The outputs are used to develop silvicultural prescriptions and estimate available treatment residues under three alternative management scenarios at a range of delivered prices per bone dried ton (bdt) to a nearby bioenergy facility in southwestern Colorado. Using a marginal cost approach where treatment costs were covered by merchantable yields, the breakeven delivered price of treatment residues in this study is $48.94 per bdt yielding 167,685 bdt following a 10 year management simulation at a 5,000 acre per year annual allowable treatment level

Living up to the hype of hyperspectral aquatic remote sensing: science, resources and outlook

Intensifying pressure on global aquatic resources and services due to population growth and climate change is inspiring new surveying technologies to provide science-based information in support of management and policy strategies. One area of rapid development is hyperspectral remote sensing: imaging across the full spectrum of visible and infrared light. Hyperspectral imagery contains more environmentally meaningful information than panchromatic or multispectral imagery and is poised to provide new applications relevant to society, including assessments of aquatic biodiversity, habitats, water quality, and natural and anthropogenic hazards. To aid in these advances, we provide resources relevant to hyperspectral remote sensing in terms of providing the latest reviews, databases, and software available for practitioners in the field. We highlight recent advances in sensor design, modes of deployment, and image analysis techniques that are becoming more widely available to environmental researchers and resource managers alike. Systems recently deployed on space- and airborne platforms are presented, as well as future missions and advances in unoccupied aerial systems (UAS) and autonomous in-water survey methods. These systems will greatly enhance the ability to collect interdisciplinary observations on-demand and in previously inaccessible environments. Looking forward, advances in sensor miniaturization are discussed alongside the incorporation of citizen science, moving toward open and FAIR (findable, accessible, interoperable, and reusable) data. Advances in machine learning and cloud computing allow for exploitation of the full electromagnetic spectrum, and better bridging across the larger scientific community that also includes biogeochemical modelers and climate scientists. These advances will place sophisticated remote sensing capabilities into the hands of individual users and provide on-demand imagery tailored to research and management requirements, as well as provide critical input to marine and climate forecasting systems. The next decade of hyperspectral aquatic remote sensing is on the cusp of revolutionizing the way we assess and monitor aquatic environments and detect changes relevant to global communities

Identifying Where REDD+ Financially Out Competes Oil Palm in Floodplain Landscapes Using a Fine-Scale Approach

Reducing Emissions from Deforestation and forest Degradation (REDD+) aims to avoid forest conversion to alternative land-uses through financial incentives. Oil-palm has high opportunity costs, which according to current literature questions the financial competitiveness of REDD+ in tropical lowlands. To understand this more, we undertook regional finescale and coarse-scale analyses (through carbon mapping and economic modelling) to assess the financial viability of REDD+ in safeguarding unprotected forest (30,173 ha) in the Lower Kinabatangan floodplain in Malaysian Borneo. Results estimate 4.7 million metric tons of carbon (MgC) in unprotected forest, with 64% allocated for oil-palm cultivations. Through fine-scale mapping and carbon accounting, we demonstrated that REDD+ can outcompete oil-palm in regions with low suitability, with low carbon prices and low carbon stock. In areas with medium oil-palm suitability, REDD+ could outcompete oil palm in areas with: very high carbon and lower carbon price; medium carbon price and average carbon stock; or, low carbon stock and high carbon price. Areas with high oil palm suitability, REDD + could only outcompete with higher carbon price and higher carbon stock. In the coarse-scale model, oil-palm outcompeted REDD+ in all cases. For the fine-scale models at the landscape level, low carbon offset prices (US $3 MgCO2e) would enable REDD+ to outcompete oil-palm in 55$27 million to secure these areas for 25 years. Higher carbon offset price (US $30 MgCO2e) would increase the competitiveness of REDD+ within the landscape but would still only capture between 69$380–416 million in carbon financing. REDD+ has been identified as a strategy to mitigate climate change by many countries (including Malaysia). Although REDD+ in certain scenarios cannot outcompete oil palm, this research contributes to the global REDD+ debate by: highlighting REDD+ competitiveness in tropical floodplain landscapes; and, providing a robust approach for identifying and targeting limited REDD+ funds

Identifying Where REDD plus Financially Out-Competes Oil Palm in Floodplain Landscapes Using a Fine-Scale Approach

Reducing Emissions from Deforestation and forest Degradation (REDD+) aims to avoid forest conversion to alternative land-uses through financial incentives. Oil-palm has high opportunity costs, which according to current literature questions the financial competitiveness of REDD+ in tropical lowlands. To understand this more, we undertook regional fine-scale and coarse-scale analyses (through carbon mapping and economic modelling) to assess the financial viability of REDD+ in safeguarding unprotected forest (30,173 ha) in the Lower Kinabatangan floodplain in Malaysian Borneo. Results estimate 4.7 million metric tons of carbon (MgC) in unprotected forest, with 64% allocated for oil-palm cultivations. Through fine-scale mapping and carbon accounting, we demonstrated that REDD+ can outcompete oil-palm in regions with low suitability, with low carbon prices and low carbon stock. In areas with medium oil-palm suitability, REDD+ could outcompete oil palm in areas with: very high carbon and lower carbon price; medium carbon price and average carbon stock; or, low carbon stock and high carbon price. Areas with high oil palm suitability, REDD+ could only outcompete with higher carbon price and higher carbon stock. In the coarse-scale model, oil-palm outcompeted REDD+ in all cases. For the fine-scale models at the landscape level, low carbon offset prices (US $3 MgCO_{2} e) would enable REDD+ to outcompete oil-palm in 55$27 million to secure these areas for 25 years. Higher carbon offset price (US $30 MgCO_{2}e) would increase the competitiveness of REDD+ within the landscape but would still only capture between 69$380–416 million in carbon financing. REDD+ has been identified as a strategy to mitigate climate change by many countries (including Malaysia). Although REDD+ in certain scenarios cannot outcompete oil palm, this research contributes to the global REDD+ debate by: highlighting REDD+ competitiveness in tropical floodplain landscapes; and, providing a robust approach for identifying and targeting limited REDD+ funds

Calibration of DART Radiative Transfer Model with Satellite Images for Simulating Albedo and Thermal Irradiance Images and 3D Radiative Budget of Urban Environment

Remote sensing is increasingly used for managing urban environment. In this context, the H2020 project URBANFLUXES aims to improve our knowledge on urban anthropogenic heat fluxes, with the specific study of three cities: London, Basel and Heraklion. Usually, one expects to derive directly 2 major urban parameters from remote sensing: the albedo and thermal irradiance. However, the determination of these two parameters is seriously hampered by complexity of urban architecture. For example, urban reflectance and brightness temperature are far from isotropic and are spatially heterogeneous. Hence, radiative transfer models that consider the complexity of urban architecture when simulating remote sensing signals are essential tools. Even for these sophisticated models, there is a major constraint for an operational use of remote sensing: the complex 3D distribution of optical properties and temperatures in urban environments. Here, the work is conducted with the DART (Discrete Anisotropic Radiative Transfer) model. It is a comprehensive physically based 3D radiative transfer model that simulates optical signals at the entrance of imaging spectro-radiometers and LiDAR scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental (atmosphere, topography,…) and instrumental (sensor altitude, spatial resolution, UV to thermal infrared,…) configuration. Paul Sabatier University distributes free licenses for research activities. This paper presents the calibration of DART model with high spatial resolution satellite images (Landsat 8, Sentinel 2, etc.) that are acquired in the visible (VIS) / near infrared (NIR) domain and in the thermal infrared (TIR) domain. Here, the work is conducted with an atmospherically corrected Landsat 8 image and Bale city, with its urban database. The calibration approach in the VIS/IR domain encompasses 5 steps for computing the 2D distribution (image) of urban albedo at satellite spatial resolution. (1) DART simulation of satellite image at very high spatial resolution (e.g., 50cm) per satellite spectral band. Atmosphere conditions are specific to the satellite image acquisition. (2) Spatial resampling of DART image at the coarser spatial resolution of the available satellite image, per spectral band. (3) Iterative derivation of the urban surfaces (roofs, walls, streets, vegetation,…) optical properties as derived from pixel-wise comparison of DART and satellite images, independently per spectral band. (4) Computation of the band albedo image of the city, per spectral band. (5) Computation of the image of the city albedo and VIS/NIR exitance, as an integral over all satellite spectral bands. In order to get a time series of albedo and VIS/NIR exitance, even in the absence of satellite images, ECMWF information about local irradiance and atmosphere conditions are used. A similar approach is used for calculating the city thermal exitance using satellite images acquired in the thermal infrared domain. Finally, DART simulations that are conducted with the optical properties derived from remote sensing images give also the 3D radiative budget of the city at any date including the date of the satellite image acquisition

Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations.

By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A B. This doctoral project moved from the working hypothesis that SCBIR computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding EO image understanding (EO-IU) in operating mode, synonym of GEOSS ESA EO Level 2 product human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product. Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations