GEDI and TanDEM-X Fusion for 3D Forest Structure Parameter Retrieval

GEDI: Global Ecosystem Dynamics Investigation. Selected in late 2014 for $94 M (Class C mission). Multi-beam waveform lidar instrument. Deployed on International Space Station. Launch on SpaceX-17: Nov. 2018. Nominal 2 year mission length

Trees Outside Forests are an Underestimated Resource in a Country with Low Forest Cover

Trees outside forests (TOF) are an underrepresented resource in forest poor nations. As a result of their frequent omission from national forest resource assessments and a lack of readily available very-high-resolution remotely sensed imagery, TOF status and characterization has until now, been unknown. Here, we assess the capacity of openly available 10 m ESA Sentinel constellation satellite imagery for mapping TOF extent at the national level in Bangladesh. In addition, we estimate canopy height for TOF using a TanDEM-X DEM. We map 2,233,578 ha of TOF in Bangladesh with a mean canopy height of 7.3 m. We map 31 and 53% more TOF than existing estimates of TOF and forest, respectively. We find TOF in Bangladesh is nationally fragmented as a consequence of agricultural activity, yet is capable of maintaining connectedness between remaining stands. Now, TOF accounting is feasible at the national scale using readily available datasets, enabling the mainstream inclusion of TOF in national forest resource assessments for other countries.</p

Structural characterisation of mangrove forests achieved through combining multiple sources of remote sensing data

Temporal information on mangrove extent, age, structure and biomass provides an important contribution towards understanding the role of these ecosystems in terms of the services they provide (e.g. in relation to storage of carbon, conservation biodiversity), particularly given the diversity of influences of human activity and natural events and processes. Focusing on the Matang Mangrove Forest Reserve (MMFR) in Perak Province, Peninsular Malaysia, this study aimed to retrieve comprehensive information on the biophysical properties of mangroves from spaceborne optical and Synthetic Aperture Radar (SAR) to support better understanding of their dynamics in a managed setting. For the period 1988 to 2016 (29 years), forest age was estimated on at least an annual basis by combining time-series of Landsat-derived Normalised Difference Moisture Index (NDMI) and Japanese L-band Synthetic Aperture Radar (SAR) data. The NDMI was further used to retrieve canopy cover (%). Interferometric Shuttle Radar Topographic Mission (SRTM) X/C-band (2000), TanDEM-X-band (2010–2016) and stereo WorldView-2 stereo (2016) data were evaluated for their role in estimating canopy height (CH), from which above ground biomass (AGB, Mg ha−1) was derived using pre-established allometry. Whilst both L-band HH and HV data increased with AGB after about 8–10 years of growth, retrieval was compromised by mixed scattering from varying amounts of dead woody debris following clearing and wood material within regenerating forests, thinning of trees at ~15 and 20 years, and saturation of L-band SAR data after approximately 20 years of growth. Reference was made to stereo Phantom-3 DJI stereo imagery to support estimation of canopy cover (CC) and validation of satellite-derived CH. AGB estimates were compared with ground-based measurements. Using relationships with forest age, both CH and AGB were estimated for each date of Landsat or L-band SAR observation and the temporal trends in L-band SAR were shown to effectively track the sequences of clearing and regeneration. From these, four stages of the harvesting cycle were defined. The study provided new information on the biophysical properties and growth dynamics of mangrove forests in the MMFR, inputs for future monitoring activities, and methods for facilitating better characterisation and mapping of mangrove areas worldwide.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion.</p

The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography

Obtaining accurate and widespread measurements of the vertical structure of the Earths forests has been a longsought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6 N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function

GEDI launches a new era of biomass inference from space

Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission's integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space

Comprehensive comparison of airborne and spaceborne SAR and LiDAR estimates of forest structure in the tallest mangrove forest on earth

A recent suite of new global-scale satellite sensors and regional-scale airborne campaigns are providing a wealth of remote sensing data capable of dramatically advancing our current understanding of the spatial distribution of forest structure and carbon stocks. However, a baseline for forest stature and biomass estimates has yet to be established for the wide array of available remote sensing products. At present, it remains unclear how the estimates from these sensors compare to one another in terrestrial forests, with a clear dearth of studies in high carbon density mangrove ecosystems. In the tallest mangrove forest on Earth (Pongara National Park, Gabon), we leverage the data collected during the AfriSAR campaign to evaluate 17 state-of-the-art sensor data products across the full range of height and biomass known to exist globally in mangrove forest ecosystems, providing a much-needed baseline for sensor performance. Our major findings are: (Houghton, Hall, Goetz) height estimates are not consistent across products, with opposing trends in relative and absolute errors, highlighting the need for an adaptive approach to constraining height estimates (Panet al., 2011); radar height estimates had the lowest calibration error and bias, with further improvements using LiDAR fusion (Bonan, 2008); biomass variability and uncertainty strongly depends on forest stature, with variation across products increasing with canopy height, while relative biomass variation was highest in low-stature stands (Le Quereet al., 2017); a remote sensing product's sensitivity to variations in canopy structure is more important than the absolute accuracy of height estimates (Mitchardet al., 2014); locally-calibrated area-wide totals are more representative than generalized global biomass models for high-precision biomass estimates. The findings presented here provide critical baseline expectations for height and biomass predictions across the full range of mangrove forest stature, which can be directly applied to current (TanDEM-X, GEDI, ICESat-2) and future (NISAR, BIOMASS) global-scale forest monitoring missions

Special Issue on Forest Structure Estimation in Remote Sensing

FORESTS are important ecosystems to all life on Earth. They provide shelter for many flora and fauna, rich resources for anthropogenic development and an important sink in the global carbon cycle. Satellite earth observation provides a critical capacity to observe this ecosystem wall to wall. Especially, synthetic aperture radar (SAR) and Lidar technologies hold large potential to measure forest structure properties such as forest height and biomass and are consequently used in existing observation systems. In addition to the existing observation systems space agencies are implementing or planning dedicated satellite missions capitalizing on these technologies, such as ESAs BIOMASS, NASAs GEDI, and NISAR missions or DLRs Tandem-L mission concept. Despite the sensitivity of SAR and Lidar technologies to forest structure the actual interpretation of the measurements is challenging. Forest structure, environmental effects and system parameters have a confounding effect on the measurements and need to be accounted for in the biophysical parameter retrieval. The impact of these factors is often not well understood and hampers our ability to make optimum use of newly available technologies and their synergies. The most striking development in the last years was in the three-dimensional (3-D) radar data processing. The evolution and refinement of coherent multibaseline (tomographic) processing techniques dramatically increased the quality of the reconstructed 3-D radar reflectivity allowing a deeper insight in the 3-D scattering processes at different polarizations and frequencies and of the opportunities arising from this new measurement technique to monitor and investigate 3-D forest structure. This special issue collect and highlight the latest results, Digital Object Identifier 10.1109/JSTARS.2018.2870266 data processing developments and lessons learned from existing satellite data, airborne data and tower based research campaigns