Quantifying aboveground biomass (AGB) in tropical forests is a challenging but necessary task to support actions to preserve and restore these ecosystems. Recent methods have leveraged the integration of NASA’s spaceborne lidar GEDI with imaging sensors to improve AGB predictions. However, the use of high-resolution (<1 m) stereo images from spaceborne sensors still needs to be explored within these frameworks. The objective of this study was to predict AGB in a tropical forest patch by combining stereo images and GEDI data. A digital surface model (Stereo-DSM) was generated by stereophotogrammetric processing of high-resolution stereo pairs collected from a spaceborne sensor. A canopy height model (Stereo-CHM) was then derived by subtracting an airborne lidar-derived digital terrain model from the Stereo-DSM. Descriptive statistics were calculated from the Stereo-CHM to be used as predictors in the model to predict AGB. We also calculated vegetation indices from the Harmonized Landsat-Sentinel and Sentinel-1 images to compare their relative importance in the model and assess their relationship to the Stereo-CHM metrics. Finally, we trained a Random Forest model using stereogrammetry - derived, multispectral and SAR metrics as features and GEDI’s footprint-level AGB product as a reference. The model to predict AGB yielded performance metrics of r = 0.63, RMSE = 25.38 Mg/ha, and MD = 2.1 Mg/ha. The metrics from the Stereo-CHM were ranked as the most important to the model. This is an indication that these metrics can add important information related to canopy structure to inform GEDI-based models to predict AGB. It is still necessary to evaluate these results considering a variation in canopy cover, topography, and understory vegetation. The findings are important to support advances on the integration of large footprint spaceborne lidar and images with sub-meter spatial resolution to characterize vegetation in tropical forests.

Commercial stereo imagery has provided unprecedented planar detail (< 2 m) of the Earth’s surface. However, a number compounding factors, including view angle, convergence angle, imaging detector design, ground sampling distance (GSD), time of day/year etc., induce bias in surface reconstructions. BlackSky 1 m GSD data and its unique frame detector/telescope configuration provides a novel resource with sensor capabilities of staring, and motion imagery/video to benchmark existing commercial digital elevation model (DEM) products. The BlackSky constellation with its high repeat collection capability also allows for dense imagery collection and enables multi-view stereo reconstruction. We evaluated the ability of BlackSky data for vegetation surface reconstruction using a multi-view stereo methodology and compared products to airborne LiDAR data. We found an Absolute Median Error of 1.77 m and normalized mean absolute deviation (NMAD) of 1.54 m in areas with sufficient volume of available digital surface models (DSMs). Our results indicate the potential benefit of this workflow and resulting products for vegetation analyses.

New Observing Systems (NOS) will be NASA’s next generation approach for Earth remote sensing, utilizing many diverse observing capabilities to produce optimized measurements integrated from multiple vantage points and in multiple dimensions. NOS will require strong data fusion foundations to be able to intelligently combine, and retrieve information from, data coming from assets differing in characteristics like instrument type, spectral domain, and spatial and temporal resolution. We are developing an end-to-end data fusion framework employing advanced Artificial Intelligence (AI) Machine Learning (ML) techniques with the primary purpose to drive the design and operation of multi-sensor NOS for Earth sciences and beyond. This work requires building ML-enabled analytic tools and advanced environments to take advantage of high-performance computing systems for the creation of a NOS workflow that utilizes large amounts of diverse airborne and satellite observations along with ancillary information including climate and drought time series and soil properties. We are demonstrating the framework using a forest productivity and degradation use case, but the framework is designed to be applicable to a wide variety of NOS scientific objectives.

Scientific analysis of changes of the Earth's land surface benefit from well characterized, science quality remotely sensed data. This data quality is the result of models that estimate and remove atmospheric constituents and account for sun-sensor geometry [1] – [3]. Surface reflectance (SR) in commercial very high resolution (< 5 m; VHR) spaceborne imagery routinely varies for unchanged surface features because of signal variation from the combined effects of atmospheric haze and a range of sun-sensor geometric scenarios of acquisitions [4]. Consistency from this imagery must be sufficient to identify and track the change or stability of fine-scale features that, though small, may be widely distributed across remote domains, and serve as key indicators of critical broad-scale environmental change [5], [6]. Currently commercial SR products are available, but typically the model employed is proprietary and the costs for using these products over a large domain can be significant (e.g., Planet Surface Reflectance v.2). Here we describe an open source workflow for the scientific community to improve detection of fine-scale change with commercial VHR imagery.

Validating land cover classification results from a machine learning model is a vital step in ensuring that further decisions are based on sound and robust results that can be trusted. Calculating pixel-wise validating statistics from a stack of land cover classification results, while computationally trivial for low-resolution imagery with a small spatial footprint, poses a significant challenge for very-high-resolution (VHR) imagery spanning a larger spatial footprint. Here we describe an open-source unified Python framework and workflow for the compositing of VHR imagery based on climatic and spatial information leveraging hardware acceleration. We additionally describe the implementation of per-pixel reduction algorithms which are used to reduce the stacked composite into a robust and accurate composite that is validated.

Circumboreal forests are currently experiencing rapid climate warming which is altering their structure, productivity, and status as a carbon sink. Very high-resolution (VHR; < 2 m) stereo-derived digital surface models are available to monitor these forests, but a similar resolution digital terrain model (DTM) is required to extract information about tree height, which is often used to estimate carbon content. To the best of our knowledge, no openly available VHR DTM currently exists. To address this need, we developed approaches to extract DTMs by filtering VHR stereo point clouds (PCs) in sparse canopies of Alaska. Our evaluation consisted of two stereo processing methods with three PC search radii at six different tree canopy cover (TCC) intervals. We found that VHR DTMs were robust for estimating bare ground at TCC intervals less than 40% with vertical errors <1.6 m using airborne small footprint light detection and ranging (LiDAR) as reference.

Forest aboveground biomass is a key environmental variable needed for constraining models of the global carbon cycle, monitoring stocks and fluxes of carbon in forests, and optimizing forest management toward climate mitigation. To date, limited satellite data have been available that are sensitive to Aboveground Biomass Density (AGBD), and the availability of new satellite lidar data streams from NASA's Global Ecosystem Dynamics Investigation (GEDI) [1] and Ice Cloud and Elevation Satellite (ICESat-2) [2] enable a new generation of AGBD estimates representative of 2018–2022 conditions. Here we explore the transferability of GEDI's AGBD estimation framework to ICESat-2. We compare distribution of Relative Height (RH) metrics from both products between 50 and 52° N, and find that ICESat-2's RH metrics are biased high compared to GEDI. We reprocess ICESat-2 RH metrics to make them more comparable to GEDI height metrics, and present a comparison of biomass estimates based on the original and new ICESat-2 RH metrics in boreal forests.

Commercial very high-resolution (VHR) Earth observing (EO) satellites have grown into constellations with global repeat coverage that can support existing NASA EO missions with stereo and multispectral capabilities. Sub-meter data from these instruments exceeds petabytes per year and the cost for data, storage systems and compute power have all dropped exponentially. Concurrently, through agreements with the National Geospatial-Intelligence Agency, NASA-Goddard Space Flight Center is acquiring VHR EO imagery from DigitalGlobe's WorldView-1, 2, 3 Quickbird-2, GeoEye-1 and IKONOS-2 satellites. To enhance the utility of these data we are developing an Application Program Interface (API) to produce on-demand user defined science ready products to support NASA's EO missions. These enhancements include two primary foci: 1) surface reflectance 1/2° ortho mosaics - multi-temporal 2 m multispectral imagery that can be used to investigate biodiversity, horizontal forest structure, surface water fraction, and land-cover land-use at the human scale; and 2) VHR digital elevation models (DEMs) - derived with the NASA Ames Stereo Pipeline. These enhanced products benefit Earth surface studies on the cryosphere (glacier mass balance, flow rates and snow depth), hydrology (lake/waterbody levels, landslides, subsidence) and the biosphere (vertical forest structure, tree canopy height and cover) among others. Here we present current API capabilities and recent examples of derived products used in NASA Earth Science projects.

The accurate mapping of forest AGB using remote sensing dataset is hindered by the saturation problem and the terrain effects. Direct measurement of forest spatial structures and terrains should be the solutions of these problems. However, the information of forest vertical structure and ground surface terrain are always mixed together in remote sensing datasets which can directly measure the elevations of ground objects. One potential way is to separate them is to synthesize InSAR, stereo imagery and lidar waveform. Theoretical model is needed for this effort. In this study, a unified model was presented, which can be used to simulate InSAR, stereo imagery and lidar waveforms over mountainous forest landscapes.

Time series of remote sensing data offers the opportunity to predict changes in vegetation extent and to estimate forest parameter change such as biomass. However, as sensors and technology advance, it is important to ensure that estimates obtained from different time periods or using different, but related, instruments are consistent in order to have confidence in detected change. This study compares estimates of biomass from small-footprint discrete-return LiDAR data and medium-footprint full-waveform LiDAR for Howland Experimental Forest, Maine, USA. Data were collected from both sensors during Summer 2009. Similar results were found using the same height metric with R2= 0.67, SE = 58.5 Mg ha-1and R2= 0.52, SE = 58.1 Mg ha-1, respectively. The predicted model of the relationship between LiDAR metrics and biomass was applied to data captured in 2003. Identified areas of change corresponded well with a map of forest management operations of varying intensities. Where sensitivity to change allows, vegetation age estimated using time series of Landsat observations, combined with biomass estimates, allows growth curves to be produced to monitor the effect of pests or disease, recovery rates following disturbance, or carbon sequestration.

Best merge region growing normally produces segmentations with closed connected region objects. Recognizing that spectrally similar objects often appear in spatially separate locations, we present an approach for tightly integrating best merge region growing with nonadjacent region object aggregation, which we call hierarchical segmentation or HSeg. However, the original implementation of nonadjacent region object aggregation in HSeg required excessive computing time even for moderately sized images because of the required intercomparison of each region with all other regions. This problem was previously addressed by a recursive approximation of HSeg, called RHSeg. In this paper, we introduce a refined implementation of nonadjacent region object aggregation in HSeg that reduces the computational requirements of HSeg without resorting to the recursive approximation. In this refinement, HSeg's region intercomparisons among nonadjacent regions are limited to regions of a dynamically determined minimum size. We show that this refined version of HSeg can process moderately sized images in about the same amount of time as RHSeg incorporating the original HSeg. Nonetheless, RHSeg is still required for processing very large images due to its lower computer memory requirements and amenability to parallel processing. We then note a limitation of RHSeg with the original HSeg for high spatial resolution images and show how incorporating the refined HSeg into RHSeg overcomes this limitation. The quality of the image segmentations produced by the refined HSeg is then compared with other available best merge segmentation approaches. Finally, we comment on the unique nature of the hierarchical segmentations produced by HSeg.

Mapping of boreal forest’s type, structure parameters and biomass are critical for understanding the boreal forest’s significance in the carbon cycle, its response to and impact on global climate change. The biggest deficiency of the existing ground based forest inventories is the uncertainty in the inventory data, particularly in remote areas of Siberia where sampling is sparse, lacking, and often decades old. Remote sensing methods can overcome these problems. In this study, we used the moderate resolution imaging spectroradiometer (MODIS) and unique waveform data of the geoscience laser altimeter system (GLAS) and produced a map of timber volume for a 10°×12° area in Central Siberia. Using these methods, the mean timber volume for the forested area in the total study area was 203 m3/ ha. The new remote sensing methods used in this study provide a truly independent estimate of forest structure, which is not dependent on traditional ground forest inventory methods.