Spaceborne Datasets Research Articles

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

This paper presents the object detection algorithms GRASS GIS applied for Landsat 8-9 OLI/TIRS data. The study area includes the Sudd wetlands located in South Sudan. This study describes a programming method for the automated processing of satellite images for environmental analytics, applying the scripting algorithms of GRASS GIS. This study documents how the land cover changed and developed over time in South Sudan with varying climate and environmental settings, indicating the variations in landscape patterns. A set of modules was used to process satellite images by scripting language. It streamlines the geospatial processing tasks. The functionality of the modules of GRASS GIS to image processing is called within scripts as subprocesses which automate operations. The cutting-edge tools of GRASS GIS present a cost-effective solution to remote sensing data modelling and analysis. This is based on the discrimination of the spectral reflectance of pixels on the raster scenes. Scripting algorithms of remote sensing data processing based on the GRASS GIS syntax are run from the terminal, enabling to pass commands to the module. This ensures the automation and high speed of image processing. The algorithm challenge is that landscape patterns differ substantially, and there are nonlinear dynamics in land cover types due to environmental factors and climate effects. Time series analysis of several multispectral images demonstrated changes in land cover types over the study area of the Sudd, South Sudan affected by environmental degradation of landscapes. The map is generated for each Landsat image from 2015 to 2023 using 481 maximum-likelihood discriminant analysis approaches of classification. The methodology includes image segmentation by ‘i.segment’ module, image clustering and classification by ‘i.cluster’ and ‘i.maxlike’ modules, accuracy assessment by ‘r.kappa’ module, and computing NDVI and cartographic mapping implemented using GRASS GIS. The benefits of object detection techniques for image analysis are demonstrated with the reported effects of various threshold levels of segmentation. The segmentation was performed 371 times with 90% of the threshold and minsize = 5; the process was converged in 37 to 41 iterations. The following segments are defined for images: 4515 for 2015, 4813 for 2016, 4114 for 2017, 5090 for 2018, 6021 for 2019, 3187 for 2020, 2445 for 2022, and 5181 for 2023. The percent convergence is 98% for the processed images. Detecting variations in land cover patterns is possible using spaceborne datasets and advanced applications of scripting algorithms. The implications of cartographic approach for environmental landscape analysis are discussed. The algorithm for image processing is based on a set of GRASS GIS wrapper functions for automated image classification.

Fire and Explosion Machine Learning Model for the Port of Los Angeles - California Safety Code and NFPA for Fire-Break Zoning

A correlative Hazard case study of Los Angeles Port has been performed on a tremendous dock fire that occurred on 22nd September 2014 at the Wilmington Dock along with a proposed Machine Learning approach concerning remote sensing techniques, a time-related air quality survey, and port safety codes, which are assessed and based on a modern port governance emergency intervention. The methodology of this research embraces a possible case of high fire severity urban zoning of the Los Angeles Harbour, on the occasion of the severe application of Health and Safety Code Section 18930 by the State Fire Marshal to respond opportunely to the recent extensive ignition across California. Set rules, dispatched by LA Port System, are detected following a Landsat 8/OLI (Operational Land Imager) remote sensing-based geospatial comparison with ground-based air samples. In order to target opportunely risk barriers in favour of Oil Tankers and critical garrisons, an extensive pre-processing calibration of spaceborne datasets has been performed for enhancing minor changes on the platforms. Besides, intrinsic limits of the OLI definition do not permit appropriate accuracy of small-scale evaluations; a decision tree model has been therefore engaged by embracing high variance and low bias of these compensated datasets. In this manner, four Regions of Interest have been marked to strengthen the sub-samples of these features. The flexibility of RF computation delivers positive automation for the missing values, categorical and continuous values; on the other side time consuming and interpretability drawbacks, notice a disadvantage of the implementation in terms of computational training, validation, and testing. In defence of this intervention approach, an air-model geostatistical distribution carried out pixel values based on the pre-burn weighted trained model and finally ensembled to buffer protection zones, according to the Safety Code Section 18930 as an endorsement of further sustainable and financial liabilities that are vital parameters in the post-Covid-sars19 crisis and demonstrate growth limits of Ports as periodically issued to theirs Internal Consistency Analysis. The advisory assessment research moreover a hardware performance to support on-time prediction models, by listing variable dependent regressive accuracy samples.

TomoSAR 3D Reconstruction for Buildings Using Very Few Tracks of Observation: A Conditional Generative Adversarial Network Approach

SAR tomography (TomoSAR) is an important technology for three-dimensional (3D) reconstruction of buildings through multiple coherent SAR images. In order to obtain sufficient signal-to-noise ratio (SNR), typical TomoSAR applications often require dozens of scenes of SAR images. However, limited by time and cost, the available SAR images are often only 3–5 scenes in practice, which makes the traditional TomoSAR technique unable to produce satisfactory SNR and elevation resolution. To tackle this problem, the conditional generative adversarial network (CGAN) is proposed to improve the TomoSAR 3D reconstruction by learning the prior information of building. Moreover, the number of tracks required can be reduced to three. Firstly, a TomoSAR 3D super-resolution dataset is constructed using high-quality data from the airborne array and low-quality data obtained from a small amount of tracks sampled from all observations. Then, the CGAN model is trained to estimate the corresponding high-quality result from the low-quality input. Airborne data experiments prove that the reconstruction results are improved in areas with and without overlap, both qualitatively and quantitatively. Furthermore, the network pretrained on the airborne dataset is directly used to process the spaceborne dataset without any tuning, and generates satisfactory results, proving the effectiveness and robustness of our method. The comparative experiment with nonlocal algorithm also shows that the proposed method has better height estimation and higher time efficiency.

The global and multi-annual MUSICA IASI {H<sub>2</sub>O, <i>δ</i>D} pair dataset

Abstract. We present a global and multi-annual space-borne dataset of tropospheric {H2O, δD} pairs that is based on radiance measurements from the nadir thermal infrared sensor IASI (Infrared Atmospheric Sounding Interferometer) on board the Metop satellites of EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). This dataset is an a posteriori processed extension of the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) IASI full product dataset as presented in Schneider et al. (2021b). From the independently retrieved H2O and δD proxy states, their a priori settings and constraints, and their error covariances provided by the IASI full product dataset, we generate an optimal estimation product for pairs of H2O and δD. Here, this standard MUSICA method for deriving {H2O, δD} pairs is extended using an a posteriori reduction of the constraints for improving the retrieval sensitivity at dry conditions. By applying this improved water isotopologue post-processing for all cloud-free MUSICA IASI retrievals, this yields a {H2O, δD} pair dataset for the whole period from October 2014 to December 2020 with global coverage twice per day (local morning and evening overpass times). In total, the dataset covers more than 1500 million individually processed observations. The retrievals are most sensitive to variations in {H2O, δD} pairs within the free troposphere, with up to 30 % of all retrievals containing vertical profile information in the {H2O, δD} pair product. After applying appropriate quality filters, the largest number of reliable pair data arises for tropical and subtropical summer regions, but higher latitudes also show a considerable amount of reliable data. Exemplary time series over the tropical Atlantic and West Africa are chosen to illustrate the potential of the MUSICA IASI {H2O, δD} pair data for atmospheric moisture pathway studies. Furthermore, in order to facilitate the application of this rather comprehensive MUSICA IASI {H2O, δD} pair dataset (referred to as Level-2), we additionally provide the data in a re-gridded and simplified format (Level-3) with focus on the quality-filtered {H2O, δD} pairs in the free troposphere. A technical documentation for guiding the use of both datasets is attached as the Supplement. Finally, the Level-2 dataset is referenced with the DOI https://doi.org/10.35097/415 (Diekmann et al., 2021a) and the Level-3 dataset with DOI https://doi.org/10.35097/495 (Diekmann et al., 2021b).

Investigation of the character and impact of tropical cyclone Yaas: a study over coastal districts of West Bengal, India

Tropical cyclones have become more frequent as a result of climate change and the associated temperature rise in the ocean surface, wreaking havoc on both natural and man-made elements. The most recent storm, Yaas, has had a wide-spread impact on coastal areas, with high-intensity wind, rainfall, and, most significantly, inundation in Odisha and West Bengal coastal region. Yaas formed over east central Bay of Bengal as a depression and gradually intensified to VSCS and finally made landfall near Balasore of Odisha coast, with a wind speed of 130–140 km/h. on 26th May, 2021. The present study is, therefore, aimed to characterize the cyclone Yaas and to investigate the expansion of cyclonic inundation in different sector of coastal West Bengal. Several space-borne data sets were employed in this study, including GPM data to illustrate precipitation variability, Sentinel-1 images for inundation mapping, and Sentinel-2 data to determine MNDWI for both pre- and post-cyclonic periods. The results show that during this cyclonic period, hundreds of km2 of land in West Bengal, including blocks of South 24 parganas, East Medinipur and North 24 parganas such as Sagar (37.10 km2), Namkhana (78.12 km2), Pathar Pratima (58.74 km2), Ramnagar I (15.24 km2) and II (19.62 km2), Khejuri (22.27 km2), and other blocks were inundated by cyclonic surge and about a total of 1195 mm of rainfall. Eventually, people have lost their homes, properties have damaged, and many agricultural fields have become barren by salt water accumulation.

Delay Doppler SAR Focusing and Quantitative Quality Control of the Radar for Europa Assessment and Sounding: Ocean to Near-Surface (REASON) Sounding Data Product

Synthetic aperture radar (SAR) focusing will be a fundamental step in the analysis of the radar sounding datasets collected by the Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON) instrument as part of NASA's upcoming Europa Clipper mission. Due to the flyby trajectory of the mission, REASON data acquisition will be distinct compared to other space-borne radar sounders, and therefore, require a tailored SAR focusing strategy. Here, we present a SAR focusing architecture based on the delay Doppler approach employed in US SHAllow RADar (SHARAD) data analysis with the following modifications for REASON data idiosyncrasies; an interpolation to a constant ground track interval to account for REASON's variable PRF; and an adaptive Doppler centroid estimation to account for the flyby geometry. The ability of our modified delay Doppler SAR focusing approach to focus space-borne datasets as well as its specific feasibility for REASON are demonstrated using both SHARAD and MARSIS datasets. In addition, we present a quantitative quality control framework based on pixel power probabilities and demonstrate how it can be leveraged to quantify the effects of SAR focusing and differentiate focused results generated with different processing parameters. Finally, we revisit and discuss the assumption of depth-independent SAR focusing implicit in the choice to construct a REASON SAR focusing approach based on the delay Doppler method and provide a comparison with depth-dependent SAR focusing for a simplified acquisition geometry.

Digital Elevation Models for topographic characterisation and flood flow modelling along low-gradient, terminal dryland rivers: A comparison of spaceborne datasets for the Río Colorado, Bolivia

Many dryland rivers are terminal systems, with small channels undergoing prominent downstream size reductions before ending on channelless floodplains, in wetlands, or at playa margins. Spaceborne Digital Elevation Models (DEMs) provide potential for assessing subtle topographic and hydrodynamic changes in these low-gradient, low-relief settings, but challenges are posed by limitations in vertical and horizontal accuracy. This study evaluates the use of different spaceborne DEMs for topographic characterisation and flood flow modelling of the low-gradient (<0.0006 m m−1) Río Colorado terminal system, Bolivia. A comparison between DEM and field dGPS elevation data (1290 measurement points) reveals that the TanDEM-X DEM 12 m (TDX-12 m) has a RMSE of 0.47 m, far less than those of other frequently used spaceborne DEMs such as ALOS RTC (4.58 m) and SRTM (6.02 m). For hydrodynamic modelling, TDX-12 m data were smoothed (adaptive filter and feature-preserving DEM smoothing) and upscaled. The smoothed TDX-12 m data were mosaiced with a dGPS data-derived river path, surveyed along a reach (mean width <30 m) with a prominent downstream size decrease. The methods enabled effective de-noising of the TDX-12 m data (RMSE 0.29 m) and resulted in a high linear regression correlation coefficient (0.75). HEC-RAS 2D modelling reveals that in the selected reach, overbank flooding starts in the downstream part when discharge is <18 m3/s, with flow initially spreading through crevasse channels and levee topographic lows. As discharge increases, flow spreads farther across the floodplain, ultimately forming connected floodplain flow in distal topographic lows. Satellite imagery and a derived water index indicate the same floodplain flow patterns as the modelling (critical success index 0.77). Wider use of DEMs based on TDX-12 m data for topographic characterization and flood flow modelling along relatively small, low-gradient terminal dryland rivers will result in many scientific and applied benefits.

Multiscale Integration of High-Resolution Spaceborne and Drone-Based Imagery for a High-Accuracy Digital Elevation Model Over Tristan da Cunha

Very high-resolution (VHR) optical Earth observation (EO) satellites as well as low-altitude and easy-to-use unmanned aerial systems (UAS/drones) provide ever-improving data sources for the generation of detailed 3-dimensional (3D) data using digital photogrammetric methods with dense image matching. Today both data sources represent cost-effective alternatives to dedicated airborne sensors, especially for remote regions. The latest generation of EO satellites can collect VHR imagery up to 0.30 m ground sample distance (GSD) of even the most remote location from different viewing angles many times per year. Consequently, well-chosen scenes from growing image archives enable the generation of high-resolution digital elevation models (DEMs). Furthermore, low-cost and easy to use drones can be quickly deployed in remote regions to capture blocks of images of local areas. Dense point clouds derived from these methods provide an invaluable data source to fill the gap between globally available low-resolution DEMs and highly accurate terrestrial surveys. Here we investigate the use of archived VHR satellite imagery with approx. 0.5 m GSD as well as low-altitude drone-based imagery with average GSD of better than 0.03 m to generate high-quality DEMs using photogrammetric tools over Tristan da Cunha, a remote island in the South Atlantic Ocean which lies beyond the reach of current commercial manned airborne mapping platforms. This study explores the potentials and limitations to combine this heterogeneous data sources to generate improved DEMs in terms of accuracy and resolution. A cross-validation between low-altitude airborne and spaceborne data sets describes the fit between both optical data sets. No co-registration error, scale difference or distortions were detected, and a quantitative cloud-to-cloud comparison showed an average distance of 0.26 m between both data sets. Both point clouds were merged applying a conventional georeferenced approach. The merged DEM preserves the rich detail from the drone-based survey and provides an accurate 3D representation of the entire study area. It provides the most detailed model of the island so far, suitable to support practical and scientific applications. This study demonstrates that combination archived VHR satellite and low-altitude drone-based imagery provide inexpensive alternatives to generate high-quality DEMs.

Comparison of ASTER and Sentinel-2 spaceborne datasets for geological mapping: a case study from North-East Greenland

Spaceborne remote sensing is a suitable tool for early mineral exploration and surveying large areas of high Arctic environment in a fast and cost-effective manner. While spaceborne data have been used widely to map geology in arid areas, similar approaches for remotely-sensed geological mapping of Arctic environments is yet to be developed. Freely available spaceborne optical data provides detailed information of high-quality that could potentially reduce resource exploration risk in remote regions. To this end, this study compares the use of two different multispectral spaceborne datasets (i.e. the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2) to map geological units in and around Wollaston Forland, North-East Greenland – an area rich in Jurassic and Cretaceous sedimentary rocks and important targets for offshore petroleum exploration. Multispectral image sensors simultaneously capture image data within multiple wavelength ranges (bands) across the electromagnetic spectrum. Each band is commonly described by the band number and the band wavelength centre position. Here, we identify the bands most suitable for geological mapping in an Arctic setting, using the Wollaston Forland area as an example. We compare the results obtained by processing spaceborne data with a published geological map for the area (Henriksen 2003).

Sugarcane Productivity Mapping through C-Band and L-Band SAR and Optical Satellite Imagery

Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of São Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity.

Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging

Soil Organic Carbon (SOC) is a useful representative of soil fertility and an essential parameter in controlling the dynamics of various agrochemicals in soil. Soil texture is also used to calculate soil's ability to retain water for plant growth. SOC and soil texture are thus important parameters of agricultural soils and need to be regularly monitored. Optical satellite remote sensing offers the potential for frequent surveys over large areas. In addition, the recently-operated Sentinel-2 missions provide free imagery. This study compared the capabilities of Sentinel-2 for monitoring and mapping of SOC and soil texture (clay, silt and sand content) with those obtained from airborne hyperspectral (CASI/SASI sensors) and lab ASD FieldSpec spectroradiometer measurements at four agricultural sites in the Czech Republic. Combination of 10 extracted bands of the Sentinel-2 and 18 spectral indices, as independent variables, were used to train prediction models and then produce spatial distribution maps of the selected attributes. Results showed that the prediction accuracy based on lab spectroscopy, airborne and Sentinel-2 in the majority of the sites was adequate for SOC and fair for clay; however, Sentinel-2 imagery could not be used to detect and map variations in silt and sand. The SOC and clay maps derived from the airborne and spaceborne datasets showed similar trend, with both performing better where SOC levels were relatively high, though at the highest levels Sentinel-2 was able to create the SOC map more precisely than the airborne sensors. Taken across all SOC levels measured in the reference data, Sentinel-2 results were marginally lower than lab spectroscopy and airborne imagery, but this reduction in precision may be offset by the extensive geographical coverage and more frequent revisit characteristic of satellite observation. The increased temporal revisit and area are expected to be positive enhancements to the acquisition of high-quality information on variations in SOC and clay content of bare soils.

Potential of Space-Borne PolInSAR for Forest Canopy Height Estimation Over India—A Case Study Using Fully Polarimetric L-, C-, and X-Band SAR Data

This paper aims to demonstrate the potential of space-borne datasets for forest height estimation over Indian tropical forests. Fully polarimetric space-borne SAR interferometry (PolInSAR) data is acquired at the L -, C -, and X -band frequencies. The X -band data are acquired in two modes—single pass and repeat pass. The datasets are compensated for decorrelation due to SNR and varying spatial baselines. The remaining major decorrelation is the volumetric decorrelation, which is modeled using random volume over ground model to estimate the PolInSAR height for all the three frequencies. The modified Three-stage inversion algorithm is utilized for forest stand height estimation. The effect of forest biomass on height inversion accuracy is assessed. Furthermore, a first-order estimate of the absolute temporal decorrelation is demonstrated for the repeat-pass space-borne PolInSAR datasets. Extensive field validation campaigns are carried out in the tropical forest ranges. The forest height is inverted using data at all the three frequencies and validated with field measured values. The zero-temporal baseline bistatic TerraSAR-X/TanDEM-X and the L -band ALOS-2/PALSAR-2 result in a good height inversion with ${\boldsymbol{r}^2}$ and RMSE of 0.77 and 1.86 m ( X -band) and 0.75 and 1.94 m ( L -band), respectively. Furthermore, a comparison of estimated height of ALOS-2/PALSAR-2, TerraSAR-X, and RadarSAT-2 has been done with respect to the estimated height of TerraSAR-X/TanDEM-X. It is observed that RMSE of height inversion with respect to TerraSAR-X/TanDEM-X height is found to be 5.4 m, 7.6 m, and 12.8 m for ALOS-2/PALSAR-2, RadarSAT-2, and TerraSAR-X, respectively.

Speckle Filtering and its Influence on the Decomposition and Classification of Hybrid Polarimetric Data of RISAT-1

Decomposition and classification are crucial steps in the PolSAR (polarimetric synthetic aperture radar) data processing. Speckle noise influences synthetic aperture radar data because of the coherent integration of back scattered signals from various targets. This study examined the influence of speckle noise filtering on the classification and decomposition of hybrid polarimetric data of Radar Imaging Satellite-1 (RISAT-1). A despeckling algorithm should suppress speckle noise and simultaneously maintain the polarimetric data and spatial features. Performances of the boxcar, Lee-sigma, intensity-driven adaptive-neighborhood (IDAN), and refined Lee filters were evaluated using a spaceborne dataset. RISAT‐1 C‐band hybrid polarimetric data of the Mumbai region, India, was utilized. This study addressed the influence of speckle filtering on decomposition. Various speckle suppression techniques were applied on the RISAT-1 dataset, and the m−χ decomposition was subsequently conducted on the dataset. The obtained results indicated that the IDAN filter exhibited a superior improvement in double-bounce scattering in the urban area and in diffuse scattering in the forest area compared with the other filters considered in this study. Furthermore, the effectiveness of various speckle filters was evaluated. The Wishart supervised classification was applied on the filtered and unfiltered data to evaluate the effectiveness of the speckle suppression algorithm. The refined Lee, boxcar, Lee-sigma, and IDAN filters were evaluated for improving their classification accuracy. The classification accuracy of the settlement and mangrove classes was considerably improved. A marginal improvement was observed for the forest and bare soil classes. The improvement in the classification accuracy of the water class was insignificant.

Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution

ABSTRACTFor remote-sensing applications such as spectra classification or identification, atmospheric correction constitutes a very important pre-processing step, especially in complex urban environments where a lot of phenomenons alter the shape of the signal. The objective of this article is to compare the efficiency of two atmospheric correction algorithms, COCHISE (atmospheric COrrection Code for Hyperspectral Images of remote-sensing SEnsors) and an empirical method, on hyperspectral data and for classification applications. Classification is carried out on several simulated spaceborne data sets with different spatial resolutions (from 1.6 to 9.6 m). Four classifiers are considered in the study: a k-means, a Support Vector Machine (SVM), and a sun/shadow version of each of them, which processes sunlit and shadowed pixels separately. Results show that the most relevant atmospheric method for classification depends on the spatial resolution of the processed data set. Indeed, if the empirical method performs better on high-resolution data sets (up to 4%), its superiority fades out as the spatial resolution decreases, especially with the lower spatial resolution where COCHISE can be 10% more accurate than the empirical method.

Spatially Regularized Fusion of Multiresolution Digital Surface Models

In this paper, we propose an algorithm for robustly fusing digital surface models (DSMs) with different ground sampling distances and confidences, using explicit surface priors to obtain locally smooth surface models. Robust fusion of the DSMs is achieved by minimizing the L1-distance of each pixel of the solution to each input DSM. This approach is similar to a pixel-wise median, and most outliers are discarded. We further incorporate local planarity assumption as an additional constraint to the optimization problem, thus reducing the noise compared with pixel-wise approaches. The optimization is also inherently able to include weights for the input data, therefore allowing to easily integrate invalid areas, fuse multiresolution DSMs, and to weight the input data. The complete optimization problem is constructed as a variational optimization problem with a convex energy functional, such that the solution is guaranteed to converge toward the global energy minimum. An efficient solver is presented to solve the optimization in reasonable time, e.g., running in real time on standard computer vision camera images. The accuracy of the algorithms and the quality of the resulting fused surface models are evaluated using synthetic data sets and spaceborne data sets from different optical satellite sensors.

A Comparison of Mangrove Canopy Height Using Multiple Independent Measurements from Land, Air, and Space.

Canopy height is one of the strongest predictors of biomass and carbon in forested ecosystems. Additionally, mangrove ecosystems represent one of the most concentrated carbon reservoirs that are rapidly degrading as a result of deforestation, development, and hydrologic manipulation. Therefore, the accuracy of Canopy Height Models (CHM) over mangrove forest can provide crucial information for monitoring and verification protocols. We compared four CHMs derived from independent remotely sensed imagery and identified potential errors and bias between measurement types. CHMs were derived from three spaceborne datasets; Very-High Resolution (VHR) stereophotogrammetry, TerraSAR-X add-on for Digital Elevation Measurement, and Shuttle Radar Topography Mission (TanDEM-X), and lidar data which was acquired from an airborne platform. Each dataset exhibited different error characteristics that were related to spatial resolution, sensitivities of the sensors, and reference frames. Canopies over 10 m were accurately predicted by all CHMs while the distributions of canopy height were best predicted by the VHR CHM. Depending on the guidelines and strategies needed for monitoring and verification activities, coarse resolution CHMs could be used to track canopy height at regional and global scales with finer resolution imagery used to validate and monitor critical areas undergoing rapid changes.

Area, elevation and mass changes of the two southernmost ice caps of the Canadian Arctic Archipelago between 1952 and 2014

Abstract. Grinnell and Terra Nivea Ice Caps are located on the southern Baffin Island, Nunavut, in the Canadian Arctic Archipelago. These relatively small ice caps have received little attention compared to the much larger ice masses further north. Their evolution can, however, give valuable information about the impact of the recent Arctic warming at lower latitudes (i.e. ~ 62.5° N). In this paper, we measure or estimate historical and recent changes of area, elevation and mass of both ice caps using in situ, airborne and spaceborne data sets, including imagery from the Pléiades satellites. The area of Terra Nivea Ice Cap has decreased by 34 % since the late 1950s, while that of Grinnell Ice Cap has decreased by 20 % since 1952. For both ice caps, the areal reduction accelerated at the beginning of the 21st century. The estimated glacier-wide mass balance was −0.37 ± 0.21 m a−1 water equivalent (w.e.) over Grinnell Ice Cap for the 1952–2014 period, and −0.47 ± 0.16 m a−1 w.e. over Terra Nivea Ice Cap for the 1958/59–2014 period. Terra Nivea Ice Cap has experienced an accelerated rate of mass loss of −1.77 ± 0.36 m a−1 w.e. between 2007 and 2014. This rate is 5.9 times as negative when compared to the 1958/59–2007 period (−0.30 ± 0.19 m a−1 w.e.) and 2 times as negative when compared to the mass balance of other glaciers in the southern parts of Baffin Island over the 2003–2009 period. A similar acceleration in mass loss is suspected for the Grinnell Ice Cap, given the calculated elevation changes and the proximity to Terra Nivea Ice Cap. The recent increase in mass loss rates for these two ice caps is linked to a strong near-surface regional warming and a lengthening of the melt season into the autumn that may be indirectly strengthened by a later freezing of sea ice in the Hudson Strait sector. On a methodological level, our study illustrates the strong potential of Pléiades satellite data to unlock the under-exploited archive of old aerial photographs.

High-resolution forest canopy height estimation in an African blue carbon ecosystem.

Mangrove forests are one of the most productive and carbon dense ecosystems that are only found at tidally inundated coastal areas. Forest canopy height is an important measure for modeling carbon and biomass dynamics, as well as land cover change. By taking advantage of the flat terrain and dense canopy cover, the present study derived digital surface models (DSMs) using stereo‐photogrammetric techniques on high‐resolution spaceborne imagery (HRSI) for southern Mozambique. A mean‐weighted ground surface elevation factor was subtracted from the HRSI DSM to accurately estimate the canopy height in mangrove forests in southern Mozambique. The mean and H100 tree height measured in both the field and with the digital canopy model provided the most accurate results with a vertical error of 1.18–1.84 m, respectively. Distinct patterns were identified in the HRSI canopy height map that could not be discerned from coarse shuttle radar topography mission canopy maps even though the mode and distribution of canopy heights were similar over the same area. Through further investigation, HRSI DSMs have the potential of providing a new type of three‐dimensional dataset that could serve as calibration/validation data for other DSMs generated from spaceborne datasets with much larger global coverage. HSRI DSMs could be used in lieu of Lidar acquisitions for canopy height and forest biomass estimation, and be combined with passive optical data to improve land cover classifications.

Integrated active fire retrievals and biomass burning emissions using complementary near-coincident ground, airborne and spaceborne sensor data

Ground, airborne and spaceborne data were collected for a 450ha prescribed fire implemented on 18 October 2011 at the Henry W. Coe State Park in California. The integration of various data elements allowed near-coincident active fire retrievals to be estimated. The Autonomous Modular Sensor-Wildfire (AMS) airborne multispectral imaging system was used as a bridge between ground and spaceborne data sets providing high-quality reference information to support satellite fire retrieval error analyses and fire emissions estimates. We found excellent agreement between peak fire radiant heat flux data (<1% error) derived from near-coincident ground radiometers and AMS. Both MODIS and GOES imager active fire products were negatively influenced by the presence of thick smoke, which was misclassified as cloud by their algorithms, leading to the omission of fire pixels beneath the smoke, and resulting in the underestimation of their retrieved fire radiative power (FRP) values for the burn plot, compared to the reference airborne data. Agreement between airborne and spaceborne FRP data improved significantly after correction for omission errors and atmospheric attenuation, resulting in as low as 5% difference between Aqua/MODIS and AMS. Use of in situ fuel and fire energy estimates in combination with a collection of AMS, MODIS, and GOES FRP retrievals provided a fuel consumption factor of 0.261kgMJ−1, total energy release of 14.5e+06MJ, and total fuel consumption of 3.8e+06kg. Fire emissions were calculated using two separate techniques, resulting in as low as 15% difference for various species.

FUSION OF MULTI-RESOLUTION DIGITAL SURFACE MODELS

Abstract. This paper proposes an algorithm for fusing digital surface models (DSM) obtained by heterogenous sensors. Based upon prior confidence knowledge, each DSM can be weighted locally adaptively and therefore strengthen or lessen its influence on the fused result. The proposed algorithm is based on variational methods of first and second order, minimizing a global energy functional comprising of a data term forcing the resulting DSM being similar to all of the input height information and incorporating additional local smoothness constraints. By applying these additional constraints in the form of favoring low gradients in the spatial direction, the surface model is forced to be locally smooth and in contrast to simple mean or median based fusion of the height information, this global formulation of context-awareness reduced the noise level of the result significantly. Minimization of the global energy functional is done with respect to the L1 norm and therefore is robust to large height differences in the data, which preserves sharp edges and fine details in the fused surface model, which again simple mean- and median-based methods are not able to do in comparable quality. Due to the convexity of the framed energy functional, the solution furthermore is guaranteed to converge towards the global energy minimum. The accuracy of the algorithms and the quality of the resulting fused surface models is evaluated using synthetic datasets and real world spaceborne datasets from different optical satellite sensors.