Ground Truth Sites Research Articles

Application of Sentinel-1 InSAR to monitor tailings dams and predict geotechnical instability: practical considerations based on case study insights

Tailings storage facilities (TSFs) impound mining waste behind dams to ensure public safety, but failure incidents have prompted calls for more robust monitoring programs. Satellite-based interferometric synthetic aperture radar (InSAR) has grown in popularity due to its ability to remotely detect millimeter-scale displacements in most urban and some natural terrains. However, there remains a limited understanding of whether InSAR can be as accurate or representative as on-the-ground instruments, whether failures can be predicted in advance using InSAR, and what variables govern the quality and reliability of InSAR results. To address these gaps, we analyze open-source, medium-resolution Sentinel-1 data to undertake a ground-truth assessment at a test site and a forensic analysis of five failure cases. We use a commercial software with an automated Persistent Scatterer (PS) workflow (SARScape Analytics) for all case study sites except one and a proprietary algorithm (SqueeSAR) with a dual PS and Distributed Scatterer (DS) algorithm for the ground-truth site and one forensic case. The main goal is to deliver practical insights regarding the influence of algorithm/satellite selection, environmental conditions, site activity, coherence thresholds, satellite-dam geometry, and failure modes. We conclude that Sentinel-1 InSAR can serve as a hazard-screening tool to help guide where to undertake targeted investigations; however, most potential failure modes may not exhibit InSAR-detectable accelerations that could assist with time-of-failure prediction in real time. As such, long-term monitoring programs should ideally be integrated with a combination of remote sensing and field instrumentation to best support engineering practice and judgment.

Lunar Surface Resource Exploration: Tracing Lithium, 7 Li and Black Ice Using Spectral Libraries and Apollo Mission Samples

This is an exercise to explore the concentration of lithium, lithium-7 isotope and the possible presence of black dirty ice on the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark ice on the lunar surface is closely related to future human settlement missions on the moon. We investigate the distribution of lithium and 7 Li isotope on the lunar surface by employing spectral data from the Clementine images. We utilized visible (VIS–NIR) imagery at wavelengths of 450, 750, 900, 950 and 1000 nm, along with near-infrared (NIR–SWIR) at 1100, 1250, 1500, 2000, 2600 and 2780 nm, encompassing 11 bands in total. This dataset offers a comprehensive coverage of about 80% of the lunar surface, with resolutions ranging from 100 to 500 m, spanning latitudes from 80°S to 80°N. In order to extract quantitative abundance of lithium, ground-truth sites were used to calibrate the Clementine images. Samples (specifically, 12045, 15058, 15475, 15555, 62255, 70035, 74220 and 75075) returned from Apollo missions 12, 15, 16 and 17 have been correlated to the Clementine VIS–NIR bands and five spectral ratios. The five spectral ratios calculated synthesize the main spectral features of sample spectra that were grouped by their lithium and 7 Li content using Principal Component Analysis. The ratios spectrally characterize substrates of anorthosite, silica-rich basalts, olivine-rich basalts, high-Ti mare basalts and Orange and Glasses soils. Our findings reveal a strong linear correlation between the spectral parameters and the lithium content in the eight Apollo samples. With the values of the 11 Clementine bands and the 5 spectral ratios, we performed linear regression models to estimate the concentration of lithium and 7 Li. Also, we calculated Digital Terrain Models (Altitude, Slope, Aspect, DirectInsolation and WindExposition) from LOLA-DTM to discover relations between relief and spatial distribution of the extended models of lithium and 7 Li. The analysis was conducted in a mask polygon around the Apollo 15 landing site. This analysis seeks to uncover potential 7 Li enrichment through spallation processes, influenced by varying exposure to solar wind. To explore the possibility of finding ice mixed with regolith (often referred to as `black ice’), we extended results to the entire Clementine coverage spectral indices, calculated with a library (350–2500 nm) of ice samples contaminated with various concentrations of volcanic particles.

Low sensitivity of Pinus mugo to surface ozone pollution in the subalpine zone of continental Europe.

High altitudes have been exposed to enhanced levels of surface ozone (O3) concentrations over recent decades compared to the pre-industrial era. The responses of vegetation to this toxic pollutant are species-specific and depend on the climate conditions. In this paper, we explored the reaction of Pinus mugo (P. mugo) to O3-induced stress in the continental climate of an ozone-rich mountain area in the High Tatra Mountains (Western Carpathians). The effects of O3 doses modelled by a deposition model, O3 concentrations and other factors on P. mugo were identified from (a) satellite-based data via NDVI (normalised differenced vegetation index) over 2000-2020 and (b) visible injury on needle samples gathered from P. mugo individuals at ground-truth sites in 2019 and 2020. Analysing the NDVI trend, we observed non-significant changes (p > 0.05) in the greenness of P. mugo despite growing in an environment with the average seasonal O3 concentration around 51.6 ppbv, the maximum hourly concentrations more than 90 ppbv and increasing trend of O3 doses by 0.1mmolm-2 PLA (plant leaf area) year-1. The visible O3 injury of samples collected at study sites was low (mean injury observed on 1-10% of needles' surface), and the symptoms of injury caused by other biotic and abiotic factors prevailed over those caused by O3. In addition, the correlation analyses between NDVI and the climatic factors indicated a significant (p < 0.05) and positive relationship with photosynthetic active radiation (R = 0.45) in July, and with stomatal conductance (R = 0.52) and temperature factor (R = 0.43) in August. Therefore, we concluded that the positive effect of climate conditions, which support the growth processes of P. mugo, may suppress the negative effect of the mean O3 doses of 17.8mmolm-2 PLA accumulated over the growing season.

Analyzing Satellite Ocean Color Match-Up Protocols Using the Satellite Validation Navy Tool (SAVANT) at MOBY and Two AERONET-OC Sites

The satellite validation navy tool (SAVANT) was developed by the Naval Research Laboratory to help facilitate the assessment of the stability and accuracy of ocean color satellites, using numerous ground truth (in situ) platforms around the globe and support methods for match-up protocols. The effects of varying spatial constraints with permissive and strict protocols on match-up uncertainty are evaluated, in an attempt to establish an optimal satellite ocean color calibration and validation (cal/val) match-up protocol. This allows users to evaluate the accuracy of ocean color sensors compared to specific ground truth sites that provide continuous data. Various match-up constraints may be adjusted, allowing for varied evaluations of their effects on match-up data. The results include the following: (a) the difference between aerosol robotic network ocean color (AERONET-OC) and marine optical Buoy (MOBY) evaluations; (b) the differences across the visible spectrum for various water types; (c) spatial differences and the size of satellite area chosen for comparison; and (d) temporal differences in optically complex water. The match-up uncertainty analysis was performed using Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) SNPP data at the AERONET-OC sites and the MOBY site. It was found that the more permissive constraint sets allow for a higher number of match-ups and a more comprehensive representation of the conditions, while the restrictive constraints provide better statistical match-ups between in situ and satellite sensors.

Diagnosis of degraded pastures using an improved NDVI-based remote sensing approach: An application to the Environmental Protection Area of Uberaba River Basin (Minas Gerais, Brazil)

Pasture degradation represents a global environmental problem that urges mitigation. A fundamental step towards restoration of degraded pastures is the identification and accurate mapping of these areas. In Brazil, the area of degraded pastures is immense and therefore remote sensing is a cost-effective way to map it. In this study, an improved method based on NDVI values extracted from satellite images is presented, and tested in the Environmental Protection Area of Uberaba River Basin (EPAURB) located in the state of Minas Gerais, Brazil. The EPAURB covers an area of approximately 528.1 km2, 50.9% of which is pasture. The innovative features of this method comprise: 1) the mapping is preceded by the definition of NDVI fingerprints for healthy, smoothly degraded, moderately degraded and degraded pasture (called physiognomies), based on non linear relationships between NDVI values and time; 2) the mapping of physiognomies accounts for the influence of geology and weather seasonality on the NDVI values. In the EPAURB the physiognomic categories were set by visual inspection and evaluation of soil characteristics (e.g., organic matter, nutrients, resistance to penetration) in the so-called characterization ground truth sites also termed buffers. Resistance to penetration and several other soil parameters showed statistically different (p ≤ 0.05) values among physiognomies. The definition of fingerprints was based on a 4-year record (2013–2016) of NDVI 16-day composite (MOD13Q1) 250 m time-series data. The map of degraded pastures was delineated on the basis of comparisons between the NDVI values of 23 satellite images covering the year of 2016 (termed NDVIpixel) and corresponding characteristic NDVI values of degraded pasture physiognomy extracted from the corresponding fingerprint (termed NDVIbuffer). Whenever NDVIbuffer,min ≤ NDVIpixel ≤ NDVIbuffer,max a repetition counter (n) increased one unit. For n ≥ 3 the pixel was classified as degraded pasture. The results exposed 160.1 km2 of degraded pasture for 3 ≤ n ≤ 18, which represents 60% of all pasture land. The areas mapped as degraded pasture were subject to a field check in 38 so-called validation ground truth sites, using resistance to penetration as validation parameter, with 84.1% success. Given the serious environmental damage posed by pasture degradation, several mitigation measures were discussed including the protection of degraded soil through the “polluter pays principle”.

Mineralogy and chemistry of Ti-bearing lunar soils: Effects on reflectance spectra and remote sensing observations

This paper presents results of coordinated ultraviolet and visible wavelength reflectance measurements, X-ray diffraction analyses of mineral components, and micro X-ray fluorescence analyses of Ti concentrations of 13 lunar soil samples (<210 µm) that span a range of maturity and TiO2 contents. Results of these analyses are used to determine the effects of soil maturity, TiO2 concentration, and specific mineralogical makeup, especially ilmenite content, on the ultraviolet/visible (UV/VIS) ratio for application to remote sensing observations of the Moon and other airless bodies. We find that measured ilmenite weight percent correlates highly with measured TiO2 concentrations. Thus, the ilmenite content is a good predictor of TiO2 concentration. Ilmenite is the main contributor of TiO2 for soils with greater than about 2 wt.% TiO2, so we take the effects of TiO2 on reflectance spectra to be essentially those of ilmenite. Constraining the data set to eight mature Apollo soils, we find that among the UV/VIS ratios from laboratory-measured spectra, the 321/415 nm ratio shows the best correlation with TiO2 and ilmenite. Moreover, for soils with similar maturity in the submature to mature range, those with higher TiO2 have higher 321/415 UV/VIS ratios. Finally, the correlation between TiO2 content and 321/415 ratio in samples measured in the lab appears weaker than for the same relationship using the Lunar Reconnaissance Orbiter (LRO) Wide Angle Camera (WAC) spectral data for the 321/415 ratio of Apollo ground-truth sites. The correlation between lab-derived 321/415 ratios and TiO2 content for measured samples improves when low-maturity samples are excluded from the dataset, implying that the LROC WAC spectra at 400 m/pix spatial resolution senses mostly mature soil.

Developing indices of temporal dispersion and continuity to map natural vegetation

An accurate and updated natural vegetation map is imperative for sustainable environmental management. This paper proposed a novel natural vegetation mapping algorithm based on time series images. Several indices of temporal dispersion and continuity were established for this purpose: low density (LD), medium density (MD), high density (HD) and medium continuity (MC). These indices were developed based on the particular percentiles-determined section of the EVI2 temporal profiles obtained through continuous wavelet transform. The natural vegetation was generally characterized as with lower temporal dispersion and greater temporal continuity compared with agricultural crops. The proposed methodology incorporated the indices of temporal dispersion and continuity and was applied to 13 provinces in central East China based on 500m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index with two bands (EVI2) in 2013. An overall accuracy of 92.97% was obtained when compared with 2715 ground truth sites. There was also a good agreement (kappa index=0.8049) on the distribution and areas of different vegetation types between the MODIS-estimated image and the Landsat 8 OLI interpreted data on two test regions. This study demonstrated the efficiency of the transform and metric integrated time series classification approaches in the fields of land and vegetation cover mapping.

Remote sensing-based assessment of impact of Phailin cyclone on rice in Odisha, India

Assessing the nature and extent of damage due to natural calamities remains one of the thrust areas in monitoring resource inventory through remote sensing. The effect of the cyclone Phailin and the post-incessant rains during second fortnight of October 2013 on coastal Odisha was studied in terms of rice area flooded, submerged and damaged. Multi-temporal SAR data were analysed to obtain the rice mask, and from this rice mask, the flood affected rice area was determined. Taluka-wise and district-wise crop loss proportion was estimated, and the overall production loss has been estimated. SAR data aided in delineation of flooded regions, while AWiFS NDVI data of subsequent dates showed both continued inundation and crop vigour status post-flood time period. The ground truth indicated that a major portion of the inundated region was not rice but was typha grasses and harvested rice field which should not be accounted as damage to rice crop. The damage on crop yield was difficult to assess; however, the inundation of the crop at panicle initiation and flowering would have impact on grain filling (results in chaffiness) and was considered as completely damaged. Most of the current inundated rice regions fall in this category. It was estimated that a total of 0.167 million hectares and 0.37 million tons of rice crop was lost in the cyclone and floods. The district-level percentage area of rice flooded was communicated to State Remote Sensing Centre in four days timeframe. The overall accuracy obtained for the validation of the ground truth sites was 91.5 %.

SOIL FERTILITY EVALUATION FOR FERTILISER RECOMMENDATION USING HYPERION DATA

Abstract. Soil fertility characterised by nitrogen, phosphorus, potassium, calcium, magnesium and sulphur is traditionally measured from soil samples collected from the field. The process is very cumbersome and time intensive. Hyperspectral data available from Hyperion payload of EO 1 was used for facilitating preparation of soil fertility map of Udaipur district of Rajasthan state, India. Hyperion data was pre-processed for band and area sub setting, atmospheric correction and reflectance data preparation. Spectral analysis in the form of SFF and PPI were carried out for selecting the ground truth sites for soil sample collection. Soil samples collected from forty one sites were analysed for analysis of nutrient composition. Generation of correlogram followed by multiple regressions was done for identifying the most important bands and spectral parameters that can be used for nutrient map generation.

Benthic habitat β-diversity modeling and landscape metrics for the selection of priority conservation areas using a systematic approach: Magdalena Bay, Mexico, as a case study

Albeit the need and importance of protected areas worldwide, there is no best practice methodology for their placement and design as this depends on several factors such as resource availability, data disponibility, and technical capacity. In this study, we show a novel approach on the use of β-diversity modeling and landscape metrics for the selection of priority conservation areas to assist the management of Magdalena Bay, one of the most important bays of Mexico. Quantitative estimates on the percentage of cover of benthic substrates from 60 ground truthed sites were used to perform a cluster analysis using the Bray–Curtis similarity index. Seven habitats were recognized, with habitats characterized by an inter-mix of sand, seagrass, macroalgae and rock substrates. A thematic map of benthic habitats was produced utilizing a supervised classification with the maximum likelihood rule on a natural color mosaic of high resolution satellite scenes (overall accuracy 80%, Kappa = 0.69). Matlab was used to calculate β-diversity values based on the nature and distribution of habitats. A weighted overlay analysis considering habitat type, patch area/perimeter2 ratio and β-diversity was performed to prioritize areas for conservation. Three areas, which constitute ∼20% of the shallow benthic landscape, are proposed to be considered within a Marine Protected Area scheme. By selecting sites with these landscape characteristics we developed a cost-effective systematic approach to protect the natural processes responsible for maintaining diversity and the livelihoods of the local communities.

The use of medium point density LiDAR elevation data to determine plant community types in Baltic coastal wetlands

This study investigates the use of medium point density LiDAR (light detection and ranging) elevation data for identifying and locating plant community types in Baltic coastal wetlands. Ground-surveyed micro-topography data obtained using real-time kinematic differential GPS were used to analyse and correct the accuracy of LiDAR sourced elevation values in Matlab R2010a. Following the application of elevation corrections, 3 interpolation techniques were applied: Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and the Delaunay Triangulated Irregular Network (TIN) interpolation. Analysis showed that the TIN method produced a more robust model that adequately represented the micro-topographical variation across the wetland. The TIN elevation interpolation was then used to characterise the extent and distribution of the plant communities using known elevation preferences in one coastal wetland site, Tahu. The plant community model developed from the TIN elevation interpolation was then further ground-truthed in two separate Baltic coastal wetland sites, Matsalu and Kudani, in order to test model validity.The results showed that the accuracy of the LiDAR data varied dependent upon plant community type. In 5 of the 6 studied community types, the LiDAR data gave a mean elevation difference of 0.177m (0.016SD) above the dGPS elevations. With dGPS elevation corrections, the LiDAR data produced an accurate model of the location of the different plant community types, Kappa coefficient (κ) 0.63, at the initial site, Tahu. The model was found to be very accurate in one of the subsequent ground-truthed sites, κ 0.81, with a similar management history, but less accurate, κ 0.53, in a wetland more exposed to wave action and with a longer history of vegetation management.The inclusion of dGPS ground survey data was shown to considerably improve the accuracy of the LiDAR elevation data to facilitate the identification of plant community types on the basis of elevation. The approach developed in this study of Baltic coastal wetlands may provide a transferable method for rapidly and accurately mapping plant communities in other open environments.

Validation of GOES-R Satellite Land Surface Temperature Algorithm Using SURFRAD Ground Measurements and Statistical Estimates of Error Properties

Validation of satellite land surface temperature (LST) is a challenge because of spectral, spatial, and temporal variabilities of land surface emissivity. Highly accurate in situ LST measurements are required for validating satellite LST products but are very hard to obtain, except at discrete points or for very short time periods (e.g., during field campaigns). To compare these field-measured point data with moderate-resolution (1 km) satellite products requires a scaling process that can introduce errors that ultimately exceed those in the satellite-derived LST products whose validation is sought. This paper presents a new method of validating the Geostationary Operational Environmental Satellite (GOES) R-Series (GOES-R) Advanced Baseline Imager (ABI) LST algorithm. It considers the error structures of both ground and satellite data sets. The method applies a linear fitting model to the satellite data and coregistered “match-up” ground data for estimating the precisions of both data sets. In this paper, GOES-8 Imager data were used as a proxy of the GOES-R ABI data for the satellite LST derivation. The in situ data set was obtained from the National Oceanic and Atmospheric Administration's SURFace RADiation (SURFRAD) budget network using a stringent match-up process. The data cover one year of GOES-8 Imager observations over six SURFRAD sites. For each site, more than 1000 cloud-free match-up data pairs were obtained for day and night to ensure statistical significance. The average precision over all six sites was found to be 1.58 K, as compared to the GOES-R LST required precision of 2.3 K. The least precise comparison at an individual SURFRAD site was 1.8 K. The conclusion is that, for these ground truth sites, the GOES-R LST algorithm meets the specifications and that an upper boundary on the precision of the satellite LSTs can be determined.

Effects of hurricanes on the stability of reef-associated landscapes

The effects of Hurricane Emily (July 2005, Category 4) on nine different benthic substrates in the reef-associated landscape along the west coast of Cozumel (Mexico) were evaluated by comparing the cover of nine types of substrate on ground-truthed sites before (ten months) and after (two months) the hurricane. Four substrates showed significant differences (Wilcoxon signed-rank test, P &lt; 0.05). Coverage of sand and sand over rock increased, while that of seagrass and hard corals decreased. The effects of the hurricane on soft corals diminished below 10 m depth. Sites between 0.5 and 5.5 m were less affected towards the southwest of the island, where the reefs are more developed. Algal cover was similar to pre-hurricane values after two months, which suggests fast algal recolonization. Cozumel reefs are considered to be healthy; however, a possible increase in the abundance of macroalgae as a result of hurricanes coupled with poorly regulated tourism activities could eventually favor a phase shift in the system.

Search for cryptic Pseudomys shortridgei in suitable habitat in the south-east of South Australia

Pseudomys shortridgei has been recorded from a restricted and fragmented distribution across southern Australia. Fossil deposits represented the species in mainland South Australia. However, the discovery of an extant population of P. shortridgei in the lower south-east of South Australia, and its morphological similarity to the more common Rattus fuscipes and R. lutreolus highlighted the need to verify the current distribution of the species. Existing vegetation community and systematic baseline biological survey data were used in a fast, cost-effective and systematic desktop assessment to identify the distribution of potentially suitable habitat. Attributes within 12 spatial layers were assigned a value of ecological relevance, reclassified and combined using weighted overlay analysis in ESRI ArcGIS 9.1. At ground-truthed sites, randomly assigned to the most suitable habitat within the ‘equal weightings’ output, understorey had been poorly represented by available data and some vegetation community classifications were no longer current. Despite this limitation, potentially suitable habitat was located within three remnants and targeted in a small mammal survey. From 1459 trap-nights, only Antechinus flavipes, R. fuscipes, Isoodon obesulus obesulus, Austrelaps superbus and Mus musculus were captured. It is unlikely that P. shortridgei inhabits the remnants targeted; however, further field surveys targeting smaller remnants or vegetation communities poorly represented by existing data used in the desktop assessment are recommended.

Interannual vegetation phenology estimates from global AVHRR measurements: Comparison with in situ data and applications

In a previous paper, we described a procedure to correct the directional effects in AVHRR reflectance time series. The corrected measurements show much less high frequency variability than their original counterparts, which makes them suitable to study vegetation dynamics. The time series are used here to estimate the start and ending dates of the growing season for 18 years from 1982 to 1999. We focus on the interannual variations of these phenological parameters. A database of in situ phenology observations is used to quantify the accuracy of the satellite-based estimates. Although based on a limited sampling of the Northern mid and high latitudes, the comparison indicates that i) the satellite phenological product contains meaningful information on interannual onset anomalies; ii) there is a higher degree of consistency over regions covered by Broadleaf Forests, Grasses and cereal Crops than over those covered by Needleleaf Forests or Savannas; and iii) the satellite phenological product is of lower quality in regions with mountainous terrain. In favorable conditions, interannual variations of the onset are captured with an accuracy of a few days. As this satellite-derived product captures the interannual onset variability at ground-truth sites, we confidently use it to larger scales studies. Mapped at a continental scale, the onset anomalies show coherent patterns at the regional (≈ 1000 km) scale for the mid and high latitudes of the Northern hemisphere, which is consistent with a meteorological forcing. In the tropics, there is larger spatial heterogeneity, which suggests more complex controls of the phenology. The relation between vegetation phenology and climate is further investigated over Europe by comparing the variability of the satellite-derived vegetation onset and that of the winter North Atlantic Oscillation index, at a fine spatial scale. The strong correlations observed confirm that this climate forcing parameter explains most of the onset variability over a large fraction of Northern Europe (earlier onsets for positive winter NAO), with lower impact towards the south and opposite effects around the Mediterranean basin. The NAO has a predictive character as the January–February NAO index is strongly correlated with the vegetation onset that occurs around April in Northern Europe.

A remote sensing approach to estimating harvestable kelp biomass

Regulations of the Alaska Department of Fish and Game require that all fisheries in the state have a harvest management plan. In southeast Alaska two species of floating kelps, Nereocystis luetkeana and Alaria fistulosa, have been commercially harvested since 1992 for use as agrochemicals by the Alaska Kelp Company. However, there is currently no harvest management plan for this fishery. The lack of a formalized management plan is one factor that has kept the kelp industry from expanding in the state. We have employed an aerial digital multispectral imaging system (DMSC) calibrated with ground truthing for performing such an assessment. The system can be flown at varying altitudes to achieve spatial resolutions ranging from 0.5 to 2 m. Rapid ground truthing techniques were developed using morphometric measurements to predict biomass. Analysis of the DMSC imagery showed that good correlations could be developed between the multispectral imagery and kelp biomass estimates collected at the ground-truth sites. Repeatable estimates of kelp bed area derived from the multispectral imagery could be made at varying tidal levels. However, broad scale maps of kelp biomass suitable for estimating harvest rates could not be made at different tide levels. Multispectral imagery suitable for this purpose must be collected at a standard tidal level.

白神山地・泊の平地区における航空ハイパースペクトルセンサデータを用いた植生のマッピング

Vegetation was classified using aerial hyperspectral sensor (CASI-3) data and field survey data for the beech-forested Shirakami Mountains in Aomori Prefecture, Japan. Previous study on German forest reported that different plants show similar hyperspectral response patterns and that the accuracy of vegetation classification using aerial hyperspectral sensor data alone was 66%. In this study, radiance in each grid was divided by average radiance in a vegetation-coverage area. This“normalized”spectral response pattern enabled finer plant discrimination than the non-normalized patterns used in the previous study.Plants typifying those in the study area were selected, their radiances were investigated at ground truth sites, and eight vegetation classifications were selected : walnut (high and low radiance), beech (high and low radiance), altherbosa, dwarf bamboo, bare ground, and other. Classification accuracy was 77.6% at the ground truth sites, but the classification accuracy for each of altherbosa, bamboo grass, and bare ground was 100%. Walnut classification accuracies (63.6% for high radiance and 50.0% for low radiance) were equal to or lower than beech classification accuracies (50.0% for high radiance and 88.9% for low radiance) . When radiance is disregarded, classification accuracy was 76.5% for walnut and 80.0% for beech. The classification accuracy of 76.5% for walnut 80.0% for beech suggests that aerial hyperspectral sensor data can be used to map vegetation.

Observation of sea-ice thickness in the sea of Okhotsk by using dual-frequency and fully polarimetric airborne SAR (pi-SAR) data

To investigate the possibilities of using dual-frequency, multipolarization synthetic aperture radar (SAR) data to monitor sea ice, we derived the relationship between various polarization characteristics and the physical parameters of sea ice. We discuss the frequency and polarization characteristics of the backscattering coefficients of sea ice and then characterize its thickness by comparing the corresponding backscattering coefficient for each polarization with the physical parameters of the ice. We first propose a methodology for classifying sea-ice types by using a polarimetric decomposition technique, before comparing an estimation of the sea-ice thickness with the corresponding dual-frequency, multipolarization SAR data. We utilized the backscattering ratio to estimate the thickness of the sea ice. This ratio canceled the effect of roughness on the backscattering. The method was validated using Pi-SAR (polarimetric and interferometric airborne SAR) observation data obtained at ground-truth sites.

Seasonal variation of ocean bottom pressure derived from Gravity Recovery and Climate Experiment (GRACE): Local validation and global patterns

The Gravity Recovery and Climate Experiment (GRACE) processing centers at the GeoForschungsZentrum Potsdam (GFZ) and the University of Texas Center for Space Research (UTCSR) provide time series of monthly gravity field solutions covering the period since mission launch in March 2002. Although the achieved accuracy still remains an order of magnitude below the mission's baseline goal, these time series have successfully been used to study terrestrial phenomena such as water storage variations. Over the oceans, the monthly gravity field solutions can be converted into estimates of the fluctuating ocean bottom pressure (OBP), which is the sum of atmospheric and oceanic mass variations. The GRACE products may be validated against in situ OBP observations which are available from a ground truth site in the tropical northwest Atlantic Ocean. Large differences are observed between the in situ and GRACE‐derived OBP which are investigated by comparing the tidal and nontidal ocean models used at GFZ and UTCSR for dealiasing short‐term (&lt;2 months) mass variations from satellite measurements. Results show that the barotropic nontidal and tide models need improvement at periods shorter than 1 day and longer than 2 weeks. On a global scale the monthly OBP fields from GRACE generally overestimate the variability compared to ocean general circulation models, especially in tropical regions. This may be attributed to continuing deficiencies in GRACE data processing. Nevertheless, there is some initial evidence that GRACE possesses the potential to observe large‐scale averages of bottom pressure fluctuations.

Correlation of sidescan backscatter with grain size distribution of surficial seabed sediments

The dependence of acoustic backscatter on sediment grain size distribution is examined using dual frequency (100 and 410 kHz) sidescan sonar and 22 sediment grab samples from the Loch Linnhe artificial reef site on the west coast of Scotland. The sidescan data were processed to remove an empirically estimated average grazing angle dependence on backscatter. The processed data were analysed by forming histograms of pixels extracted from a 20 m 2 box around each ground truth site. A positive correlation ( r=0.73) between mean backscatter intensity and mean grain size was obtained, i.e., the coarsest samples had the brightest backscatter. A positive correlation ( r=0.59) was also found between the standard deviations of the backscatter and grain size distributions, i.e., poorly sorted sediments gave the most variable backscatter. The performance of the sidescan data was compared to results from a co-incident single-beam echo-sounder RoxAnn survey. The RoxAnn roughness index E1 compared well with the sidescan, whilst the RoxAnn hardness index E2 did not. This may be due to a physical link between the acoustic measures. The comparison showed the sidescan to have delivered a significantly higher-resolution image of the seabed for a similar amount of ship-time. Imaging of the artificial reef modules themselves was found to be frequency dependent.