Radiometric Resolution Research Articles

Performance-Enhanced Bolometric Terahertz Detectors Based on V2O5 for 15 to 30 THz

Terahertz (THz) radiation perception using uncooled detectors are gaining importance due to the increasing demands in the areas of military, space, and industrial, medical, and surveillance applications. In spite of the efforts of researchers to fill the THz gap, there exists a need for detectors in the range between 15 THz and 30 THz. In this paper, we discuss the development of bolometric detectors whose performance is enhanced by an optical immersion technique and their characterization in the aforesaid range of frequencies. These detectors are characterized by high specific detectivity (D*) of 1.28 × 109 cmHz1/2 W−1 and high radiometric resolution (noise-equivalent temperature difference = 26 mK) and are fast enough for bolometric detectors (time constant = 1.7 ms), which make them suitable for spectroscopic and imaging applications.

Wrapped Staring Spotlight SAR

This paper proposes the wrapped staring spotlight (WSS) SAR imaging mode, which is a new method to extend the azimuth steering capability for phased array SAR to achieve either an improved azimuth geometric or radiometric resolution. It investigates the utility of steering directions with main lobe gains that are smaller than that of the grating lobes and exposes how these directions can be exploited. Furthermore, two methods are proposed to reduce the speckle and the image noise at once, i.e., the Look-Normalized Pattern Correction and the Ω-weighting. Based on two example TerraSAR-X WSS acquisitions, the image performance of extended and point targets is discussed.

Multi-scale synthetic aperture radar remote sensing for archaeological prospection in Han Hangu Pass, Xin'an China

ABSTRACTIncreasing availability of multi-resolution synthetic aperture radar (SAR) data enabled us to demonstrate the benefits of the existing imaging solutions offered by radar satellites for purposes of archaeological prospection. In this study, mid-resolution L-band phased array type L-band synthetic aperture radar (PALSAR) to high-to-very-high-resolution X-band TerraSAR-X/TanDEM-X TerraSAR-X add-on for digital elevation measurements (TanDEM-X) (Stripmap/Staring Spotlight) images were synergistically applied for the archaeological prospection at the Han Hangu Pass, in Xin’an, China. First, the evidence and the stretching direction of the outer walls of the pass were identified from the temporal-averaged products of PALSAR and the terrain from TanDEM Co-registered Single-Look Data. Then, the spatial layout of archaeological monuments (previously documented as well as recently discovered) was observed in the visualization-enhanced Staring Spotlight TerraSAR-X imagery owing to its high spatial and radiometric resolution. The findings of this study highlight the effectiveness of using integrated multi-scale SAR remote sensing data from different platforms in archaeology.

Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space

The remote sensing of grass aboveground biomass (AGB) has gained considerable attention, with substantial research being conducted in the past decades. Of significant importance is their photosynthetic pathways (C3 and C4), which epitomizes a fundamental eco-physiological distinction of grasses functional types. With advances in technology and the availability of remotely sensed data at different spatial, spectral, radiometric and temporal resolutions, coupled with the need for detailed information on vegetation condition, the monitoring of C3 and C4 grasses AGB has received renewed attention, especially in the light of global climate change, biodiversity and, most importantly, food security. This paper provides a detailed survey on the progress of remote sensing application in determining C3 and C4 grass species AGB. Importantly, the importance of species functional type is highlighted in conjunction with the availability and applicability of different remote sensing datasets, with refined resolutions, which provide an opportunity to monitor C3 and C4 grasses AGB. While some progress has been made, this review has revealed the need for further remote sensing studies to model the seasonal (cyclical) variability, as well as long-term AGB changes in C3 and C4 grasses, in the face of climate change and food security. Moreover, the findings of this study have shown the significance of shifting towards the application of advanced statistical models, to further improve C3 and C4 grasses AGB estimation accuracy.

Sensitivity of evapotranspiration retrievals from the METRIC processing algorithm to improved radiometric resolution of Landsat 8 thermal data and to calibration bias in Landsat 7 and 8 surface temperature

Abstract We made an assessment on the use of 12-bit resolution of Landsat 8 (L8) on evapotranspiration (ET) retrievals via the METRIC process as compared to using 8-bit resolution imagery of previous Landsat missions. METRIC (Mapping Evapotranspiration at high Resolution using Internalized Calibration) is an ET retrieval system commonly used in water and water rights management where the surface energy balance process is coupled with an extreme-end point calibration process to remove most impacts of systematic bias in remotely sensed inputs. We degraded L8 thermal images by grouping sequential digital numbers to reduce the apparent numerical resolution and then recomputed ET using METRIC and compared to nondegraded ET products. The use of 8-bit thermal data did not substantially impair the accuracy of ET retrievals derived from METRIC, as compared to the use of 12-bit thermal data. The largest error introduced into ET was We evaluated the impact of bias in land surface temperature (LST) retrievals on ET determination using the CIMEC calibration approach (Calibration using Inverse Modeling using Extreme Member Calibration) employed in METRIC by introducing globally systematic biases into LST retrievals from L7 and L8 and comparing to ET from non-biased retrievals. The impacts of the introduction of both additive and multiplicative biases into surface temperature on ET were small for the three regions of the US studied, and for both L7 and L8 satellite systems. An independent study showed that METRIC-produced ET compared to within 3% of measured ET for the California site. The study assessed the impact of the February 2014 recalibration of L8 thermal data that caused a 3 K downward shift in LST estimation and changed reflectance values by about 0.7%. We found that the use of the recalibrated LST and shortwave data sets in METRIC did not change the accuracy of ET retrievals due to the automatic compensation for systematic biases employed by METRIC.

Comparison of Landsat 8 OLI and Landsat 7 ETM+ for estimating grassland LAI using model inversion and spectral indices: case study of Mpumalanga, South Africa

ABSTRACTThe leaf area index (LAI) is the key biophysical indicator used to assess the condition of rangeland. In this study, we investigated the implications of narrow spectral response, high radiometric resolution (12 bits), and higher signal-to-noise ratio of the Landsat 8 Operational Land Imager (OLI) sensor for the estimation of LAI. The Landsat 8 LAI estimates were compared to that of its predecessors, namely Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (8 bits). Furthermore, we compared the radiative transfer model (RTM) and spectral indices approaches for estimating LAI on rangeland systems in South Africa. The RTM was inverted using artificial neural network (ANN) and lookup table (LUT) algorithms. The accuracy of the models was higher for Landsat 8 OLI, where ANN (root mean squared error, RMSE = 0. 13; R2 = 0. 89), LUT (RMSE = 0. 25; R2 = 0. 50), compared to Landsat 7 ETM+, where ANN (RMSE = 0. 35; R2 = 0. 60), LUT (RMSE = 0. 38; R2 = 0. 50). Compared to an empirical approach, the RTM provided higher accuracy. In conclusion, Landsat 8 OLI provides an improvement for the estimation of LAI over Landsat 7 ETM+. This is useful for rangeland monitoring.

Mapping inland water carbon content with Landsat 8 data

ABSTRACTLandsat 8 is the first Earth observation satellite with sufficient radiometric and spatial resolution to allow global mapping of lake CDOM and DOC (coloured dissolved organic matter and dissolved organic carbon, respectively) content. Landsat 8 is a multispectral sensor however, the number of potentially usable band ratios, or more sophisticated indices, is limited. In order to test the suitability of the ratio most commonly used in lake carbon content mapping, the green–red band ratio, we carried out fieldwork in Estonian and Brazilian lakes. Several atmospheric correction methods were also tested in order to use image data where the image-to-image variability due to illumination conditions would be minimal. None of the four atmospheric correction methods tested, produced reflectance spectra that matched well with in situ measured reflectance. Nevertheless, the green–red band ratio calculated from the reflectance data was in correlation with measured CDOM values. In situ data show that there is a strong correlation between CDOM and DOC concentrations in Estonian and Brazilian lakes. Thus, mapping the global CDOM and DOC content from Landsat 8 is plausible but more data from different parts of the world are needed before decisions can be made about the accuracy of such global estimation.

TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (<a href="http://earthexplorer.usgs.gov/"target="_blank">http://earthexplorer.usgs.gov/</a>). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION

Abstract. Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (http://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS

Abstract. Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be easily modeled analytically from the first one. We thus remove the remaining along-track jitter effects in the DEMs statistically through temporal DEM stacks to finally compute the glacier volume changes over time. Our method yields cleaner and spatially more complete elevation data, which also proved to be more in accordance to reference DEMs, compared to NASA’s AST14DMO DEM standard products. The quality of the demonstrated measurements promises to further unlock the underused potential of ASTER DEMs for glacier volume change time series on a global scale. The data produced by our method will help to better understand the response of glaciers to climate change and their influence on runoff and sea level.

LONG-TERM MONITORING OF WATER DYNAMICS IN THE SAHEL REGION USING THE MULTI-SAR-SYSTEM

Abstract. Fresh water is a scarce resource in the West-African Sahel region, seasonally influenced by droughts and floods. Particularly in terms of climate change, the importance of wetlands increases for flora, fauna, human population, agriculture, livestock and fishery. Hence, access to open water is a key factor. Long-term monitoring of water dynamics is of great importance, especially with regard to the spatio-temporal extend of wetlands and drylands. It can predict future trends and facilitate the development of adequate management strategies. Lake Tabalak, a Ramsar wetland of international importance, is one of the most significant ponds in Niger and a refuge for waterbirds. Nevertheless, human population growth increased the pressure on this ecosystem, which is now degrading for all uses. The main objective of the study is a long-term monitoring of the Lake Tabalak’s water dynamics to delineate permanent and seasonal water bodies, using weather- and daytime-independent multi-sensor and multi-temporal Synthetic Aperture Radar (SAR) data available for the study area. Data of the following sensors from 1993 until 2016 are used: Sentinel-1A, TerraSARX, ALOS PALSAR-1/2, Envisat ASAR, RADARSAT-1/2, and ERS-1/2. All SAR data are processed with the Multi-SAR-System, unifying the different characteristics of all above mentioned sensors in terms of geometric, radiometric and polarimetric resolution to a consistent format. The polarimetric representation in Kennaugh elements allows fusing single-polarized data acquired by older sensors with multi-polarized data acquired by current sensors. The TANH-normalization guarantees a consistent and therefore comparable description in a closed data range in terms of radiometry. The geometric aspect is solved by projecting all images to an earth-fixed coordinate system correcting the brightness by the help of the incidence angle. The elevation model used in the geocoding step is the novel global model produced by the TanDEM-X satellite mission. The advantage of the Multi-SAR-System is that it comprises ortho-rectification, radiometric enhancement, normalization and Kennaugh decomposition, independent from sensors, modes, polarizations or acquisition date of SAR data. In addition, optical satellite data can be included as well, to fill gaps where SAR data are missing due to the special normalization scheme. This kind of pre-processing is exclusively implemented at the Earth Observation Center of the German Aerospace Center in Oberpfaffenhofen, Germany. Therefore, the dynamic change of the open water of the Lake Tabalak could be classified over dry and rainy seasons and years, using different SAR data. The study provides a unique database and contributes to a better understanding of wetland systems in the Sahel region influenced by human pressure and climate change.

LONG-TERM MONITORING OF WATER DYNAMICS IN THE SAHEL REGION USING THE MULTI-SAR-SYSTEM

Fresh water is a scarce resource in the West-African Sahel region, seasonally influenced by droughts and floods. Particularly in terms of climate change, the importance of wetlands increases for flora, fauna, human population, agriculture, livestock and fishery. Hence, access to open water is a key factor. Long-term monitoring of water dynamics is of great importance, especially with regard to the spatio-temporal extend of wetlands and drylands. It can predict future trends and facilitate the development of adequate management strategies. Lake Tabalak, a Ramsar wetland of international importance, is one of the most significant ponds in Niger and a refuge for waterbirds. Nevertheless, human population growth increased the pressure on this ecosystem, which is now degrading for all uses. The main objective of the study is a long-term monitoring of the Lake Tabalak’s water dynamics to delineate permanent and seasonal water bodies, using weather- and daytime-independent multi-sensor and multi-temporal Synthetic Aperture Radar (SAR) data available for the study area. Data of the following sensors from 1993 until 2016 are used: Sentinel-1A, TerraSARX, ALOS PALSAR-1/2, Envisat ASAR, RADARSAT-1/2, and ERS-1/2. All SAR data are processed with the Multi-SAR-System, unifying the different characteristics of all above mentioned sensors in terms of geometric, radiometric and polarimetric resolution to a consistent format. The polarimetric representation in Kennaugh elements allows fusing single-polarized data acquired by older sensors with multi-polarized data acquired by current sensors. The TANH-normalization guarantees a consistent and therefore comparable description in a closed data range in terms of radiometry. The geometric aspect is solved by projecting all images to an earth-fixed coordinate system correcting the brightness by the help of the incidence angle. The elevation model used in the geocoding step is the novel global model produced by the TanDEM-X satellite mission. The advantage of the Multi-SAR-System is that it comprises ortho-rectification, radiometric enhancement, normalization and Kennaugh decomposition, independent from sensors, modes, polarizations or acquisition date of SAR data. In addition, optical satellite data can be included as well, to fill gaps where SAR data are missing due to the special normalization scheme. This kind of pre-processing is exclusively implemented at the Earth Observation Center of the German Aerospace Center in Oberpfaffenhofen, Germany. Therefore, the dynamic change of the open water of the Lake Tabalak could be classified over dry and rainy seasons and years, using different SAR data. The study provides a unique database and contributes to a better understanding of wetland systems in the Sahel region influenced by human pressure and climate change.

GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS

Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. <br><br> As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be easily modeled analytically from the first one. We thus remove the remaining along-track jitter effects in the DEMs statistically through temporal DEM stacks to finally compute the glacier volume changes over time. Our method yields cleaner and spatially more complete elevation data, which also proved to be more in accordance to reference DEMs, compared to NASA’s AST14DMO DEM standard products. <br><br> The quality of the demonstrated measurements promises to further unlock the underused potential of ASTER DEMs for glacier volume change time series on a global scale. The data produced by our method will help to better understand the response of glaciers to climate change and their influence on runoff and sea level.

IMPROVEMENT EVALUATION ON CERAMIC ROOF EXTRACTION USING WORLDVIEW-2 IMAGERY AND GEOGRAPHIC DATA MINING APPROACH

Advances in geotechnologies and in remote sensing have improved analysis of urban environments. The new sensors are increasingly suited to urban studies, due to the enhancement in spatial, spectral and radiometric resolutions. Urban environments present high heterogeneity, which cannot be tackled using pixel–based approaches on high resolution images. Geographic Object–Based Image Analysis (GEOBIA) has been consolidated as a methodology for urban land use and cover monitoring; however, classification of high resolution images is still troublesome. This study aims to assess the improvement on ceramic roof classification using WorldView-2 images due to the increase of 4 new bands besides the standard “Blue-Green-Red-Near Infrared” bands. Our methodology combines GEOBIA, C4.5 classification tree algorithm, Monte Carlo simulation and statistical tests for classification accuracy. Two samples groups were considered: 1) eight multispectral and panchromatic bands, and 2) four multispectral and panchromatic bands, representing previous high-resolution sensors. The C4.5 algorithm generates a decision tree that can be used for classification; smaller decision trees are closer to the semantic networks produced by experts on GEOBIA, while bigger trees, are not straightforward to implement manually, but are more accurate. The choice for a big or small tree relies on the user’s skills to implement it. This study aims to determine for what kind of user the addition of the 4 new bands might be beneficial: 1) the common user (smaller trees) or 2) a more skilled user with coding and/or data mining abilities (bigger trees). In overall the classification was improved by the addition of the four new bands for both types of users.

IMPROVEMENT EVALUATION ON CERAMIC ROOF EXTRACTION USING WORLDVIEW-2 IMAGERY AND GEOGRAPHIC DATA MINING APPROACH

Abstract. Advances in geotechnologies and in remote sensing have improved analysis of urban environments. The new sensors are increasingly suited to urban studies, due to the enhancement in spatial, spectral and radiometric resolutions. Urban environments present high heterogeneity, which cannot be tackled using pixel–based approaches on high resolution images. Geographic Object–Based Image Analysis (GEOBIA) has been consolidated as a methodology for urban land use and cover monitoring; however, classification of high resolution images is still troublesome. This study aims to assess the improvement on ceramic roof classification using WorldView-2 images due to the increase of 4 new bands besides the standard “Blue-Green-Red-Near Infrared” bands. Our methodology combines GEOBIA, C4.5 classification tree algorithm, Monte Carlo simulation and statistical tests for classification accuracy. Two samples groups were considered: 1) eight multispectral and panchromatic bands, and 2) four multispectral and panchromatic bands, representing previous high-resolution sensors. The C4.5 algorithm generates a decision tree that can be used for classification; smaller decision trees are closer to the semantic networks produced by experts on GEOBIA, while bigger trees, are not straightforward to implement manually, but are more accurate. The choice for a big or small tree relies on the user’s skills to implement it. This study aims to determine for what kind of user the addition of the 4 new bands might be beneficial: 1) the common user (smaller trees) or 2) a more skilled user with coding and/or data mining abilities (bigger trees). In overall the classification was improved by the addition of the four new bands for both types of users.

ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

Abstract. The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

Remote Sensing of Black Lakes and Using 810 nm Reflectance Peak for Retrieving Water Quality Parameters of Optically Complex Waters

Many lakes in boreal and arctic regions have high concentrations of CDOM (coloured dissolved organic matter). Remote sensing of such lakes is complicated due to very low water leaving signals. There are extreme (black) lakes where the water reflectance values are negligible in almost entire visible part of spectrum (400–700 nm) due to the absorption by CDOM. In these lakes, the only water-leaving signal detectable by remote sensing sensors occurs as two peaks—near 710 nm and 810 nm. The first peak has been widely used in remote sensing of eutrophic waters for more than two decades. We show on the example of field radiometry data collected in Estonian and Swedish lakes that the height of the 810 nm peak can also be used in retrieving water constituents from remote sensing data. This is important especially in black lakes where the height of the 710 nm peak is still affected by CDOM. We have shown that the 810 nm peak can be used also in remote sensing of a wide variety of lakes. The 810 nm peak is caused by combined effect of slight decrease in absorption by water molecules and backscattering from particulate material in the water. Phytoplankton was the dominant particulate material in most of the studied lakes. Therefore, the height of the 810 peak was in good correlation with all proxies of phytoplankton biomass—chlorophyll-a (R2 = 0.77), total suspended matter (R2 = 0.70), and suspended particulate organic matter (R2 = 0.68). There was no correlation between the peak height and the suspended particulate inorganic matter. Satellite sensors with sufficient spatial and radiometric resolution for mapping lake water quality (Landsat 8 OLI and Sentinel-2 MSI) were launched recently. In order to test whether these satellites can capture the 810 nm peak we simulated the spectral performance of these two satellites from field radiometry data. Actual satellite imagery from a black lake was also used to study whether these sensors can detect the peak despite their band configuration. Sentinel 2 MSI has a nearly perfectly positioned band at 705 nm to characterize the 700–720 nm peak. We found that the MSI 783 nm band can be used to detect the 810 nm peak despite the location of this band is not in perfect to capture the peak.

CRITICAL ASSESSMENT OF OBJECT SEGMENTATION IN AERIAL IMAGE USING GEO-HAUSDORFF DISTANCE

Abstract. Aerial Image records the large-range earth objects with the ever-improving spatial and radiometric resolution. It becomes a powerful tool for earth observation, land-coverage survey, geographical census, etc., and helps delineating the boundary of different kinds of objects on the earth both manually and automatically. In light of the geo-spatial correspondence between the pixel locations of aerial image and the spatial coordinates of ground objects, there is an increasing need of super-pixel segmentation and high-accuracy positioning of objects in aerial image. Besides the commercial software package of eCognition and ENVI, many algorithms have also been developed in the literature to segment objects of aerial images. But how to evaluate the segmentation results remains a challenge, especially in the context of the geo-spatial correspondence. The Geo-Hausdorff Distance (GHD) is proposed to measure the geo-spatial distance between the results of various object segmentation that can be done with the manual ground truth or with the automatic algorithms.Based on the early-breaking and random-sampling design, the GHD calculates the geographical Hausdorff distance with nearly-linear complexity. Segmentation results of several state-of-the-art algorithms, including those of the commercial packages, are evaluated with a diverse set of aerial images. They have different signal-to-noise ratio around the object boundaries and are hard to trace correctly even for human operators. The GHD value is analyzed to comprehensively measure the suitability of different object segmentation methods for aerial images of different spatial resolution. By critically assessing the strengths and limitations of the existing algorithms, the paper provides valuable insight and guideline for extensive research in automating object detection and classification of aerial image in the nation-wide geographic census. It is also promising for the optimal design of operational specification of remote sensing interpretation under the constraints of limited resource.

CRITICAL ASSESSMENT OF OBJECT SEGMENTATION IN AERIAL IMAGE USING GEO-HAUSDORFF DISTANCE

Aerial Image records the large-range earth objects with the ever-improving spatial and radiometric resolution. It becomes a powerful tool for earth observation, land-coverage survey, geographical census, etc., and helps delineating the boundary of different kinds of objects on the earth both manually and automatically. In light of the geo-spatial correspondence between the pixel locations of aerial image and the spatial coordinates of ground objects, there is an increasing need of super-pixel segmentation and high-accuracy positioning of objects in aerial image. Besides the commercial software package of eCognition and ENVI, many algorithms have also been developed in the literature to segment objects of aerial images. But how to evaluate the segmentation results remains a challenge, especially in the context of the geo-spatial correspondence. The Geo-Hausdorff Distance (GHD) is proposed to measure the geo-spatial distance between the results of various object segmentation that can be done with the manual ground truth or with the automatic algorithms.Based on the early-breaking and random-sampling design, the GHD calculates the geographical Hausdorff distance with nearly-linear complexity. Segmentation results of several state-of-the-art algorithms, including those of the commercial packages, are evaluated with a diverse set of aerial images. They have different signal-to-noise ratio around the object boundaries and are hard to trace correctly even for human operators. The GHD value is analyzed to comprehensively measure the suitability of different object segmentation methods for aerial images of different spatial resolution. By critically assessing the strengths and limitations of the existing algorithms, the paper provides valuable insight and guideline for extensive research in automating object detection and classification of aerial image in the nation-wide geographic census. It is also promising for the optimal design of operational specification of remote sensing interpretation under the constraints of limited resource.