Assessment of the ASAR sensor radiometric quality in comparison to ERS‐2 and RADARSAT‐1 SAR data

The estimation of geophysical parameters from Synthetic Aperture Radar (SAR) data necessitates well‐calibrated sensors with good radiometric precision. In this paper, the radiometric calibration of the new Advanced Synthetic Aperture Radar (ASAR‐ENVISAT) sensor was assessed by comparing ASAR data with ERS‐2 and RADARSAT‐1 SAR data. By analysing the difference between radar signals of forest stands, the results show differences of varying importance between the ASAR on the one hand, and the ERS‐2 and the RADARSAT‐1 on the other. For recent data acquired at the end of 2005, the difference varies from ?0.72 to +0.72 dB, with temporal variations that can reach 1.1 dB. For older data acquired in 2003 and 2004, we observe a sharp decrease in the radar signal in the range direction, which can attain 3.5 dB. The use of revised calibration constants provided recently by the European Space Agency (ESA) significantly improves the results of the radiometric calibration, where the difference between the ASAR and the other SARs will be lower than 0.5 dB.     

Reflectance of floodplain waterbodies using EO‐1 Hyperion data from high and receding flood periods of the Amazon River

The potential of Hyperion images acquired on September 2001 (receding flood period of the Amazon River) and June 2005 (high flood) was investigated for reflectance characterization of selected Amazon floodplain waterbodies using a linear spectral mixture model. The results show the ability of Hyperion to measure adequately the major variation in water reflectance spectral features in response to the annual flood pulse of the Amazon River. Mixture model fraction values were correlated with measured inorganic suspended solids (ISS) but not with chlorophyll (Chl) in the high flood period. Inspection of the fractions across the two images revealed variation in water composition. Small changes in ISS‐ and Chl‐bearing water fractions between the images indicated relatively stable spectral conditions for low (Tapajós River and Lake Juruparipucu) and high (Amazon River) turbidity waterbodies. Large changes indicated reflectance variation in some lakes when the water receded due to algal blooms (Lake Curumu) and sediment resuspension in shallow regions (Lake Aritapera). Although not all water constituents were modelled adequately for quantification purposes, spectral mixture modelling is still an interesting approach for spectral–temporal reflectance characterization of Amazonian floodplains with hyperspectral data.     

Combining airborne lidar and Landsat ETM+ data with photoclinometry to produce a digital elevation model for Langjökull,Iceland

An incomplete airborne lidar survey of Langjökull, Iceland's second largest ice cap (?900 km²) and the surrounding area was undertaken in August 2007. Elevation data were interpolated between the lidar swaths using the technique of photoclinometry (PC), which relates Sun-parallel slope angles to image brightness. A Landsat Enhanced Thematic Mapper Plus (ETM+) image from March 2002 was used for this purpose. Different bands and band combinations were assessed and Band 4 (760–900 nm) was found to be the most appropriate. Parameters in the slope–brightness equation were derived empirically by comparing the image brightness with lidar elevation data in a 4 km × 4 km region in the centre of the ice cap. This relationship was then used to calculate the slopes, and, by integration between tie points of known lidar elevation, the elevations of the 30 m pixels that were not surveyed by lidar. The root-mean-square (RMS) precision (repeatability) of lidar elevations was 0.18 m and the accuracy was estimated to be 0.25 m. The 68.3% quantile of absolute difference relative to lidar (analogous to root-mean-square error (RMSE)) of all interpolated areas where PC assumptions are met was 5.44 m (4.66 m and 8.73 m for on- and off-ice areas, respectively). Where one or more PC assumptions were not met (e.g. self-shading, sensor saturation), the 68.3% quantile of absolute difference relative to lidar was 27.89 m (18.52 m on the ice cap and 32.91 m off-ice). These accuracies were applicable to 63%, 31%, and 6% of the ice cap and 59%, 28%, and 13% of the final digital elevation model (DEM), respectively. The area-weighted average 68.3% quantiles were 2.89 m for the ice cap and 6.75 m for the entire DEM. The PC technique applied to satellite imagery is a useful and appropriate method for interpolating a lidar survey of an ice cap.     

The Spatial Transformation and Hotspot Detection of Land Use and Cover in Arid Basin：A Case of the Middle Reaches of Heihe River

Based on the data of land use/cover change in the middle reaches of the Heihe River in 1987,2001 and 2016,with the support of remote sensing technology,the Markov transfer matrix and land use dynamic estimation model were used to simulate the land use transformation direction and spatial distribution characteristics,detect hotspots of land use/cover change,analyze the relevant driving factors,and propose the countermeasures for development bottlenecks during the study period in the middle reaches of the Heihe River between 1987~2001 and 2001~2016.Its main findings are as follows：the main changes of land use in the middle reaches of Heihe River as the land of human activities gradually increasing,the ecological land decreasing continuously.The areas with sharp changes in land use are mainly concentrated on both sides of the river,the types of transformation are mainly in the conversion of waters into grassland,grassland into cultivated land,and the unused land to construction land;The scope of land use transformation in the study area have obvious spatial differences,the frequency and expansion are significantly higher than the previous period;as a whole,the hot region are located in the oasis area of agriculture in Zhangye,but the early hot region is more dispersed,small,no benefit,the later is concentrated,and has more central tendency in the larger space.     

Uncertainty analysis of flash‐flood system using remote sensing and a geographic information system based on GcIUH in the Yeongdeok Basin,Korea

The current paper focuses on minimizing flood damage in the Yeongdeok basin of South Korea by establishing a flood prediction model based on a geographic information system (GIS), remote sensing and geomorphoclimatic instantaneous unit hydrograph (GcIUH) techniques. The GIS database for flash flood prediction was created using data from digital elevation models (DEMs), soil maps and Landsat satellite imagery. Flood prediction was based on the peak discharge calculated at the sub‐basin scale using hydrogeomorphologic techniques and the threshold runoff value. Using the developed flash flood prediction model, rainfall conditions with the potential to cause flooding were determined based on the cumulative rainfall for 20 min, considering rainfall duration, peak discharge and flooding in the Yeongdeok basin.     

A study of the seasonal dynamics of grassland growth rates in Inner Mongolia based on AVHRR data and a light‐use efficiency model

Growth rate data for different pastures could provide important reference data for developing rotation grazing plans, for hay production, and for forage replenishment. Based on AVHRR NDVI data and a light‐use efficiency (LUE) model, we estimated absorbed photosynthetically active radiation and LUE (ε) by integrating air and soil temperature, precipitation and total solar radiation time series data from 1986 to 1999, and calculated the absolute growth rate (AGR) and cumulative absolute growth rate (CAGR) of aboveground biomass in each growing season in China's Inner Mongolia region. AGR and CAGR estimated by the LUE model were validated using monthly growth data obtained for the vegetation in desert steppe, typical steppe, and meadow steppe ecosystems from 1986 to 1995. The LUE model provided sufficiently good simulation accuracy that its use should permit improved livestock feed management in the study area. From 1986 to 1999, average CAGR of steppe vegetation during the growing season increased quickly in June and July, reached a maximum in July and August, and declined in September. In 1999, AGR reflected the pattern of seasonal vegetation dynamics during the growing season.     

A linear model to predict with a multi‐spectral radiometer the amount of nitrogen in winter wheat

The objective was to develop an optimal vegetation index (VI_opt) to predict with a multi‐spectral radiometer nitrogen in wheat crop (kg[N] ha^?1). Optimality means that nitrogen in the crop can be measured accurately in the field during the growing season. It also means that the measurements are stable under changing light conditions and vibrations of the measurement platform. Different fields, on which various nitrogen application rates and seeding densities were applied in experimental plots, were measured optically during the growing season. These measurements were performed over three years. Optical measurements on eight dates were related to calibration measurements of nitrogen in the crop (kg[N] ha^?1) as measured in the laboratory. By making combinations of the wavelength bands, and whether or not the soil factor was taken into account, numerous vegetation indices (VIs) were examined for their accuracy in predicting nitrogen in wheat. The effect of changing light conditions in the field and vibrations of the measurement platform on the VIs were determined based on tests in the field. VI_opt ((1+L)?(R ² _NIR+1)/(R _red+L) with L = 0.45), the optimal vegetation index found, was best in predicting nitrogen in grain crop. The root mean squared error (RMSE), determined by means of cross‐validation, was 16.7 kg[N] ha^?1. The RMSE was significantly lower compared to other frequently used VIs such as NDVI, RVI, DVI, and SAVI. The L‐value can change between 0.16 and 1.6 without deteriorating the RMSE of prediction. Besides being the best predictor for nitrogen, VI_opt had the advantage of being a stable vegetation index under circumstances of changing light conditions and platform vibrations. In addition, VI_opt also had a simple structure of physically meaningful bands.     

A digital camera as a tool to measure colour indices and related properties of sandy soils in semi‐arid environments

Soil colour carries important information regarding the soil's chemical and physical properties. However, common practices for measuring soil colour, either by Munsell charts or by field/laboratory spectrometers, are insufficient, due to the subjective and nonquantitative character of the Munsell charts, and to the high cost and inconvenience of field spectrometers. We present herein, a method to characterize the colour of soil samples, and related chemical and physical properties of the soil, using a digital camera, and an array of coloured plastic chips, that are used for calibration purposes. Using 370 samples of sandy soils, we have demonstrated that both RGB values from digital images and their derived soil indices, correlate highly with similar measurements performed by a field spectrometer. When checked against free iron oxide content and against the percentage of fine particles in a sub‐sample set of 42 soils, the redness index as measured by the digital camera gave similar or better correlations than those obtained from a field spectrometer, against both free iron oxides and fine particle contents (R ² of 89% for the iron oxides, and of 81% for the fine particles). We propose the use of a digital camera as a field analytical tool to determine precisely: soil colour, iron oxide and fine particle content. Further study in this direction, with other soil population and more soil properties, is strongly advised in order to launch this as a vastly applicable and generic method.     

How to assess privacy violations in the age of Big Data? Analysing the three different tests developed by the ECtHR and adding for a fourth one

It is commonly believed that privacy cases are resolved by balancing the private interest (e.g. personal autonomy) and the common interest (e.g. national security) involved with a particular privacy violation. Clearly, this approach no longer holds in the age of Big Data, in which massive amounts of personal data are gathered without a pre-established goal. Not only is the balancing test inapplicable because it is often unclear how certain data gathering and processing initiatives improve the societal interest, but it is also hard to demonstrate whether and if so how an individual has suffered from such massive data processing systems. Besides the balancing test, however, the European Court of Human Rights (ECtHR) applies two other tests when dealing with privacy issues. Both have an added value when applied to privacy violations following from Big Data processes. Still, if Article 8 of the European Convention on Human Rights (ECHR) is to retain its significance in the new technological environment, it might be necessary to develop a new test, the rudiments of which might already be found in the Court's case law.     

Assessment of land‐cover changes related to shrimp aquaculture using remote sensing data: a case study in the Giao Thuy District,Vietnam

Shrimp culture is a sector of aquaculture that has a high potential for poverty alleviation and rural development in Vietnam. However, the development of this activity induces changes that potentially have negative impacts on the environment, one of which is wetland deterioration. This paper describes the use of a proposed change detection methodology in the assessment of mangrove forest alterations caused by aquaculture development, as well as the effectiveness of the measures taken to mitigate deforestation in the district of Giao Thuy, Vietnam, between 1986, 1992 and 2001. Geometric and radiometric corrections were applied to Landsat images prior to identifying changes through comparison of unsupervised classifications. Changes were afterwards validated using a thresholding method based on Tasselled Cap feature image differencing and a rule‐based feature selection matrix. The matrix is used to identify the feature that is most efficient at detecting the presence of change between given land‐cover classes. The proposed approach aims to minimize commission errors in the post‐classification change detection process. The results suggest that 63% of mangrove areas apparent in 1986 had been replaced by shrimp ponds in 2001. Between 1986 and 1992, 440 ha of adult mangrove trees had disappeared, whereas the mangrove extent increased by 441 ha between 1992 and 2001. This recovery is attributed to reforestation projects and conservation efforts that promoted natural regeneration.     

Spatial and temporal responses of different crop‐growing environments to agricultural drought: a study in Haryana state,India using NOAA AVHRR data

Spatial and temporal responses to agricultural drought of different districts with different crop‐growing environments were assessed using National Oceanic & Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)‐derived monthly time composite Normalized Difference Vegetation Index (NDVI) images of a drought year (2002) and a normal year (2004) in Haryana state, one of the most prolific agricultural states of India. The seasonal NDVI profiles derived from NOAA AVHRR data, despite coarse spatial resolution, successfully captured the response of vulnerable districts to drought events. The greenness (NDVI) in mid‐season and at the end of the season of drought and normal years was compared. Districts having less irrigation support due to insufficient canal supplies and poor quality of groundwater had very high NDVI deviation from normal, signifying the impact of severe drought conditions in terms of reduced/delayed sown area, poor germination etc. in the year 2002. The districts with high irrigation support (surface water plus good quality groundwater) have either higher NDVI or insignificant deviation from a normal year and are not influenced by meteorological drought. Thus, quality of groundwater in different districts is a key factor to determine the vulnerability and sensitivity of the district to meteorological drought events in the study area state. The results of the study are relevant for vulnerability mapping and drought hazard zonation in the state to aid in‐season and long‐term management of droughts.     

An empirical study on the utility of BRDF model parameters and topographic parameters for mapping vegetation in a semi‐arid region with MISR imagery

In this study we show that multiangle remote sensing is useful for increasing the accuracy of vegetation community type mapping in desert regions. Using images from the National Aeronautics and Space Administration (NASA) Multiangle Imaging Spectroradiometer (MISR), we compared roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with those played by topographic parameters in improving vegetation community type classifications for the Jornada Experimental Range and the Sevilleta National Wildlife Refuge in New Mexico, USA. The BRDF models used were the Rahman–Pinty–Verstraete (RPV) model and the RossThin‐LiSparseReciprocal (RTnLS) model. MISR nadir multispectral reflectance was considered as baseline because nadir observation is the most basic remote sensing observation. The BRDF model parameters and the topographic parameters were considered as additional data. The BRDF model parameters were obtained by inversion of the RPV model and the RTnLS model against the MISR multiangle reflectance data. The results of 32 classification experiments show that the BRDF model parameters are useful for vegetation mapping; they can be used to raise classification accuracies by providing information that is not available in the spectral‐nadir domain, or from ancillary topographic parameters. This study suggests that the Moderate Resolution Imaging Spectroradiometer (MODIS) and MISR BRDF model parameter data products have great potential to be used as additional information for vegetation mapping.     

Monitoring post-tsunami vegetation recovery in Phang-Nga province,Thailand, based on IKONOS imagery and field investigations – a contribution to the analysis of tsunami vulnerability of coastal ecosystems

A major tsunami in December 2004 devastated the coastal ecosystems along the Andaman Sea coast of Thailand. Since intact coastal ecosystems provide many important services for local communities at the Andaman Sea, it is crucial to investigate to what extent (in terms of percentage area and speed) the affected ecosystems were capable of recovering after the tsunami. Field measurements and multi-date IKONOS imagery were used to estimate the recovery and succession patterns of coastal vegetation types in the Phang-Nga province of Thailand, three years after the tsunami. Thus, this study contributes to a holistic understanding of the ecological vulnerability of the coastal area to tsunamis. A zone-based change detection approach is applied by comparing two change detection techniques: the first method involves the calculation of a recovery rate based on multi-temporal TNDVI (transformed normalized difference vegetation index) images (TNDVI approach), whereas the second approach is a combined approach of the change vector analysis (CVA). Although these two methods provide different types of information (quantitative for the TNDVI approach, qualitative for the CVA), they are comparable in terms of results and accuracies. The results reveal that recovery processes vary based on the type of the ecosystem and, furthermore, are strongly influenced by human activities. Grasslands, coconut plantations and the mixed vegetation cover could recover faster than the mangroves and casuarina forests. Among the forest ecosystems, recovery rates of casuarina forests were higher than for mangroves, but the recovery area was smaller. This study also discusses the potential and some limitations and inaccuracies of applying high-resolution optical imagery for assessing vegetation recovery at a local scale.