Monitoring of soil moisture over the Kuwait desert using remote sensing techniques

Field experiments were conducted in synchronous with Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) passes over the Kuwait desert covering one pixel of 25 km circular diameter. Forty-five soil samples were collected within a pixel resolution to estimate the effective soil moisture, and nine such campaigns were conducted during the period December 2005 to March 2006. Field-estimated soil moisture values up to 5 cm depth were compared with AMSR-E soil moisture values and our model results. It was observed that the field soil moisture values are consistently lower than AMSR-E and our model values. However, the difference is within the errors. AMSR-E soil moisture and our model values agree with each other. Monthly average soil moisture maps of Kuwait were generated from AMSR-E data to study the temporal and spatial variability of soil moisture. It is observed that the maximum soil moisture during January is about 10%, and most of the year the values are about 5% soil moisture.     

Monitoring agricultural soil moisture extremes in Canada using passive microwave remote sensing

Current methods to assess soil moisture extremes rely primarily on point-based in situ meteorological stations which typically provide precipitation and temperature rather than direct measurements of soil moisture. Microwave remote sensing offers the possibility of quantifying surface soil moisture conditions over large spatial extents. Capturing soil moisture anomalies normally requires a long temporal record of data, which most operating satellites do not have. This research examines the use of surface soil moisture from the AMSR-E passive microwave satellite to derive surface soil moisture anomalies by exploiting spatial resolution to compensate for the shorter temporal record of the satellite sensor. Four methods were used to spatially aggregate information to develop a surface soil moisture anomaly (SMA). Two of these methods used soil survey and climatological zones to define regions of homogeneity, based on the Soil Landscapes of Canada (SLC) and the EcoDistrict nested hierarchy. The second two methods (ObShp3 and ObShp5) used zones defined by a data driven segmentation of the satellite soil moisture data. The level of sensitivity of the calculated SMA decreased as the number of pixels used in the spatial aggregation increased, with the average error reducing to less than 5% when more than 15 pixels are used. All methods of spatial aggregation showed somewhat weak but consistent relationship to in situ soil moisture anomalies and meteorological drought indices. The size of the regions used for aggregation was more important than the method used to create the regions. Based on the error and the relationship to the in situ and ancillary data sets, the EcoDistrict or ObShp3 scale appears to provide the lowest error in calculating the SMA baseline. This research demonstrates that the use of spatial aggregation can provide useful information on soil moisture anomalies where satellite records of data are temporally short.     

Technique of large-scale soil mapping in basaltic terrain using satellite remote sensing data

The present paper discusses the technique of large-scale soil mapping using remote sensing data. IRS-1C PAN merged data of two seasons, namely late Kharif (monsoon) and Rabi (post rainy season), were interpreted visually in conjunction with Survey of India (SOI) toposheet (1:50?000 scale) and available ground data to prepare the physiography–land use (PLU) map. The PLU delineation explained a three-tier approach comprising landform, slope and land use characteristics of a given parcel of land. The first letter in the PLU legend indicates the landform, the second letter denotes slope percentage and the third letter indicates the land use characteristics. Soils occurring in different PLU units were examined and a PLU–soil relationship was developed. The soil map depicting phases of soil series was prepared using ILWIS software.     

Characterizing 40 years of natural Pinus sylvestris var. mongolica carbon stocks in Northeast China using stand age from remote sensing time series

ABSTRACT

Remote sensing (RS)-assisted estimates of forest carbon stocks are essential for modelling carbon budgets at large scales. Research on the dynamic variations in forest biomass and carbon stocks is important to improve the sustainable use of forest resources and understand forest carbon budgets in China. The Honghuaerji region of the Inner Mongolia Autonomous Region is where Pinus sylvestris var. mongolica Litv. (MP) originated. MP forests contained the most important coniferous silvicultural tree species and were later introduced to the desert regions of northern China. However, there has been very little research on carbon stocks (vegetation and soil carbon) over time in the original MP forests. Our goal was to estimate the changes in the carbon stocks of the natural MP forests in the Honghuaerji region using a 40-year RS time series, forest survey data and laboratory data. First, we mapped MP forests and estimated forest ages based on Key Hole 9 and Landsat time series stacks from 1975 to 2015, and the estimated forest ages were shown to be consistent with the surveyed tree ages with a coefficient of determination (R²) of 0.96. Second, fifteen vegetation indices were evaluated as indicators of diameter at breast height (DBH). Of these, tasselled cap greenness provided the best relationships, and an RS-based DBH equation was constructed with an R² of 0.65. Third, the carbon densities in the different ages of MP forests were obtained at regional scales. Finally, the dynamics of the carbon stocks in the natural MP forest ecosystem (including stems, branches, leaves, roots and soils) over 40 years were obtained. The results of this study indicated that the total MP forest ecosystem carbon stocks increased significantly from 7.36 ± 1.92 Tg C in 1975 to 11.52 ± 2.51 Tg C in 2015 at a rate of 1.42% year^–1, among which vegetation carbon stocks increased 5.54% year^–1 and soil carbon stocks increased 0.45% year^–1. The results reveal a notable carbon increase in the MP forests in the Honghuaerji region over the past four decades and provide information on the role of natural forest conservation and management in China in mitigating global climate change.     

Land cover inventory in the Netherlands using remote sensing; application in a soil and groundwater vulnerability assessment system

Abstract

Information on actual land cover is necessary for various applications, such as soil and groundwater protection studies and hydrological studies. Therefore, a decision to produce a national land cover data base of the Netherlands, using satellite images, was made. The first version of the data base is now available for the whole of the Netherlands. Prior to the supervised classification the area was stratified in more or less homogeneous areas. Because cost, time and logistics required for a random sampling of the entire country were prohibitive, a mixed quantitative/qualitative classification accuracy assessment procedure was proposed. Classification performances were quantitatively assessed by comparing the classification results with digitized ground reference maps using a Geographical Information System (GIS). This offers a flexible method for locating the incorrectly labelled pixels and determining the possible reasons thereof. Classification accuracy of the land cover types which do not change much in time was also qualitatively assessed, using aerial photographs and topographical maps. The land cover data derived from remote sensing images can be readily combined with other digitized geographical data bases (e.g. soil maps).

The results of the proposed classification and validation procedure are presented for a test site situated in a stratum in the south of the Netherlands. It is shown how the land cover data are applied in a soil and groundwater vulnerability assessment system.     

Statistical analysis of soil colour and spectroradiometric data for hyperspectral remote sensing of soil properties (example in a southern Italy Mediterranean ecosystem)

Munsell hue, value and chroma of 69 surface soil samples were both visually estimated by four observers under diffuse daylight and computed from laboratory reflectance spectra by applying the CIE 1931 standard method. Significant relationships were found between 'observed' and 'computed' colour components, and between the latter and some soil properties. Using a correspondence analysis, soil colour was shown to be important in differentiating between soil types. From the original spectra, the visible bands of the MIVIS hyperspectral sensor were simulated and related to the colour components through single and multiple regression analyses. The R² values for hue, value and chroma were 0.58, 0.81 and 0.87 respectively. The results were compared with those obtained using simulated visible Thematic Mapper (TM) bands. For each sample, a curve was fitted to both the MIVIS and TM bands. From these curves, values of colour components were computed and compared with those obtained from the original spectra. Results showed a clear improvement in colour determination. Nevertheless, the complexity and variability of the best fitting curves makes this approach difficult to apply to the images. Remote sensing of soil colour is expected to improve with future launches of higher resolution hyperspectral sensors.     

An approach to quantify depth-resolved marine photochemical fluxes using remote sensing: Application to carbon monoxide (CO) photoproduction

This paper presents a model to calculate depth-resolved marine photochemical fluxes from remotely sensed ocean color and modeled solar irradiance. The basic approach uses three components: 1) below-sea-surface spectral downward scalar irradiance calculated from a radiative transfer model (STAR) and corrected for clouds using TOMS UV reflectivities; 2) surface-ocean spectral diffuse attenuation coefficients and absorption coefficients for chromophoric dissolved organic matter retrieved from SeaWiFS ocean color using the SeaUV/SeaUVc algorithms; and (3) spectral apparent quantum yield for the photochemical reaction considered. The output of the model is a photochemical rate profile, Ψ_PR(z), where z represents depth.We implemented the model for carbon monoxide (CO) photochemistry using an average apparent quantum yield spectrum and generated a monthly climatology of depth-resolved CO photoproduction rates in the global ocean. The climatology was used to compute global budgets and investigate the spatial and seasonal variabilities of CO photoproduction in the ocean. The model predicts a global CO photoproduction rate of about 41 TgC yr^− 1, in good agreement with other recent published estimates ranging from 30 to 84 TgC yr^− 1. The fate of photochemically derived CO and its role in global biogeochemical cycles remains uncertain however, with biological consumption and sea-air exchange competing for its removal in the surface ocean. Knowledge of the vertical distribution of CO photoproduction is critical in the quantification of the relative magnitudes of these sink mechanisms. The depth-resolution capabilities of this model, together with US Naval Research Laboratory climatology for mixed layer depths allowed further estimation that > 95% of the total water-column CO photoproduction occurs within the mixed layer on a global, yearly basis. Despite this compelling figure, the model also suggests significant spatio-temporal variability in the vertical distribution of CO photoproduction in the subtropical gyres, where up to 40% of water-column CO can be produced below the mixed layer during summertime.While the approach can be applied to other photochemical fluxes (e.g. DIC formation or DMS removal), accurate quantification of such processes with remote sensing will be limited until the mechanisms regulating observed oceanic variability in the apparent quantum yields are better understood. Minor modification to this model can also make it applicable for the determination of the effects of UV and visible solar radiation on sensitive biological systems.     

Technical Note Flood management using remote sensing technology: the Punjab (India) experience

The ability of space technology to provide basic information in space, time and frequency domains has been proved to be very useful in providing permanent records by mapping, monitoring and managing flood dynamics. In July 1993, floods were at their devastating worst in Punjab (India) for the second time since September 1988. In this study, IRS 1A LISS-I false colour composites and ERS panchromatic images were used to obtain near real time information on damage due to floods in July 1993. The flood map of September 1988 was also used to monitor the flood damage. The flood maps for the year 1988 and 1993 were superimposed to prepare a combined map on a 1:500 000 scale to extract and demarcate flood prone areas in Punjab (India). This study suggests that during 1988, the flooding was mainly due to overflowing of rivers, whereas in 1993 it was due to breaches in embankment of rivers and canals.     

Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers

Researchers in lidar (Light Detection And Ranging) strive to search for the most appropriate laser-based metrics as predictors in regression models for estimating forest structural variables. Many previously developed models are scale-dependent that need to be fitted and then applied both at the same scale or pixel size. The objective of this paper is to develop methods for scale-invariant estimation of forest biomass using lidar data. We proposed two scale-invariant models for biomass: a linear functional model and an equivalent nonlinear model that use lidar-derived canopy height distributions (CHD) and canopy height quantile functions (CHQ) as predictors, respectively. The two models are called functional regression models because the predictors CHD and CHQ are themselves functions or functional data. The model formulation was justified mathematically under moderate assumptions. We also created a fine-resolution biomass map by mapping individual tree component biomass in a temperate forest of eastern Texas with a lidar tree-delineation approach. The map was used as reference data to synthesize training and test datasets at multiple scales for validating the two scale-invariant models. Results suggest that the models can accurately predict biomass and yield consistent predictive performances across a variety of scales with an R² ranging from 0.80 to 0.95 (RMSE: from 14. 3 Mg/ha to 33.7 Mg/ha) among all the fitted models. Results also show that a training data size of around 50 plots or less was enough to guarantee a good fitting of the linear functional model. Our findings demonstrate the effectiveness of CHD and CHQ as lidar metrics for estimating biomass as well as the capability of lidar for mapping biomass at a range of scales. The functional regression models of this study are useful for lidar-based forest inventory tasks where the analysis units vary in size and shape. They also hold promise for estimating other forest characteristics such as below-ground biomass, timber volume, crown fuel weight, and Leaf Area Index.     

Unsupervised line network extraction in remote sensing using a polyline process

Marked point processes provide a rigorous framework to describe a scene by an unordered set of objects. The efficiency of this modeling has been shown on line network extraction with models manipulating interacting segments. In this paper, we extend this previous modeling to polylines composed of an unknown number of segments. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We accelerate the convergence of the algorithm by using appropriate proposal kernels. Results on aerial and satellite images show that this new model outperforms the previous one.     

Assessment of the effectiveness of desertification rehabilitation measures in Yulin,north-western China using remote sensing

In China tremendous efforts have gone into the rehabilitation of desertified land for productive use such as grazing, crop cultivation and afforestation. In this study the effectiveness of these agro-ecological measures to combat desertification in Yulin, Shaanxi Province of north-western China, is evaluated from multi-temporal aerial photographs in a geographical information system (GIS). The trend of desertification between 1960 and 1987 is modelled from changes in other land covers. It is found that desertified areas have decreased by 717.91 ha during the study period as a result of rehabilitation efforts. Plantation of grass is the least effective measure for halting sand dune encroachment whereas the planting of shrubs is the most effective. The trend of desertification is most accurately modelled from the changes in grassland and farmland at an R 2 value of 0.90.     

A novel method for vehicle detection in high-resolution aerial remote sensing images using YOLT approach

Multimedia Tools and Applications - Owing to the innovative challenge stood by an intergovernmental military alliance, we have proposed a model to find novel solutions in the areas of data...     

Establishing snowmelt runoff simulating model using remote sensing data and GIS in the west of China

Supported by Geographical Information Systems (GIS), snowmelt runoff simulation models have been built for the large-scale basin in the west of China. Digital Terrain Factors (DTF) were employed for dividing the basin. Using image-processing software, the problems of image correction and match between the satellite image and the images transformed from vector files have been resolved. Meanwhile, the thresholds of snow-pixel in images were achieved by means of a trial-and-error iterative process. Supervising classification techniques were utilized to determine the percentage of snow cover area in different zones and in the whole basin. The experimental watershed of the Qushenan (5286 km 2 ), in the upper reaches of the Yellow River was selected as a test basin. Snowmelt runoff models were developed for each sub-basin from which three vertical zones were simulated separately. The snowmelt runoff was computed by weighting coefficients. The results show that this approach is significant and practical.     

Land-use change analysis in Yulin prefecture,northwestern China using remote sensing and GIS

Rapid land-use change has taken place in many arid and semi-arid regions of China such as Yulin prefecture over the last decade due to rehabilitation measures. In this paper, land-use change dynamics were investigated by the combined use of satellite remote sensing and geographical information systems (GIS). Our objectives were to determine land-use transition rates among land-use types in Yulin prefecture over 14 years from 1986-2000 and to quantify the changes of various landscape metrics using FRAGSTATS, the spatial pattern analysis program for Categorical Maps. Using 30-m resolution Landsat Thematic Mapper (TM) data from the Institute of Remote Sensing Application (IRSA) in China, we classified images into six land-use types: cropland, forestland, grassland, water, urban and/or built-up land, and barren land. Significant changes in land-use occurred within the area over the study period. The results show the significant decrease in barren land was mainly due to conversion to grassland. Cropland increased by 3.39%, associated with conversions from grassland and barren land. The landscape has become more continuous, clumped and more homogeneous. The study demonstrates that the integration of satellite remote sensing and GIS was an effective approach for analysing the direction, rate and spatial pattern of land-use change.     

Panchromatic sharpening of remote sensing images using a multiscale Kalman filter

This paper presents a solution to the problem of enhancing the spatial resolution of multispectral images with high-resolution panchromatic observations. The proposed method exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and up-sampling the wavelet filter bank, and the vector multiscale Kalman filter, which is used to model the injection process of wavelet details. Kalman modelization is exploited by spatial detail analysis at coarser scales in which multispectral and panchromatic representations are known. Results are presented and discussed on very-high resolution images acquired by Quickbird satellite systems. Fusion simulations on spatially degraded data and fusion tests at the full scale reveal that an accurate and reliable PAN-sharpening is achieved by the proposed method.     

Environmental impact analysis of Dudhganga Dam in India - a multi-temporal remote sensing approach

Surface-cover status of the catchment, reservoir and dam site, before, during and after the completion of a water resources project, plays a vital role in an Environmental Impact Assessment (EIA). Orbital satellite data, along with theme and site-based information, were used in an EIA of Dudhganga Dam in India. The collection and analysis of these data in the monitoring of on-going projects is highlighted in this study.     

Detection of blue-absorbing aerosols using near infrared and visible (ocean color) remote sensing observations

An algorithm is presented, which is designed to identify blue-absorbing aerosols from near infrared and visible remote-sensing observations, as they are in particular collected by satellite ocean color sensors. The technique basically consists in determining an error budget at one wavelength around 510 nm, based on a first-guess estimation of the atmospheric path reflectance as if the atmosphere was of a maritime type, and on a reasonable hypothesis about the marine signal at this wavelength. The budget also includes the typical calibration uncertainty and the natural variability in the ocean optical properties. Identification of blue-absorbing aerosols is then achieved when the error budget demonstrates a significant over-correction of the atmospheric signal when using non-absorbing maritime aerosols. Implementation of the algorithm is presented, and its application to real observations by the MERIS and SeaWiFS ocean color sensors is discussed. The results demonstrate the skill of the algorithm in various regions of the ocean where absorbing aerosols are present, and for two different sensors. A validation of the results is also performed against in situ data from the AERONET, and further illustrates the skill of the algorithm and its general applicability.     

A study of the endangered plants of the West Ordos Plateau,China using remote sensing data

This paper presents the results of a study of endangered plants using remote sensing data. The aim of this study was to investigate and assess spatial distribution changes and temporal coverage changes of the following endangered rare plants: Tetraena mongolica, Helianthemum soongolicum, Reaumuria trigyna, Reaumuria soongorica, Potaninia mongolica, and Ammopiptanthus mongolicus, in the West Ordos Plateau National Conservation Region in Inner Mongolia, China. Using ETM+ (August 1999) remote sensing data, DEM data, and data from 25 field samples collected from Gangdeger Mountain in July 1999, digital maps highlighting endangered plant populations (1994) were processed using the ‘MPH feature extraction method’ to view the current spatial distribution pattern of the endangered plants. The ‘radiometric normalization’ and ‘vegetation change detection’ methods were then used to reveal temporal coverage changes of the endangered plants, using TM (September 1987) and ETM+ (August 1999) data.     

Satellite remote sensing of fine particulate matter (PM2.5) air quality over Beijing using MODIS

Fine particulate matter (aerodynamic diameters of less than 2.5 µm, PM_2.5) air pollution has become one of the major environmental challenges, causing severe environmental issues in urban visibility, climate, and public health. In this study, ground-level PM_2.5 concentrations, air-quality categories (AQCs), and health risk categories (HRCs) over Beijing, China, have been estimated based on mid-visible column aerosol optical depth (AOD) measurements extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data on board both Terra and Aqua satellites. Our results indicate that the MODIS AOD retrievals at 550 nm (AOD₅₅₀) match hourly aerosol robotic network (AERONET) measurements with correlation coefficients (r) of 0.950 for Terra and 0.895 for Aqua. The relationship between ground-level PM_2.5 and MODIS AOD₅₅₀ from March 2012 to February 2013 showed correlation coefficients of 0.69, 0.60, and 0.73 for spring, summer, and autumn, respectively. The atmospheric boundary layer height and relative humidity (RH) adjustments improved the AOD–PM_2.5 relationship in summer months. The estimates of daily average PM_2.5 from satellite measurements were used to predict both AQCs and HRCs, which are well matched with observations. Satellite remote sensing of atmospheric aerosols continues to show great potential for estimating ground-level PM_2.5 concentrations and can be further used to monitor the atmospheric environment in China.