Residential population estimation using a remote sensing derived impervious surface approach

Residential population estimation was explored based on impervious surface coverage in Marion County, Indiana, USA. The impervious surface was developed by spectral unmixing of a Landsat Enhanced Thematic Mapper (ETM+) multispectral image. The residential impervious surface was then derived by geographic information system (GIS) overlay of residential land class and impervious surface. Regression analysis was conducted to develop population density estimation models. We found that the residential impervious surface‐based approach provided the best population density estimation result, with mean and median relative errors of 38% and 23%, respectively. An overall population estimation error of ?0.97% was achieved.     

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.     

Landscape evolution in the Yellow River Basin using satellite remote sensing and GIS during the past decade

Over the past decade, rapid landscape pattern change has taken place in many arid and semi-arid regions of China, such as the Yellow River Basin. In this paper, landscape evolution was investigated by the combined use of satellite remote sensing, geographic information system (GIS) and landscape modelling technologies. The aim was to improve our understanding of landscape changes so that sustainable land use could be established. First, the changes in various landscape metrics were analysed using the landscape structure analysis programme. Second, the mathematical methodology was explored and developed for landscape pattern change, which included: the status and trends change model for individual landscape types, the 1-km² area percentage data model and the transition matrix of landscape types. The results show that the area of the Yellow River Basin was about 794 000 km² during the period from 1990 to 2000; cropland, built-up land and unused land expanded significantly whereas woodland, grassland and water bodies contracted substantially. The area of cropland increased dramatically by 2817 km², and the areas of grassland and woodland decreased by 4669 and 33 km², respectively. Meanwhile, the landscape pattern in the study area also experienced numerous changes over the past decade. The major factors that caused the landscape changes in this area over the past decade were found to be governmental policies for environmental protection, population growth, and meteorological and environmental conditions.     

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.     

A structured approach to the analysis of remote sensing images

The number of studies for the analysis of remote sensing images has been growing exponentially in the last two decades. Many studies, however, only report results – in the form of certain performance metrics – by a few selected algorithms on a training and testing sample. While this often yields valuable insights, it tells little about some important aspects. For example, one might be interested in understanding the nature of a study by the interaction of algorithm, features, and the sample as these collectively contribute to the outcome; among these three, which would be a more productive direction in improving a study; how to assess the sample quality or the value of a set of features, etc.. With a focus on land-use classification, we advocate the use of a structured analysis. The output of a study is viewed as the result of interplay among three input dimensions: feature, sample, and algorithm. Similarly, another dimension, the error, can be decomposed into error along each input dimension. Such a structural decomposition of the inputs or error could help better understand the nature of the problem and potentially suggest directions for improvement. We use the analysis of a remote sensing image at a study site in Guangzhou, China, to demonstrate how such a structured analysis could be carried out and what insights it generates. We expect this will inform practice in the analysis of remote sensing images, and help advance the state-of-the-art of land-use classification.     

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.     

Analysis of a small agricultural watershed using remote sensing techniques

Salinization of land and sweet water is an increasing problem worldwide. In the Carpathian Basin, particularly in arid and semi‐arid regions, irrigation is a contributing factor to the secondary salinization problems, one of the major problems affecting soils in Hungary. Conventional broadband sensors such as SPOT, Landsat MSS, and Landsat ETM+ are not suitable for mapping soil properties, because their bandwidth of 100–200 mm cannot resolve diagnostic spectral features of terrestrial materials. Analytical techniques, developed for analysis of broadband spectral data, are incapable of taking advantage of the full range of information present in hyperspectral remote sensing imagery. In our pilot project in Tedej farm in the Great Plain Region, Hungary, the DAIS sensor was used to assess salinity risk, covering the spectral range from the visible to the thermal infrared wavelengths at 5 m spatial resolution, and other major indicators of soil salinization (NDVI, SAVI, canopy cover) were quantified with advanced remote sensing techniques using the TETRACAM ADC agricultural multispectral camera which offers red/green and NIR imaging at megapixel resolution. As a result, prominent absorption bands around 1450 nm and 1950 nm wavelength in most soil spectra are attributed to water and hydroxyl ions. Occasional weaker absorption bands caused by water also occur at 970, 1200, and 1700 nm. Absorption features near the 400 nm wavelength for all samples are also noticeable. Absorption bands at 1800 and 2300 nm are attributed to gypsum, while strong absorption features near 2350 nm are assigned to calcite (CaCo₃). Saline soils exhibited significantly higher reflectance values all throughout the 325–2500 nm wavelengths of the spectrum. Soils with a high amount of soluble salts gave a higher average reflectance than soils with a low salt content. In the project, an ADC camera‐based real‐time integrated system was developed to take advantage of more specialized spectral information and to provide even more accurate and useful data directly from the field. The results revealed that the NDVI and SAVI index and the canopy cover mapping taken with multispectral cameras can be useful as an indirect marker and help for detecting salinization. However, we did not find a strong correlation between NDVI and soil salinity. This is probably because the detection and assessment of lower levels of salinity are difficult, mainly owing to the nature of the remotely sensed images; with such images, it is not possible to obtain information on the third dimension of the 3‐D soil body. Also, the impact of salinity on electromagnetic properties needs to be explored further to understand how it can be derived indirectly from remotely sensed information. With the rapid validation of remotely sensed hyperspectral data, the decision in the future, with the best trade‐off between irrigation and sustainable land use made by agricultural specialists in this region, can be more environmentally sound and more accurate using the results from the pilot.     

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.     

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.     

Flood monitoring using microwave passive remote sensing (AMSR-E) in part of the Brahmaputra basin,India

In this work, Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) signatures were analysed over some critical sites in Lakhimpur District in Brahmaputra basin, India, characterized by a high frequency of flooding events. The site is mostly covered by paddy fields. Results obtained were compared with water level measurements in three stations close to the main channel of the river. Information about surface temperature, which allowed us to estimate the emissivity, was also available. Investigations were carried out at the C, X, and K_a bands of the AMSR-E channel. A multi-frequency analysis indicated that the X band would represent a good compromise between resolution and sensitivity requirements, while at the C band the resolution was too coarse and at the K_a band the signatures were affected by raindrops. Samples collected during rain were eliminated using techniques based on the 89.0 GHz channel. However, even after this correction, the K_a band showed poor sensitivity due to higher attenuation by vegetation. The correlations between different pairs of variables, viz. polarization index (PI), water level (WL), and fractional water surface area (F _WS), were also investigated. At the X band, the water level was better correlated with the PI than with emissivity and other parameters defined in the literature. The correlation was good in cases of slow variation in WL. In cases of sudden variation in the river, the PI followed the variations with some time delay related to the propagation of water within the covered AMSR-E pixel.     

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.