Spatial characterization of electrical power consumption patterns over India using temporal DMSP‐OLS night‐time satellite data

Changes in electric power consumption patterns of a country over a period of time reflect on its socio‐economic development and energy utilization processes. In the present study, we characterized spatial and temporal changes in electric power consumption patterns over India during 1993 to 2002, using ‘night‐time lights’ data given by the Defense Meteorological Satellite Program–Operational Line Scan System (DMSP‐OLS) over the Indian region. The OLS operates in two bands: visible (0.5–0.9 µm) and thermal (10.5–12.5 µm) and has a unique capability of picking up faint sources of visible–near infrared emissions (lights) at night on the Earth's surface including cities, towns and villages with a DN value ranging from 1 to 63. Night‐time light images for cloud‐free dates given by the DMSP‐OLS from 1993 to 2002 were segregated into respective years and were integrated to generate one ‘Stable light image’ per year. Changes in light scenarios over the Indian region in the decadal time frame were studied using stable lights datasets from 1993 to 2002. Information on changes in the light scenarios was integrated with demographic data to characterize developments in major cities and states of India. Results of the study suggested an increase in population by 170 million and power consumption from 44962 million kWh to 306355 million kWh over the country during 1993–2002, which was associated with an overall increase in number of night‐time lights of up to 26% in all states, indicating development in electric power consumption patterns. Correlation analysis between increase in population to the increase in night‐time lights and electric power consumption showed a coefficient of determination, R ², of 0.59 and 0.56 respectively. Increase in light intensities along the peripheries of major Indian cities was observed, which indicated increased stress on the cities and corresponding development in power consumption patterns during the decadal time frame. Certain states, however, showed a decrease in night‐time lights in some areas, which are primarily attributed to the decreased economic growth trend and poverty and accounted to the scatter observed in the correlation analysis. Results are discussed in the paper.     

Discrimination of invaded and native species sites in a semi‐desert grassland using MODIS multi‐temporal data

Over the past several decades, one of the most significant changes in semi‐desert grasslands of the southwestern US has been the invasion of South African grass Eragrostis lehmanniana. The objective of this study was to characterize the phenology of systems occupied by E. lehmanniana and/or native grasses using time‐series of field observations and the Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index (MODIS NDVI) and brightness (red and near‐infrared reflectance) data. Results demonstrated that it was possible to use NDVI and/or spectral reflectance data to discern the phenological differences across a gradient of E. lehmanniana infested grasslands due to variations in plant biodiversity, morphology and seasonal productivity. This work establishes the feasibility of integrating field and MODIS vegetation and spectral time‐series data to characterise landscapes dominated by different herbaceous species, which in turn provides opportunities to monitor E. lehmanniana in semi‐arid environments at a large spatial scale.     

Analysis of urban growth using multi‐temporal satellite data in Istanbul,Turkey

Uncontrolled population growth, especially in developing countries, causes serious problems, such as scarcity of food, informal settlements, environmental pollution, destruction of ecological structure, unemployment, etc. This phenomenon will require advanced methodologies, such as space technologies, to enable city planners, economists, environmentalists, ecologists and resource managers to solve these problems. In Turkey, as a result of the undesired population growth, new settlements are continuously appearing and adverse developments and changes are occurring in the presently populated areas. In Turkey's major cities, such as in Istanbul, Izmir, Ankara, Adana, Bursa and Antalya, which have seen mass migrations of people, considerable urban developments and changes have occurred. Consequently, the mostly negative impacts of uncontrolled population growth on the urban environment must be monitored continuously.

The objective of this study was to investigate the impact of urban growth on land‐use changes, especially the agricultural land in the district of Büyükçekmece in suburban Istanbul. The study is based on the 1984–1997 population database, multi‐temporal satellite data and remote sensing methods. The study revealed significant loss of agricultural and natural land areas to urban developments throughout the period 1984–1998.     

Mapping forest successional stages following deforestation in Brazilian Amazonia using multi‐temporal Landsat images

Tropical forest successional stages have been mapped previously with multi‐temporal satellite sensor imagery. The precise identification and classification of such stages, however, has proved difficult. This Letter presents a new method for the classification of forest successional stages following deforestation in Brazilian Amazonia. Multi‐temporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) and derived fraction images and field data were used in a semi‐automatic classification approach. The results were encouraging and signal the application of the method for the entire Brazilian Amazonia.     

Land degradation monitoring using multi‐temporal Landsat TM/ETM data in a transition zone between grassland and cropland of northeast China

Land degradation is one of the most pressing problems of environments. This research presents a methodology to monitor land degradation in a transition zone between grassland and cropland of northeast China, where soil salinization and grassland degradation, even desertification, have been observed in the past few decades. Landsat TM/ETM data in 1988, 1996 and 2001 were selected to determine the rate and status of grassland degradation and soil salinization together based on both decision tree (DT) classifier and the field investigation. The thermal radiance values of TM/ETM 6 data, the Normalized Difference Vegetation Index (NDVI), and new variables (brightness, greenness, and wetness) generated by the Kauth–homas Transforms (KT) algorithms from Landsat TM/ETM data served as the feature nodes of a DT classifer and contributed to improving the classification results. It showed an overall accuracy of more than 85% and a Kappa statistic of agreement of about 0.79 in 1996 and 2001 with the exception of about 0.69 in 1988. The statistical areas of land degradation in the observation periods revealed that land degradation, especially the salt‐affected soil, is accelerating. The distribution maps of land degradation in the years of 1988, 1996 and 2001 were generated respectively based on the classification results. Their change maps were created by the difference between the distribution maps from 1988 to 1996 and from 1996 to 2001 respectively. The changes of salt‐affected soil occurred near the water bodies due to variations of water sizes, and most of the degraded grassland appeared around the salt‐affected soil. Although climate variations play an important role in this region, human activities are also crucial to land degradation.     

Detection of coal fire location and change based on multi‐temporal thermal remotely sensed data and field measurements

This study focuses on analysis methods for monitoring coal fires, using a combination of multi‐temporal thermal infrared data, high spatial resolution remote sensing data and field measurements. This technical note is prepared as a feasibility study for the detection of coal fire dynamics in the Inner Mongolia Autonomous Region in northern China.     

Temporal and spatial soil moisture change pattern detection in an agricultural area using multi‐temporal Radarsat ScanSAR data

Monitoring the characteristics of spatially and temporally distributed soil moisture is important to the study of hydrology and climatology for understanding and calculating the surface water balance. The major difficulties in retrieving soil moisture with Synthetic Aperture Radar (SAR) measurements are due to the effects of surface roughness and vegetation cover. In this study we demonstrate a technique to estimate the relative soil moisture change by using multi‐temporal C band HH polarized Radarsat ScanSAR data. This technique includes two components. The first is to minimize the effects of surface roughness by using two microwave radar measurements with different incidence angles for estimation of the relative soil moisture change defined as the ratio between two soil volumetric moistures. This was done by the development of a semi‐empirical backscattering model using a database that simulated the Advanced Integral Equation Model for a wide range of soil moisture and surface roughness conditions to characterize the surface roughness effects at different incidence angles. The second is to reduce the effects of vegetation cover on radar measurements by using a semi‐empirical vegetation model and the measurements obtained from the optical sensors (Landsat TM and AVHRR). The vegetation correction was performed based on a first‐order semi‐empirical backscattering vegetation model with the vegetation water content information obtained from the optical sensors as the input. For the validation of this newly developed technique, we compared experimental data obtained from the Southern Great Plain Soil Moisture Experiment in 1997 (SGP97) with our estimations. Comparison with the ground soil moisture measurements showed a good agreement for predication of the relative soil moisture change, in terms of ratio, with a Root Mean Square Error (RMSE) of 1.14. The spatially distributed maps of the relative soil moisture change derived from Radarsat data were also compared with those derived from the airborne passive microwave radiometer ESTAR. The maps of the spatial characteristics of the relative soil moisture change showed comparable results.     

Regional thermal and terrain modelling of the Afar Depression from MODIS multi‐temporal monthly night LST data

Multi‐temporal MODIS thermal imagery isolates geothermal from seasonal land surface temperature (LST) variability in Afar Depression and identifies localized LST thermal anomalies during the September 2005 seismo‐volcanic crisis. The interpretation of the monthly night annual imagery sequences and time series analysis proved that in the northern and the central parts, LST is maximized throughout the year. The two regions lie along the NW‐SE trending Red Sea Propagator, which is the on‐land southern termination of the Red Sea Rift. Linear regression analysis proved that LST variation is terrain elevation dependent. Terrain segmentation outlined 5 terrain classes. The localized geothermal activity during the September 2005 seismic‐volcanic crisis activity in both Erta Ale and Dabbahu vicinity was revealed by combined terrain‐LST modelling. The LST statistics of the segmented terrain classes revealed that in September 2005, a regional minor increase in LST also affected the four classes with positive elevation.     

Predicting winter wheat condition,grain yield and protein content using multi‐temporal EnviSat‐ASAR and Landsat TM satellite images

Since optical and microwave sensors respond to very different target characteristics, their role in crop monitoring can be viewed as complementary. In particular, the all‐weather capability of Synthetic Aperture Radar (SAR) sensors can ensure that data gaps that often exist during monitoring with optical sensors are filled. There were three Landsat Thematic Mapper (TM) satellite images and three Envisat Advanced Synthetic Aperture Radar (ASAR) satellite images acquired from reviving stage to milking stage of winter wheat. These data were successfully used to monitor crop condition and forecast grain yield and protein content. Results from this study indicated that both multi‐temporal Envisat ASAR and Landsat TM imagery could provide accurate information about crop conditions. First, bivariate correlation results based on the linear regression of crop variables against backscatter suggested that the sensitivity of ASAR C‐HH backscatter image to crop or soil condition variation depends on growth stage and time of image acquisition. At the reviving stage, crop variables, such as biomass, Leaf Area Index (LAI) and plant water content (PWC), were significantly positively correlated with C‐HH backscatter (r = 0.65, 0.67 and 0.70, respectively), and soil water content at 5 cm, 10 cm and 20 cm depths were correlated significantly with C‐VV backscatter (r = 0.44, 0.49 and 0.46, respectively). At booting stage, only a significant and negative correlation was observed between biomass and C‐HH backscatter (r = ?0.44), and a saturation of the SAR signal to canopy LAI could explain the poor correlation between crop variables and C‐HH backscatter. Furthermore, C‐HH backscatter was correlated significantly with soil water content at booting and milking stage. Compared with ASAR backscatter data, the multi‐spectral Landsat TM images were more sensitive to crop variables. Secondly, a significant and negative correlation between grain yield and ASAR C‐HH & C‐VV backscatter at winter wheat booting stage was observed (r = ?0.73 and ?0.55, respectively) and a yield prediction model with a correlation coefficient of 0.91 was built based on the Normalized Difference Water Index (NDWI) data from Landsat TM on 17 April and ASAR C‐HH backscatter on 27 April. Finally, grain protein content was found to be correlated significantly with ASAR C‐HH backscatter at milking stage (r = ?0.61) and with Structure Insensitive Pigment Index (SIPI) data from Landsat TM at grain‐filling stage (r = 0.53), and a grain protein content prediction model with a correlation coefficient of 0.75 was built based on the C‐HH backscatter and SIPI data.     

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.     

Inferences of all‐sky solar irradiance using Terra and Aqua MODIS satellite data

Solar irradiance is a key environmental control, and accurate spatial and temporal solar irradiance data are important for a wide range of applications related to energy and carbon cycling, weather prediction, and climate change. This study presents a satellite‐based scheme for the retrieval of all‐sky solar irradiance components, which links a physically based clear‐sky model with a neural network version of a rigorous radiative transfer model. The scheme exploits the improved cloud characterization and retrieval capabilities of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and employs a cloud motion tracking scheme for the production of hourly solar irradiance data throughout the day. The scheme was implemented for the Island of Zealand, Denmark (56° N, 12° E) and Southern Arizona, USA (31° N, 110° W) permitting model evaluation for two highly contrasting climates and cloud environments. Information on the atmospheric state was provided by MODIS data products and verifications against AErosol RObotic NETwork (AERONET) data demonstrated usefulness of MODIS aerosol optical depth and total precipitable water vapour retrievals for the delineation of spatial gradients. However, aerosol retrievals were significantly biased for the semi‐arid region, and water‐vapour retrievals were characterized by systematic deviations from the measurements. Hourly global solar irradiance data were retrieved with overall root mean square deviations of 11.5% (60 W m^?2) and 26.6% (72 W m^?2) for Southern Arizona and the Island of Zealand, respectively. For both regions, hourly satellite estimates were shown to be more reliable than pyranometer measurements from ground stations only 15 km away from the point of interest, which is comparable to the accuracy level obtainable from geostationary satellites with image acquisitions every 15–30 min. The proposed scheme is particularly useful for solar irradiance mapping in high‐latitude regions as data from geostationary satellites experience a gradual degradation in spatial resolution and overall quality with latitude and become unusable above approximately 60° latitude. However, in principle, the scheme can be applied anywhere on the globe, and a synergistic use of MODIS and geostationary satellite datasets may be envisaged for some applications.     

Comparison of SeaWiFS,MODIS‐Terra and MODIS‐Aqua in the Southern Ocean

The surface chlorophyll‐a concentrations measured by SeaWiFS, MODIS‐Terra and MODIS‐Aqua are compared in the Southern Ocean in summer 2003. The radiometers generally agree within their estimated accuracy. Residual discrepancies could be reduced by regional calibrations of the bio‐optical algorithms.     

Estimating post‐fire organic soil depth in the Alaskan boreal forest using the Normalized Burn Ratio

As part of a long‐term moose browse/fire severity study, we used the Normalized Burn Ratio (NBR) with historic Landsat Thematic Mapper (TM) imagery to estimate fire severity from a 1983 wildfire in interior Alaska. Fire severity was estimated in the field by measuring the depth of the organic soil at 57 sites during the summer of 2006. Sites were selected for field sampling from five fire severity classes based on threshold NBR values. The linear relationship between post‐fire NBR and organic soil depth among sites within the burn was weak (r ² = 0.26), and improved substantially (r ² = 0.66) when restricted to non‐wetland black spruce sites. The relationship between NBR and aspen/willow counts was non‐linear. Sites with high densities of aspen stems consistently occurred in the high fire severity classes, and sites with high willow stem densities consistently occurred in the moderate fire severity class. However, NBR varied substantially from sites with low aspen or willow reproduction and therefore predicting aspen or willow regeneration based on post‐fire NBR values would be difficult.     

Airborne traffic monitoring in large areas using LiDAR data – theory and experiments

This article investigates the theoretical background for airborne LiDAR (light detection and ranging) and ALS (airborne laser scanning) systems that are used to monitor traffic from airborne platforms. An object moving with a velocity deviating from the assumptions incorporated in the scanning process will generally appear both stretched and sheared – motion artefacts. To study the impact of these deformations on the ALS data, the analytic relations between an arbitrarily moving object and its conjugate in the ALS data have been examined and adapted to concrete airborne specifications. Furthermore, a complete scheme is proposed to analyse urban traffic in real-life situations, which combines vehicle detection successively with the motion classification method, which is the main focus of this article. Finally, the velocity of the moving vehicle can be derived with knowledge about the vehicle shape. The experimental results obtained by using real ALS data were assessed with respect to the reference data concurrently acquired by a video camera to validate the theory.     

Evaluation of the B‐method for determining actual evapotranspiration in a boreal forest from MODIS data

Boreal forests occupy about 11% of the terrestrial surface and represent an important contribution to global energy balance. The ground measurement of daily evapotranspiration (LE_d) is very difficult due to the limitations on experiments. The objective of this paper is to present and explore the applicability of the B‐method for monitoring actual LE_d in these ecosystems. The method shown in this paper allows us to determine the surface fluxes over boreal forests on a daily basis from instantaneous information registered in a conventional meteorological tower, as well as the canopy temperature (T _c) retrieved by satellite. Images collected by the MODIS (moderate resolution imaging spectroradiometer) on board EOS‐Terra have been used for this study. The parameters of the model were calibrated from the SIFLEX‐2002 (Solar Induced Fluorescence Experiment 2002) campaign dataset in a northern boreal forest in Finland. A study of these parameters was made on an hourly basis in order to make the method applicable, not only at midday but within an interval of 7 h around it. This is an important advance with respect to the original formulation of this approach since the overpass time of satellites can be very variable. The comparison between T _c ground measured with a thermal infrared radiometer, and T _c retrieved from land surface temperature (LST) MODIS data, showed an estimation error of ±1.4°C for viewing angles from 5 to 60°. A complete sensitivity analysis was carried out and an estimation error of about ±35%, corresponding to the interval 10.00–11.00 h UTC, was shown as the lowest in LE_d retrieval. Finally, the method was validated over the study site using 21 MODIS images for 2002 and 2003. The results were compared with eddy‐correlation ground measurements. An accuracy of ±1.0 mm/day and an overestimation of 0.3 mm/day were shown in the LE_d retrieval.     

A comparative analysis of the use of NOAA‐AVHRR NDVI and FWI data for forest fire risk assessment

Fires are a major hazard to forests in the Mediterranean region, where, on average, half a million hectares of forested areas are burned every year. The assessment of fire risk is therefore at the heart of fire prevention policies in the region. The estimation of forest fire risk often involves the integration of meteorological and other fuel‐related variables, leading to an index that assesses the different levels of risk. Two indices frequently used to estimate the level of fire risk are the Fire Weather Index (FWI) and the Normalized Difference Vegetation Index (NDVI). Although a correlation between the number of fires and the level of risk determined by these indices has been demonstrated in previous studies, the analyses focused on the changes in fire risk levels in areas where fires took place. The present study analyses the behaviour of the fire risk indices not only in areas where fires occurred but also in areas where fires did not take place. Specifically, the objective of this work was to compare the potential of the two indices to discriminate different levels of fire risk over large areas. Qualitative and quantitative methods were used to compare the statistical distributions of fire event frequencies with those of fire risk levels. The qualitative method highlights graphically the statistical difference between the values of the indices computed over burnt areas and the overall distribution of the values of the indices. The quantitative method, based on the use of the so‐called performance index, was used to evaluate and compare numerically the potential of the indices. The analyses were performed considering very extensive datasets of fire events, satellite data and meteorological data for Spain during a 10‐year period. Although the NDVI is assumed to describe the vegetation status as related to fire ignition, the results show conclusively an enhanced performance of the FWI over the NDVI in identifying areas at risk of fires.     

Monitoring Sahelian floodplains using Fourier analysis of MODIS time‐series data and artificial neural networks

Fourier analysis of Moderate Resolution Image Spectrometer (MODIS) time‐series data was applied to monitor the flooding extent of the Waza‐Logone floodplain, located in the north of Cameroon. Fourier transform (FT) enabled quantification of the temporal distribution of the MIR band and three different indices: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Enhanced Vegetation Index (EVI). The resulting amplitude, phase, and amplitude variance images for harmonics 0 to 3 were used as inputs for an artificial neural network (ANN) to differentiate between the different land cover/land use classes: flooded land, dry land, and irrigated rice cultivation. Different combinations of input variables were evaluated by calculating the Kappa Index of Agreement (KIA) of the resulting classification maps. The combinations MIR/NDVI and MIR/EVI resulted in the highest KIA values. When the ANN was trained on pixels from different years, a more robust classifier was obtained, which could consistently separate flooded land from dry land for each year.     

Coastal geomorphological and land‐use and land‐cover study of Sagar Island,Bay of Bengal (India) using remotely sensed data

Morphological changes on Sagar Island are occurring at an alarming rate due to both natural and anthropogenic activities. The eastern part of the island is rapidly eroding due to destabilization and growth of tidal flats in the Muriganga estuary and the gradual shifting of water current towards the island. Over the last four years (1996–1999), the rate of coastal erosion has been much higher (11.35 km²) than accretion (2.65 km²), compared with the conditions prior to 1996. Coastal places like Dublat, Basantpur, Gobindapur, Collectorganj, and Sumatinagar have become the critical zones of erosion. The shorelines along the eastern and south‐western sides are receding. The extent of coverage of the paddy field, sandy beaches, and land vegetation has decreased from 1996 to 1999 by 15.7, 1.1, and 3.5 km², respectively. An integrated database of the island was generated using spatial and non‐spatial data collected through field survey, satellite images of IRS‐1C LISS III, and topomaps. Spatial data include coastal geomorphological landforms, land‐use and land cover, shoreline change, sandy beaches, coastal erosion sites, agricultural fields, aquaculture sites, and coastal riparian vegetations. Non‐spatial data include the demography and evolution of the island. The main critical environmental issues of the island are: (1) degradation of mangrove forests and coastal erosion; (2) overpopulation and over‐exploitation of living resources; and (3) destruction of seawalls. Further degradation may lead to extinction of a variety of species and scarcity of marine food unless properly managed and regulated.     

Delineating compositionally different dykes in the Ulukışla basin (Central Anatolia,Turkey) using computer‐enhanced multi‐spectral remote sensing data

In the Uluk??la basin (Central Anatolia, Turkey) several geological mapping campaigns were carried out using conventional field methods to delineate compositionally different Middle–Upper Eocene dykes. However, complete and correct mapping of these dykes was hampered by rugged terrain, lack of road access, wide spatial dyke distributions with small exposures and diverse weathering of these dykes. For these reasons, Landsat‐5 Thematic Mapper (TM) satellite image of the study area was used to facilitate delineation of the exact boundaries of gabbroic, dioritic and trachytic dykes found in the area. Remotely sensed data were analysed using several image enhancement procedures, including colour composites, band ratios, principal components analysis (PCA), and Crosta technique. Results obtained from all the processes were examined, and it was found that dyke boundaries are best visible in the PCA123 image; RGB 731 colour composite; TM band ratio 5/7, 5/1, 4 combination; and 1457‐PC4 image obtained by Crosta technique. The alteration differences of three dyke groups are enhanced much better in the 1457‐PC4 image obtained by Crosta technique, which highlights the hydroxyl‐bearing minerals as white‐coloured pixels. Using computer‐enhanced multi‐spectral remote sensing data, we were able to map the boundaries and spatial distributions of compositionally different dykes, which otherwise is an overwhelmingly difficult task to achieve using conventional field methods. In similar settings, remote sensing techniques applied in this study may provide an efficient and low‐cost alternative to time‐consuming and physically demanding field‐mapping campaigns.