High Resolution Satellite Datasets Research Articles

Geospatial assessment of forest fire impacts utilizing high-resolution KazEOSat-1 satellite data

Forest fires or wildfires frequently occur in Kazakhstan, especially in the months from June to September, damaging the forest resources. Burnt area mapping is important for fire managers to take appropriate mitigation steps and carry out restoration activities after the fire event. In this study, KazEOSat-1 high-resolution satellite datasets are used to map the burnt area in the regions of Kazakhstan. KazEOSat-1 satellite is in a Sun-synchronous orbit, consisting of four bands, namely blue, green, red, and NIR multispectral bands, in 4 m spatial resolution, while panchromatic data are in 1 m spatial resolution. This study examined three spectral indices, namely AVI, BAI, and GEMI, for mapping the burnt area based on the four spectral bands NIR, blue, red, and green of the KazEOSat-1 satellite datasets. The DN values for each band are used to determine TOA reflectance, which is then used as a basis for deriving the aforementioned spectral indices. The results of spectral indices, AVI, BAI, and GEMI are compared based on a discriminative index (M) for quantifying the effectiveness of each index based on burned area derived from KazEOSat-1 datasets. The spectral index BAI shows higher M values than other indices; therefore, the index BAI has the higher capability to extract the burned area as compared with AVI and GEMI. Accuracy was calculated based on the number of forest fire incidents that fell in burned and unburned areas, and the results indicate that BAI shows the highest accuracy, whereas AVI shows the lowest accuracy among them. Therefore, the BAI has the highest ability for extracting the burned area using the KazEOSat-1 satellite datasets. As the revisit time period of KazEOSat is 3 days, this study will be useful to map the burnt area and fire progression in Kazakhstan.

An Optimized Smoke Segmentation Method for Forest and Grassland Fire Based on the UNet Framework

Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification—an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle–high-resolution satellite datasets for forest and grassland fire smoke, and the associated challenges in identifying smoke, the CAF_SmokeSEG dataset was constructed for smoke segmentation. The dataset was created based on GF-6 WFV smoke images of forest and grassland fire globally from 2019 to 2022. Then, an optimized segmentation algorithm, GFUNet, was proposed based on the UNet framework. Through comprehensive analysis, including method comparison, module ablation, band combination, and data transferability experiments, this study revealed that GF-6 WFV data effectively represent information related to forest and grassland fire smoke. The CAF_SmokeSEG dataset was found to be valuable for pixel-level smoke segmentation tasks. GFUNet exhibited robust smoke feature learning capability and segmentation stability. It demonstrated clear smoke area delineation, significantly outperforming UNet and other optimized methods, with an F1-Score and Jaccard coefficient of 85.50% and 75.76%, respectively. Additionally, augmenting the common spectral bands with additional bands improved the smoke segmentation accuracy, particularly shorter-wavelength bands like the coastal blue band, outperforming longer-wavelength bands such as the red-edge band. GFUNet was trained on the combination of red, green, blue, and NIR bands from common multispectral sensors. The method showed promising transferability and enabled the segmentation of smoke areas in GF-1 WFV and HJ-2A/B CCD images with comparable spatial resolution and similar bands. The integration of high spatiotemporal multispectral data like GF-6 WFV with the advanced information extraction capabilities of deep learning algorithms effectively meets the practical needs for pixel-level identification of smoke areas in forest and grassland fire scenarios. It shows promise in improving and optimizing existing forest and grassland fire monitoring systems, providing valuable decision-making support for fire monitoring and early warning systems.

GAN-assisted Road Segmentation from Satellite Imagery

Geo-information extraction from satellite imagery has become crucial to carry out large-scale ground surveys in a short amount of time. With the increasing number of commercial satellites launched into orbit in recent years, high-resolution RGB color remote sensing imagery has attracted a lot of attention. However, because of the high cost of image acquisition and even more complicated annotation procedures, there are limited high-resolution satellite datasets available. Compared to close-range imagery datasets, existing satellite datasets have a much lower number of images and cover only a few scenarios (cities, background environments, etc. ). They may not be sufficient for training robust learning models that fit all environmental conditions or be representative enough for training regional models that optimize for local scenarios. Instead of collecting and annotating more data, using synthetic images could be another solution to boost the performance of a model. This study proposes a GAN-assisted training scheme for road segmentation from high-resolution RGB color satellite images, which includes three critical components: a) synthetic training sample generation, b) synthetic training sample selection, and c) assisted training strategy. Apart from the GeoPalette and cSinGAN image generators introduced in our prior work, this paper in detail explains how to generate new training pairs using OpenStreetMap (OSM) and introduces a new set of evaluation metrics for selecting synthetic training pairs from a pool of generated samples. We conduct extensive quantitative and qualitative experiments to compare different image generators and training strategies. Our experiments on the downstream road segmentation task show that 1) our proposed metrics are more aligned with the trained model performance compared to commonly used GAN evaluation metrics such as the Fréchet inception distance (FID); and 2) by using synthetic data with the best training strategy, the model performance, mean Intersection over Union (mean IoU), is improved from 60.92% to 64.44%, when 1,000 real training pairs are available for learning, which reaches a similar level of performance as a model that is standard-trained with 4,000 real images (64.59%), i.e. , enabling a 4-fold reduction in real dataset size.

Estimation of actual evapotranspiration using the simplified-surface energy balance index model on an irrigated agricultural farm

Evapotranspiration (ET) plays a crucial role in the energy and water balance of agricultural ecosystems and is a vital component of the hydrological cycle. Efficient irrigation water management relies on accurate spatiotemporal coverage of crop ET across a farm. Thanks to the availability of multi-temporal high-resolution satellite datasets and remote sensing-based surface energy balance models, near-real-time estimation of ET is now possible. This study utilized Landsat 8/9 data to estimate ET using the simplified surface energy balance index (S-SEBI) model, which was then compared to eddy covariance measurements over a semi-arid agricultural farm in New Delhi, India during the post-monsoon periods of 2021-22 and 2022-23. The S-SEBI model predicted daily ET from Landsat 8/9 data with an average correlation coefficient and RMSE of 0.89 and 0.79 mm/day, respectively. The spatiotemporal map was also used to evaluate the model's performance, and it could accurately differentiate between ET over dryland crops and well-irrigated wheat fields on the farm. Despite underestimating ET (0.51 mm/day) during the initial growing season (Nov-Dec) and overestimating it (0.73 mm/day) during mid-season (Feb-Mar), the S-SEBI model can still be an operational tool for mapping ET with high accuracy and sufficient variation across pixels, making it an ideal option for incorporating into irrigation scheduling.

Outlet glacier flow response to surface melt: based on analysis of a high-resolution satellite data set

The dynamics of the Greenland Ice Sheet are affected by surface meltwater reaching the base of the ice, altering ice contact with the bedrock. Lack of understanding of this evolution hampers the ability to predict the effects of increasing temperatures on the Greenland Ice Sheet mass balance. Here we present a unique high-resolution study of ice velocity response to surface melting based on data from a COSMO-SkyMed satellite campaign over Upernavik Isstrøm (Northwest Greenland) for two months around the end of the 2014 melt season. We show that the velocity variations, due to both short-term (days) and seasonal variations in surface melt rates, are increasing in relative strength farther from the glacier terminus. Furthermore, we observe how ice dynamic response to frontal retreat, reaching several kilometres inland, can obscure the meltwater-induced velocity change close to the terminus. Future studies should consider the flow velocity dependence on the distance to the terminus, and local geometry, to distinguish subglacial hydrologic system changes from frontal processes and local basal conditions.

Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data

Continuous observation and management of agriculture are essential to estimate crop yield and crop failure. Remote sensing is cost-effective, as well as being an efficient solution to monitor agriculture on a larger scale. With high-resolution satellite datasets, the monitoring and mapping of agricultural land are easier and more effective. Nowadays, the applicability of deep learning is continuously increasing in numerous scientific domains due to the availability of high-end computing facilities. In this study, deep learning (U-Net) has been implemented in the mapping of different agricultural land use types over a part of Punjab, India, using the Sentinel-2 data. As a comparative analysis, a well-known machine learning random forest (RF) has been tested. To assess the agricultural land, the major winter season crop types, i.e., wheat, berseem, mustard, and other vegetation have been considered. In the experimental outcomes, the U-Net deep learning and RF classifiers achieved 97.8% (kappa value: 0.9691) and 96.2% (Kappa value: 0.9469), respectively. Since little information exists on the vegetation cultivated by smallholders in the region, this study is particularly helpful in the assessment of the mustard (Brassica nigra), and berseem (Trifolium alexandrinum) acreage in the region. Deep learning on remote sensing data allows the object-level detection of the earth’s surface imagery.

Design of an optimized deep learning algorithm for automatic classification of high-resolution satellite dataset (LISS IV) for studying land-use patterns in a mining region

Accurate classification of the satellite image is always a challenging task, and hence uncertainty involves in the change detection analysis in the land use for mining regions. The present study attempts to demonstrate the development of an optimized deep convolutional neural network with stochastic gradient descent momentum (DCNN + SGDM) using Linear imaging self-scanning sensor- IV (LISS-IV) data for automatic classification of satellites image to assess the land-use pattern in a mining region. An image dataset was prepared by extracting the LISS-IV image samples of five common land-use types (coal mining region, built-up area, barren land, vegetation, and waterbody). A total of 6250 image samples (1250 for each class) of 6 × 6 patch size were generated for the model training and testing. The study area selected for analyzing the model performance and impact analysis is Jharia coalfield (JCF), Jharkhand. The study area consists of 5184 × 7794 pixels. The model performance results indicated that classification accuracies on the training and testing dataset are 99.04% and 83.50%, respectively. The model performance was also compared to the performance reported in the well-established deep learning algorithms, including Alex Net, Squeeze Net, Google Net, VGG Net 16, and VGG Net 19. It was observed that the DCNN + SGDM model performs better (99.04%) than the existing deep learning models (73.8% in Alex Net, 80.3 in Squeeze Net, 93.33 in Google Net, 93.20% in VGG Net 16, and 93.00 in VGG Net 19). Moreover, the DCNN + SGDM model's performance was compared with the two pixel-based classification algorithms (support vector machine (SVM) and deep neural network (DNN)) in terms of accuracy and processing time. The comparative values of accuracies are 70.40% for SVM, 69.10% for DNN, 99.04% for DCNN on the training dataset, and the respective elapsed time of the SVM, DNN, and DCNN models are 1.716 s, 3.59 s, and 1587 s. That is, the processing time of the DCNN model is high but offers higher accuracy in comparison to the pixel-based classification algorithms (SVM and DNN).

Inverting ice surface elevation and velocity for bed topography and slipperiness beneath Thwaites Glacier

Abstract. There is significant uncertainty over how ice sheets and glaciers will respond to rising global temperatures. Limited knowledge of the topography and rheology of the ice–bed interface is a key cause of this uncertainty as models show that small changes in the bed can have a large influence on predicted rates of ice loss. Most of our detailed knowledge of bed topography comes from airborne and ground-penetrating radar observations. However, these direct observations are not spaced closely enough to meet the requirements of ice-sheet models, so interpolation and inversion methods are used to fill in the gaps. Here we present the results of a new inversion of surface elevation and velocity data over Thwaites Glacier, West Antarctica, for bed topography and slipperiness (i.e. the degree of basal slip for a given level of drag). The inversion is based on a steady-state linear perturbation analysis of the shallow-ice-stream equations. The method works by identifying disturbances to surface flow which are caused by obstacles or sticky patches in the bed and can therefore be applied wherever the shallow-ice-stream equations hold and where surface data are available, even where the ice thickness is not well known. We assess the performance of the inversion for topography with the available radar data. Although the topographic output from the inversion is less successful where the bed slopes steeply, it compares well with radar data from the central trunk of the glacier for medium-wavelength features (5–50 km). This method could therefore be useful as an independent test of other interpolation methods such as mass conservation and kriging. We do not have data to allow us to assess the success of the slipperiness results from our inversions, but we provide maps that may guide future seismic data collection across Thwaites Glacier. The methods presented here show significant promise for using high-resolution satellite datasets, calibrated by sparser field datasets, to generate high-resolution bed topography products across the ice sheets and therefore contribute to reduced uncertainty in predictions of future sea-level rise.

HrreNet: semantic segmentation network for moderate and high-resolution satellite images

ABSTRACT Semantic segmentation is a commonly used intelligent interpretation method. However, semantic segmentation via deep learning usually faces several problems: resolution loss due to downsampling, difficulty in obtaining context information, and imprecise object boundaries. These problems lead to a poor performance when using satellite images instead of conventional images and limit the value of intelligent interpretation of satellite images in practical application. In this study, we construct a Qilian Ecological Resource Extraction Dataset (QERED) with multiple scene satellite images of Qilian after preprocessing via correction, fusion, mosaic, and annotation. We then propose a segmentation network, namely, High-resolution Resource Extracting Network (HrreNet), by using high-resolution feature representation, multi-scale context fusion, boundary refinement with relearning, and structural similarity loss. Experiments on QERED show that HrreNet greatly improves performance on small-size objects and slightly improves performance on larger-size objects. HrreNet achieves the best result of 73.36% mIoU on a moderate-resolution satellite dataset CCFD and 80.87% mIoU on a high-resolution satellite dataset Vaihingen.

Development of a Phenology-Based Method for Identifying Sugarcane Plantation Areas in China Using High-Resolution Satellite Datasets

Sugarcane is an important sugar and biofuel crop with high socio-economic importance, and its planted area has increased rapidly in recent years. China is the world’s third or fourth sugarcane producer. However, to our knowledge, no study has investigated the mapping of sugarcane cultivation areas across entire China. In this study, we developed a phenology-based method to identify sugarcane plantations in China at 30-m spatial resolution from 2016–2020 using the time-series of Landsat and Sentinel-1/2 images derived from Google Earth Engine (GEE) platform. The method worked by comparing the phenological similarity in normalized difference vegetation index (NDVI) series between unknown pixels and sugarcane samples. The phenological similarity was assessed using the time-weighted dynamic time warping method (TWDTW), which has less sensitivity to training samples than machine learning methods and therefore can be easily applied to large areas with limited samples. More importantly, our method introduced multiple and moving time standard phenological curves of sugarcane to the TWDTW by fully considering the variable crop life-cycle of sugarcane, particularly its long harvest season spanning from December to March of the following year. Validations showed the method performed well in 2019, with overall accuracies of 93.47% and 92.74% for surface reflectance (SR) and top of atmosphere reflectance (TOA) data, respectively. The sugarcane maps agreed well with the agricultural statistical areas from 2016–2020. The mapping accuracies using TOA data were comparable to SR data in 2019–2020, but outperformed SR data in 2016–2018 when SR data had lower availability on GEE. The sugarcane maps produced in this study can be used to monitor growing conditions and production of sugarcane and, therefore, can benefit sugarcane management, sustainable sugarcane production, and national food security.

Doubling of annual forest carbon loss over the tropics during the early twenty-first century

Previous estimates of tropical forest carbon loss in the twenty-first century using satellite data typically focus on its magnitude, whereas regional loss trajectories and associated drivers are rarely reported. Here we used different high-resolution satellite datasets to show a doubling of gross tropical forest carbon loss worldwide from 0.97 ± 0.16 PgC yr−1 in 2001–2005 to 1.99 ± 0.13 PgC yr−1 in 2015–2019. This increase in carbon loss from forest conversion is higher than in bookkeeping models forced by land-use statistical data, which show no trend or a slight decline in land-use emissions in the early twenty-first century. Most (82%) of the forest carbon loss is at some stages associated with large-scale commodity or small-scale agriculture activities, particularly in Africa and Southeast Asia. We find that ~70% of former forest lands converted to agriculture in 2001–2019 remained so in 2020, confirming a dominant role of agriculture in long-term pan-tropical carbon reductions on formerly forested landscapes. The acceleration and high rate of forest carbon loss in the twenty-first century suggest that existing strategies to reduce forest loss are not successful; and this failure underscores the importance of monitoring deforestation trends following the new pledges made in Glasgow.

ICESat-2 Applications for Investigating Emerging Volcanoes

Submarine volcanism in shallow waters (<100 m), particularly in remote settings, is difficult to monitor quantitatively and, in the rare formation of islands, it is challenging to understand the rapid-paced erosion. However, these newly erupted volcanic islands become observable to airborne and/or satellite remote sensing instruments. NASA’s ICESat-2 satellite laser altimeter, combined with visible imagery (optical and microwave), provide a novel method of evaluating the elevation characteristics of newly emerged volcanoes and their subaerial eruption products. Niijima Fukutoku-Okanoba (NFO) is a submarine volcano 1300 km south of Tokyo (Ogasawara Archipelago of Japan) that periodically breaches the ocean surface to create new islands that are subsequently eroded. The recent eruption in August 2021 is a rare opportunity to investigate this island evolution using high-resolution satellite datasets with geodetic-quality ICESat-2 altimetry. Lansdat-8 and Planet imagery provide a qualitative analysis of the exposed volcanic deposits, while ICESat-2 products provide elevation profiles necessary to quantify the physical surface structures. This investigation determines an innovative application for ICESat-2 data in evaluating newly emerged islands and how the combination of satellite remote sensing (visible and lidar) to investigate these short-lived volcanic features can improve our understanding of the volcanic island system in ways not previously possible.

Modeling the Near-Surface Energies and Water Vapor Fluxes Behavior in Response to Summer Canopy Density across Yanqi Endorheic Basin, Northwestern China

The Yanqi basin is the main irrigated and active agroecosystem in semi-arid Xinjiang, northwestern China, which further seeks responses to the profound local water-related drawbacks in relation to the unceasing landscape desiccation and scant precipitation. Yet, it comes as an astonishment that a few reported near-surface items and water vapor fluxes as so far required for water resources decision support, particularly in a scarce observation data region. As a contributive effort, here we adjusted the sensible heat flux (H) calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to high-resolution satellite dataset coupled with in-situ observation, through a wise guided “anchor” pixel assortment from surface reflectance-α, Leaf area index-LAI, vegetation index-NDVI, and surface temperature (Pcold, Phot) to model the robustness of energy fluxes and Evapotranspiration-ETa over the basin. Results reasonably reflected ETa which returned low RMSE (0.6 mm d−1), MAE (0.48 mm d−1) compared to in-situ recordings, indicating the competence of SEBAL to predict vapor fluxes in this region. The adjustment unveiled the estimates of the land-use contribution to evapotranspiration with an average ranging from 3 to 4.69 mm d−1, reaching a maximum of 5.5 mm d−1. Furthermore, findings showed a high striking energy dissipation (LE/Rn) across grasslands and wetlands. The vegetated surfaces with a great evaporative fraction were associated with the highest LE/Rn (70–90%), and water bodies varying between 20% and 60%, while the desert ecosystem dissipated the least energy with a low evaporative fraction. Still, besides high portrayed evaporation in water, grasslands and wetlands varied interchangeably in accounting for the highest ETa followed by cropland. Finally, a substantial nexus between available energy (Rn-G) and ETa informed the available energy, influenced by NDVI to be the primary driver of these oases’ transpiration. This study provides essentials of near-surface energy fluxes and the likelihood of ETa with considerable baseline inferences for Yanqi that may be beneficial for long-term investigations that will attend in agrometeorological services and sustainable management of water resources in semi-arid regions.

Delineation and mapping of palaeochannels using remote sensing, geophysical, and sedimentological techniques: A comprehensive approach

ABSTRACT Palaeochannels are remnants of rivers or stream channels that flowed in the past and have been currently filled or buried by younger fluviatile sediments. There are a number of techniques for detecting palaeochannels with each of them having its own intrinsic advantages and disadvantages. In the present study, apart from applying different approaches, the capabilities, and effectiveness of the approaches to locate and map palaeochannels are compared. Remote sensing-based approaches are comprised of high-resolution satellite datasets and a LiDAR-derived elevation map for displaying extensive drainage networks of surface feature channels hypothesized to be palaeochannels, palaeorivers, or palaeovalleys. Ground-penetrating radar (GPR) transects along with concurrent visual observations are ground-truthing field approaches and has proved to be one of the most appropriate and accurate methods for locating palaeochannels due to their capability to identify subsurface sedimentary stratigraphy innate to palaeochannels. Nevertheless, visual field observations for ground-truthing of prospective palaeochnnels either exhibiting only subtle alterations in desiccation or vegetation are considered a potent field approach. The present review work lays a strong emphasis on delineation and mapping of palaeochannels using remote sensing, geophysical, and sedimentological techniques to meet the sustainable groundwater development goals.

Seasonal Variability of Temperature Fronts in the Black Sea from Satellite Data

The structure and seasonal variability of temperature frontal zones and fronts in the Black Sea are studied on the basis of a modern high-resolution satellite data set. The distributions of the total temperature gradient allow us to distinguish the following frontal zones: the western frontal zone; the Turkish coastal frontal zone; the Anatolian upwelling frontal zone; the southeastern coastal frontal zone; northeastern, Crimea, and Kerch-Feodosia frontal zones; and the frontal zone of the deep part of the Black Sea. Large-scale fronts are identified within most frontal zones based on an analysis of distributions of meridional and zonal temperature gradients. It is shown that the combined effect of large-scale processes (seasonal warming and cooling of waters and their advection by the Rim Current) and regional factors (river discharge, coastline configuration, shelf width, development of upwelling, and formation of coastal eddies) produces significant spatiotemporal variability of the temperature field and, hence, frontal zones and fronts. It is ascertained that most fronts exist for several months. In the northern part of the sea, the fronts intensify mainly in winter due to an increase in the contrasts between warm Rim Current waters and colder coastal waters. The frontal zones near the southern coast of the sea intensify mainly in late spring–summer during the spring flood, the development of Anatolian upwelling, and active warming of waters in the eastern part of the sea.

Application of Fengyun-4 Satellite to Flood Disaster Monitoring through a Rapid Multi-Temporal Synthesis Approach

Fengyun-4A (FY-4A) belongs to the second generation of geostationary meteorological satellite series in China. Its observations with high frequency and resolution provide a better data basis for monitoring of extreme weather such as sudden flood disasters. In this study, the flood disasters occurred in Bangladesh, India, and some other areas of South Asia in August 2018 were investigated by using a rapid multi-temporal synthesis approach for the first time for removal of thick clouds in FY-4A images. The maximum between-class variance algorithm (OTSU; developed by Otsu in 2007) and linear spectral unmixing methods are used to extract the water area of flood disasters. The accuracy verification shows that the water area of flood disasters extracted from FY-4A is highly correlated with that from the high-resolution satellite datasets Gaofen-1 (GF-1) and Sentinel-1A, with the square correlation coefficient R2 reaching 0.9966. The average extraction accuracy of FY-4A is over 90%. With the rapid multi-temporal synthesis approach used in flood disaster monitoring with FY-4A satellite data, advantages of the wide coverage, fast acquisition, and strong timeliness with geostationary meteorological satellites are effectively combined. Through the synthesis of multi-temporal images of the flood water body, the influence of clouds is effectively eliminated, which is of great significance for the real-time flood monitoring. This also provides an important service guarantee for the disaster prevention and reduction as well as economic and social development in China and the Asia–Pacific region.

Assessing Machine Learning Based Supervised Classifiers For Built-Up Impervious Surface Area Extraction From Sentinel-2 Images

Urban development concept requires a way forward towards smarter and easier urban development. But due to unintended urbanization, which contributes to increased impervious surface areas with several environmental issues. There was a requirement to assess and map the urban built-up impervious areas for solving various socio-environmental issues. This work attempts to evaluate and implement machine learning (ML) based supervised classifiers to semi-automate the process for extraction, quantification and mapping of urban built-up impervious areas. The work emphasised on the usage of freely available open-source high-resolution satellite datasets acquired from Sentinel-2 mission. Three different machine learning-based supervised classification techniques were evaluated for a better understanding of feature extraction methods along with suitable classifier for classification of urban impervious areas. It is a well-known phenomenon that an increase in the impervious surface contributes to declining of green cover. Also, a zonal analysis of extracted built-up impervious surfaces was conducted to understand the spatial configuration of the pilot study area. This zonal assessment of urban built-up impervious surfaces can be used as a worthy tool for better sustainable smart cities development. These can serve as a valuable resource for restoring the required urban green cover for better sustainable urban development.

Mapping evapotranspiration variability over a complex oasis-desert ecosystem based on automated calibration of Landsat 7 ETM+ data in SEBAL

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature Ts and Normalized Difference Vegetation Index NDVI ( ). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: – desert ecosystem ETa R2 = 0.94; oasis ecosystem ETa R2 = 0.98 (P < 0.05). The improvement uncovered the exact land use contributions to ETa variability, with average estimates ranging from 1.24 mm to 6.98 mm . Additionally, instantaneous ET to NDVI (ETins-NDVI) ratio indicated that desert and oasis consumptive water use vary significantly with time of the season. This study indicates the possibility of continuous daily ET monitoring with considerable implications for improving water resources decision support over complex data-scarce drylands.

Enhancement of Archaeological Proxies at Non-Homogenous Environments in Remotely Sensed Imagery

Optical remote sensing has been widely used for the identification of archaeological proxies. Such proxies, known as crop or soil marks, can be detected in multispectral images due to their spectral signatures and the distinct contrast that they provide in relation to the surrounding area. The current availability of high-resolution satellite datasets has enabled researchers to provide new methodologies and algorithms that can further enhance archaeological proxies supporting thus image-interpretation. However, a critical point that remains unsolved is the detection of crop and soil marks in non-homogenous environments. In these areas, interpretation is problematic even after the application of sophisticated image enhancement analysis techniques due to the mixed landscape and spectral confusion produced from the high-resolution datasets. To overcome this problem, we propose an image-based methodology in which the vegetation is suppressed following the “forced invariance” method and then we apply a linear orthogonal transformation to the suppressed spectral bands. The new Red–Green–Blue (RGB) image corresponds to a new three-band spectral space where the three axes are linked with the crop mark, vegetation, and soil components. The study evaluates the proposed approach in the archaeological site of “Nea Paphos” in Cyprus using a WorldView-2 multispectral image aiming to overcome the limitations of the mixed environments.

OBJECT ORIENTED CLASSIFICATION AND FEATURE EXTRACTION FOR PARTS OF EAST DELHI USING HYBRID APPROACH

Abstract. Rapid urbanization of Indian cities requires a focused attention with respect to preparation of Master Plans of cities. Urban land use/land cover from very high resolution satellite data sets is an important input for the preparation of the master plans of the cities along with extraction of transportation network, infrastructure details etc. Conventional classifiers, which are pixel based do not yield reasonably accurate urban land use/land cover classification of very high resolution satellite data (usually merged images of Panchromatic &amp;amp; Multispectral). Object Based Image Classification techniques are being used to generate urban land use maps with ease which is GIS compatible while using very high resolution satellite data sets. In this study, Object Based Image Analysis (OBIA) has been used to create broad level urban Land Use / Land Cover (LU/LC) map using high resolution ResourceSat-2 LISS-4 and Cartosat-1 pan-sharpened image on the study area covering parts of East Delhi City. Spectral indices, geometric parameters and statistical textural methods were used to create algorithms and rule sets for feature classification. A LU/LC map of the study area comprising of 4 major LU/LC classes with its main focus on separation of barren areas from built up areas has been attempted. The overall accuracy of the result obtained is estimated to be approximately 70%.