Low Resolution Satellite Research Articles

Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation

The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout the Russian Federation and in its individual regions. An approach to the use of remote sensing and geoinformatics methods for monitoring the impact of wildfires on the state of various types of vegetation cover without involving ground truth data is proposed. Potential for using a normalized burn ratio index to estimate the degree of vegetation damage and recovery process using low-resolution satellite data is demonstrated. It was established that the total area of burned vegatation over the country ranged from 68.40 to 234.46 thousand km2 per year for the period from 2002 till 2017. Seasonal peaks of fire activity, patterns of the spatial distribution of fire sources, and the main types of burned vegetation were identified for each of the eight federal districts of Russia. The accuracy of burned areas detection was estimated as 75–81% depending on the information product used according to the low (500 m) and average (30 m) spatial resolution data.

Unmanned aerial system and satellite-based high resolution imagery for high-throughput phenotyping in dry bean

Dry bean breeding programs are crucial to improve the productivity and resistance to biotic and abiotic stress. Phenotyping is a key process in breeding that refers to crop trait evaluation. In recent years, high-throughput plant phenotyping methods are being developed to increase the accuracy and efficiency for crop trait evaluations. In this study, aerial imagery at different resolutions were evaluated to phenotype crop performance and phenological traits using genotypes from two breeding panels, Durango Diversity Panel (DDP) and Andean Diversity Panel (ADP). The unmanned aerial system (UAS) based multispectral and thermal data were collected for two seasons at multiple time points (about 50, 60 and 75 days after planting/DAP in 2015; about 60 and 75 DAP in 2017). Four image-based features were extracted from multispectral images. Among different features, normalized difference vegetation index (NDVI) data were found to be consistently highly correlated with performance traits (above ground biomass, seed yield), especially during imaging at about 60–75 DAP (early pod development). Overall, correlations were higher using NDVI in ADP than DDP with biomass (r = −0.67 to −0.91 in ADP; r = −0.55 to −0.72 in DDP) and seed yield (r = 0.51 to 0.73 in ADP; r = 0.42 to 0.58 in DDP) at about 60 and 75 DAP. For thermal data, a temperature data normalization (utilizing common breeding plots in multiple thermal images) was implemented and the MEAN plot temperatures generally correlated significantly with biomass (r = 0.28–0.88). Finally, lower resolution satellite images (0.05–5 m/pixel) using UAS data was simulated and image resolution beyond 50 cm was found to reduce the relationship between image features (NDVI) and performance variables (biomass, seed yield). Four different high resolution satellite images: Pleiades-1A (0.5 m), SPOT 6 (1.5 m), Planet Scope (3.0 m), and Rapid Eye (5.0 m) were acquired to validate the findings from the UAS data. The results indicated sub-meter resolution satellite multispectral imagery showed promising application in field phenotyping, especially when the genotypic responses to stress is prominent. The correlation between NDVI extracted from Pleiades-1A images with seed yield (r = 0.52) and biomass (r = −0.55) were stronger in ADP; where the strength in relationship reduced with decreasing satellite image resolution. In future, we anticipate higher spatial and temporal resolution data achieved with low-orbiting satellites will increase applications for high-throughput crop phenotyping.

CROP INVENTORY OF ORCHARD CROPS IN INDIA USING REMOTELY SENSED DATA

Abstract. The use of satellite remote sensing (RS) technologies for purpose of crop discrimination, mapping, area estimation, condition and yield assessment has been proved to be effective and efficient in terms of time and cost, having better consistency implemented with scientific approaches. However, application of satellite RS technology for horticultural crops in India has certain challenges due to scattered and small field sizes, comparatively short duration such as vegetable crops and mixed cropping. Hence the study was taken for developing research methodology for area assessment of three major fruit crops such as Banana, Mango and Citrus over 20 districts in four states viz. Gujarat, Madhya Pradesh, Uttar Pradesh and Bihar. Appropriate bio-window for analysing different crop types was selected and mapping of crops were done using pixel based hybrid classification i.e. un-supervised ISODATA clustering plus supervised MXL classification as well as object based classification of high resolution remote sensing data (Resourcesat LISS III and/or LISS IV, Cartosat – 1 PAN) followed by their accuracy assessment and their comparison with departmental reported statistics. Overall, the classification accuracy was more than 80% for all the crops. Deviation from statistics were in the range of 3 to 38%. Higher deviations from statistics were mostly due to use of lower resolution satellite data or mixing of crops having similar spectral signatures e.g. mango and sapota in Navsari and Valsad districts of Gujarat. It was very difficult to discriminate the young orchards of 2–3 years from other field crops due to mixed / inter cropping practices. The maps were checked and certified by respective State Horticulture Departments and were archived in VEADS, SAC and BHUVAN, NRSC geoportals of ISRO. RISAT – 1 (microwave) data were explored for the estimation of banana orchards in order to detect banana plantation at early stage and under cloudy sky conditions. There is huge potential of application in this sector using advanced observations from hyperspectral, thermal infrared sensors and advanced radars or LIDAR’s on-board upcoming satellites.

Satellite monitoring of wildfire impacts on the conditions of various types of vegetation cover in the federal districts of the Russian Federation

Here we present the results of the analysis of multi-year satellite data series collected by Terra/Aqua (6 collections of MCD64A1 “Burned Area” monthly product) and Landsat satellites to assess the dynamics of vegetation affected by wildfires for the whole territory of Russia and its regions. An approach to use remote sensing and geoinformatic methods to monitor wildfire impacts on the conditions of various types of vegetation without involving in-situ data has been suggested. The capability to apply the normalized burned area index for the assessment of vegetation damage degree and recovering character using low-resolution satellite data has been demonstrated. It was established that between 2002 and 2017, 68.40 to 234.46 thousand km2 of vegetation cover had been burned out in Russia annually. Seasonal peaks of fire activity, laws of spatial distribution of fire sites, and main types of burned out vegetation for each of 8 Russian federal districts have been revealed. The accuracy of determination of burned out areas based on low-resolution (500 m) data using mid-resolution (30 m) data was 75–81% depending on the used data product.

Homisland-IO: Homogeneous Land Use/Land Cover over the Small Islands of the Indian Ocean

Many small islands are located in the southwestern Indian Ocean. These islands have their own environmental specificities and very fragmented landscapes. Generic land use products developed from low and medium resolution satellite images are not suitable for studying these small territories. This is why we have developed a land use/land cover product, called Homisland-IO, based on remote sensing processing on high spatial resolution satellite images acquired by SPOT 5 satellite between December 2012 and July 2014. This product has been produced using an object-based classification process. The overall accuracy of the product is 86%. Homisland-IO is freely accessible through a web portal and is thus available for future use.

A Systematic Review of Coastal Vulnerability Assessment Studies along Andhra Pradesh, India: A Critical Evaluation of Data Gathering, Risk Levels and Mitigation Strategies

The establishment and alteration of any coastal feature is largely dependent upon complex hydrological and geomorphologic processes. Therefore, understanding hazard factors and threat risk level is crucial for mitigating risk in coastal zones. This study examines coastal vulnerability factors and their influence along the Coastal Andhra Pradesh (CAP) region in India. CAP has been exposed to frequent hydrological and meteorological hazards due to variations in the geographical, geological, and bathymetric characteristics. Despite substantial vulnerabilities, the risk to the coastline of Andhra Pradesh has not been rigorously evaluated. The current research systematically reviews the drivers and effects of hazards and vulnerabilities in CAP. Findings indicate that urban cities have a considerably higher risk of cyclones and floods due to their locations on the Bay of Bengal tectonic plate, the topology of this coastal region, and higher population density. The study revealed that the Coastal Vulnerability Index (CVI) data along CAP is mostly gathered using low-resolution satellite data and/or field observation surveys. The study further revealed that there are very few existing mitigation strategies developed or discussed within the obtained results. However, more accurate data gathering techniques for coastal vulnerability factors are available such as Unmanned Aerial Vehicles (UAVs): Air-borne and LiDAR sensors, which provide very high resolution data and low-cost accessibility to physically inaccessible places, making them suitable for vulnerability data collection in coastal locations. These findings are useful for stakeholders seeking to reduce or ameliorate the impact of coastal disasters and their impacts on the CAP economy, environment, and population. The study further helps to reduce the existing shortcomings in the assessment techniques used previously.

基于遥感卫星影像的森林火险监测

This paper proposes a combined mode of using low-resolution meteorological satellites with wide coverage area and short revisit period to find the fire point, and then using high-resolution micro-satellite constellation for fixed-point tracking and shooting. It can not only realize the rapid identification of the fire risk, but also can track and predict the fire area and the fire spread trend in detail. These data can also be linked to the work deployment of firefighters. They can quickly obtain data when a fire occurs, so as to better respond to the current fire type, and Quantitative assessment of the damaged area and losses can be made after the disaster.

Image Enhancement of Low Resolution Satellite Image based on Texture and Morphological Features

Image Enhancement of Low Resolution Satellite Image based on Texture and Morphological Features

Enduring Lagrangian coherence of a Loop Current ring assessed using independent observations

Ocean flows are routinely inferred from low-resolution satellite altimetry measurements of sea surface height assuming a geostrophic balance. Recent nonlinear dynamical systems techniques have revealed that surface currents derived from altimetry can support mesoscale eddies with material boundaries that do not filament for many months, thereby representing effective transport mechanisms. However, the long-range Lagrangian coherence assessed for mesoscale eddy boundaries detected from altimetry is constrained by the impossibility of current altimeters to resolve ageostrophic submesoscale motions. These may act to prevent Lagrangian coherence from manifesting in the rigorous form described by the nonlinear dynamical systems theories. Here we use a combination of satellite ocean color and surface drifter trajectory data, rarely available simultaneously over an extended period of time, to provide observational evidence for the enduring Lagrangian coherence of a Loop Current ring detected from altimetry. We also seek indications of this behavior in the flow produced by a data-assimilative system which demonstrated ability to reproduce observed relative dispersion statistics down into the marginally submesoscale range. However, the simulated flow, total surface and subsurface or subsampled emulating altimetry, is not found to support the long-lasting Lagrangian coherence that characterizes the observed ring. This highlights the importance of the Lagrangian metrics produced by the nonlinear dynamical systems tools employed here in assessing model performance.

SPECTRAL ANALYSIS OF THE HIMAWARI-8 DATA FOR HOTSPOT DETECTION FROM LAND/FOREST FIRES IN SUMATRA

Himawari-8 is the last generation of the low spatial resolution satellite imagery that has capability to detect the thermal variation on the earth of every 10 minute. This must be very potential to be used for detecting land/forest fire. This paper has explored the spectral prospective of the Himawari-8 for detecting land/forest fire hotspot. The main objective for this study is to identify the potential use of Himawari-8 for detecting of land forest fire hotspot. The study area was performed in Ogan Komering Ilir, South of Sumatra, which on 2015 occur great forest/land fire event. The main process included in this study are image projection, training sample collection and spectral statistical analysis measured by calculate statistic, they are average values, standard deviation values from reflectance visible band value and brightness temperature value, beside that validation of data obtained from medium resolution data of Landsat 8 with the similar acquisition time. The study found that the Himawari-8 has good capacity to identify land/forest fire hotspot as expressed for high accuracy assessment using band 3 and band 7.

A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data

Deriving high quality precipitation estimates at high spatial resolution is of prime importance for many hydrological, meteorological, and environmental investigations. Rain gauge observations and satellite-derived precipitation data are two main sources of precipitation estimates. Gauge observations are accurate and reliable, but are heavily point-based and sparse in areas of rugged or complex terrains. Satellite-derived precipitation products can cover large areas, but they are generally characterized by inherent bias. To optimize the use of both datasets, we propose in this paper, a downscaling-integration framework to generate high quality monthly precipitation datasets at 1 km spatial resolution by merging rain gauge observations and TRMM 3B43 products. Firstly, an area-to-point kriging (ATPK) approach is used to downscale the original TRMM product to 1 km, so as to ensure a fair comparison with rain gauge data. Then, the downscaled TRMM precipitation datasets are integrated with the gauge observations using geographically weighted regression kriging (GWRK). The geographical factors (i.e. longitude, latitude and elevation) are also used as auxiliary variables in the GWRK model. Applying this approach to an experiment conducted at the middle and lower reaches of the Yangtze River in China from 2001 to 2014 shows that: (1) the downscaled monthly TRMM precipitation data by ATPK are more accurate than the original TRMM estimates; (2) the GWRK model employing the downscaled TRMM precipitation data and geographical factors provides better monthly precipitation estimates than the conventional ordinary kriging (OK) interpolation and the commonly used merging methods (i.e. geographical difference analysis, GDA and kriging with external drift, KED); (3) the GWRK method reduces the influence of the inaccuracy (bias) of satellite-derived precipitation data on the precipitation estimates compared to GDA. The approach presented in this study has provided an efficient alternative for solving the scale mismatch problem between point-based gauge data and low resolution satellite data, and producing improved precipitation data at high spatial resolution.

LAKE ICE DETECTION IN LOW-RESOLUTION OPTICAL SATELLITE IMAGES

Abstract. Monitoring and analyzing the (decreasing) trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250 m–1000 m) satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM) lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen) semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal). Only the cloud-free (clean) pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM). We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters.

Urban Road Network Extraction from Remote Sensing Images Using an Improved F* Algorithm

A city mapping is essential to various applications such as planning, transport management, vehicle navigation, intervention in natural disasters, etc…. For convenience and efficiency, such applications are integrated in a Geographical Information System (GIS) (Bendouda and Berrached, in Etude et realisation d’UREGIS un SIG pour la gestion du reseau routier urbain, Magister Thesis, University of Sciences and Technology of Oran Algeria 2009). GIS Map needs real time automatic updating and revisions of the road network databases. However, due to the extreme complexity of the urban environment, there are currently many methods involving the extraction of roads by means of automatic or semi-automatic approaches in rural and sub-urban areas; but in urban environment the majority of these methods failed, due to the complexity of this environment and the complex appearance of the road in the remotely sensed image. In this paper, we introduce a new approach to extract road network in urban area from low resolution satellite images. The proposed method is a modified version of the dynamic programming method for semi-automatic extraction of road network, based on the F* algorithm. The preliminary step is the seeding of points belonging to roads. F* detects the segments that may belong to a road by optimizing certain criteria. Given the complexity of urban areas and the existence of different road categories, we propose an improved version of the classical algorithm F* called PR-F*(Parallel Research-F*). It detects the road segments automatically in many directions. The obtained results are evaluated in terms of quality with respect to completeness and correctness.

도시 내 열환경 분석에서 무인항공기의 활용가능성†

Urban heat islands occur due to increases in the extent of artificial surfaces such as concrete, asphalt and high-rise buildings. In this regard, research into the use of satellite thermal infrared images for thermal environment analysis of urban areas is being carried out. However, such analysis of the characteristics of individual land cover with low-resolution satellite images suffers from limitations because land cover patterns in urban areas are complicated. Recently, UAV has been widely used, which can compensate for this limitation as it is able to acquire high-resolution images. In this paper, the accuracy of UAV infrared images is verified and the applicability of UAV in urban thermal environment analysis is examined by comparing the results with land surface temperatures from Landsat 8 thermal images. The results show a high positive correlation of temperature values at 0.95, and no statistically significant difference between the two groups. Comparisons of land surface temperature according to land cover showed that the largest difference observed was 4.63℃ in the Used area, and UAV images with small cell units reflected various surface temperatures. Furthermore, it was possible to analyze the surface temperatures of various green spaces such as wetlands and street tree areas, which can lower surface temperatures in urban areas, with street tree shadows reducing surface temperatures by about 4-6℃. UAV can easily and rapidly measure the surface temperature of urban areas and is able to analyze various types of green spaces. Thus, this is an effective tool for thermal environment analysis in urban areas to aid in the design or management of urban green spaces, as it can allow for land cover and the effects of the various green spaces.

AUTOMATIC TRANSMISSION TOWERS DETECTION IN LOW-RESOLUTION SATELLITE IMAGES

In this paper, we have implemented an automatic system for transmission towers detection in low-resolution satellite images. The satellite images are captured from Google Earth® software. The area of interest is located in Selangor-Malaysia. The satellite image went through several algorithms, including preprocessing the image in order to make it suitable for the current application and then threshold the image to detect the complete area of the transmission towers. Subsequently, this image enhanced and binarized again in order to eliminate the unnecessary objects that might be found in the transmission tower area. Finally, we tracked the boundaries for each transmission tower and label it. The proposed algorithm is tested over 100 low-resolution satellite image. Our algorithm leads to a promising outcome.

Ship target tracking based on a low-resolution optical satellite in geostationary orbit

ABSTRACTBased on the low-resolution geostationary optical satellite for maritime surveillance, a complete processing algorithm including ship detection and ship tracking is proposed. Potentially ships are first detected by a constant false alarm rate processing and a morphological filter from the area of water. Image coordinates of ships are then corrected and transformed by using rational polynomial coefficients and high-resolution shoreline database. In the ship-tracking procedure, a convenient motion model based on geographical coordinates is established, and the multiple hypothesis tracking method is used to track ships for further removing false targets and getting motion states of targets ultimately. Our algorithm has been tested using GaoFen-4 (GF-4) satellite image sequences and the automatic identification system messages of ships. The results of the experiment show that the proposed algorithm can effectively detect and track ships in complex scenes and estimate ships’ motion information accurately with the help of a low-resolution geostationary optical satellite.

Improved environmental monitoring of surface geothermal features through comparisons of thermal infrared, satellite remote sensing and terrestrial calorimetry

Assessing changes in surface heat-loss at geothermally active areas provides insight into the geothermal reservoirs at depth. Surface temperatures and heat output of steam-heated ground can be inferred through processing of thermal infrared images, as well as by direct measurements using terrestrial calorimetry. In February 2014, a high resolution aerial thermal infrared (TIR) survey and a terrestrial heat-loss survey (excluding fumarole discharge) were carried out over Karapiti (ca. 0.35 Km2 in the Wairakei-Tauhara Geothermal System) in the Taupo Volcanic Zone, New Zealand. Estimates of heat-loss are compared to those calculated from TIR data collected by the Landsat-8 satellite of the area. This paper discusses the processing techniques for the datasets and compares inferred surface heat losses. The aerial TIR and terrestrial measurements are in reasonable agreement with calculated surface heat-loss values for Karapiti range from 41 MW (theoretical radiation heat loss using aerial TIR) to 58 MW (total heat loss based on an empirical correlation with boiling-point depths). Satellite images show a large variation between day-time and night-time TIR assessments. The assessment of nocturnal (non-solar), radiation heat-loss is an order of magnitude lower than the total heat-loss determined from the other techniques and approximately 1/3 of the inferred radiated component calculated from the aerial TIR data. This is likely to be a consequence of diffuse, advecting-steam heat-loss, combined with an image resolution issue: the pixel resolution of the satellite image (30 × 30 m) is much larger than the typical size of the active geothermal surface manifestations (<10 m2). Because the radiated heat-loss is a non-linear function of land surface temperature, the in-pixel averaging under-estimates the radiated heat-loss. These and other problems currently restrict the usefulness of repeat low-resolution satellite imagery for monitoring of surface heat-loss changes in steam-heated ground.

Lake Ice Detection in Low-Resolution Optical Satellite Images

Abstract. Monitoring and analyzing the (decreasing) trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250 m–1000 m) satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM) lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen) semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal). Only the cloud-free (clean) pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM). We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters.

Multi-Frame Super-Resolution of Gaofen-4 Remote Sensing Images.

Gaofen-4 is China’s first geosynchronous orbit high-definition optical imaging satellite with extremely high temporal resolution. The features of staring imaging and high temporal resolution enable the super-resolution of multiple images of the same scene. In this paper, we propose a super-resolution (SR) technique to reconstruct a higher-resolution image from multiple low-resolution (LR) satellite images. The method first performs image registration in both the spatial and range domains. Then the point spread function (PSF) of LR images is parameterized by a Gaussian function and estimated by a blind deconvolution algorithm based on the maximum a posteriori (MAP). Finally, the high-resolution (HR) image is reconstructed by a MAP-based SR algorithm. The MAP cost function includes a data fidelity term and a regularized term. The data fidelity term is in the L2 norm, and the regularized term employs the Huber-Markov prior which can reduce the noise and artifacts while preserving the image edges. Experiments with real Gaofen-4 images show that the reconstructed images are sharper and contain more details than Google Earth ones.

Joint image registration and point spread function estimation for the super-resolution of satellite images

Image registration and point spread function (PSF) estimation are both key steps in image super-resolution (SR). Traditionally, these two steps are treated independently, which is adequate for natural images. However, for satellite images, which commonly suffer from focal plane distortion and unrecorded spacecraft jitter, it is always difficult to achieve satisfactory image registration or PSF estimation. Consequently, the errors brought by these two processes significantly affect each other and degrade the quality of the subsequent high-resolution (HR) reconstruction. In this paper, a novel joint image registration and PSF estimation method is proposed to produce HR images from a set of degraded low-resolution (LR) satellite images. The joint SR approach is formulated as a convex optimization problem which minimizes the combination of these two parts. It is aimed at achieving PSF estimation and registration simultaneously and progressively, to handle the error in different levels. In addition, the proposed method adopts a more generic observation model, including both geometric motion and radiation difference, which makes the model more universal. Moreover, an iterative scheme based on alternating minimization (AM) is developed to solve the presented cost function via simultaneous low-rank and total variation (LRTV) regularizations. The experimental results confirm the effectiveness of the proposed method on both simulated data and real satellite images.