Multi-resolution Satellite Imagery Research Articles

Correction to: Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping

Correction to: Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping

Fusion of Dense Airborne LiDAR and Multispectral Sentinel-2 and Pleiades Satellite Imagery for Mapping Riparian Forest Species Biodiversity at Tree Level.

Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity, and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion of Airborne LiDAR data and multispectral multi-source and multi-resolution satellite imagery: Sentinel-2 and Pleiades at tree level. The idea is to assess the contribution of each data source in the tree species classification at the considered level. The data fusion was processed at the feature level and the decision level. At the feature level, LiDAR 2D attributes were derived and combined with multispectral imagery vegetation indices. At the decision level, LiDAR data were used for 3D tree crown delimitation, providing unique trees or groups of trees. The segmented tree crowns were used as a support for an object-based species classification at tree level. Data augmentation techniques were used to improve the training process, and classification was carried out with a random forest classifier. The workflow was entirely automated using a Python script, which allowed the assessment of four different fusion configurations. The best results were obtained by the fusion of Sentinel-2 time series and LiDAR data with a kappa of 0.66, thanks to red edge-based indices that better discriminate vegetation species and the temporal resolution of Sentinel-2 images that allows monitoring the phenological stages, helping to discriminate the species.

Remote Sensing and Nonlinear Auto-regressive Neural Network (NARNET) Based Surface Water Chemical Quality Study: A Spatio-Temporal Hybrid Novel Technique (STHNT).

In recent days, the quality of water in inland water bodies has been threatened by various natural and anthropogenic activities. Henceforth, the continuous monitoring of water quality is mandatory to control the pollution level in surface water bodies. In this work, remote sensing technology integrated with an Artificial Intelligence (AI) algorithm, a new technique called Spatio-Temporal Hybrid Novel Technique (STHNT), was used to predict, and monitor the chemical water quality pollution level through the Water Quality Index (WQI). The Two Bands Regression Empirical (TBRE) water quality model has been used to retrieve water quality parameters from multi-resolution satellite imagery (Sentinel-2 MSI). The Nonlinear Auto-regressive Neural Network (NARNET), which is an Artificial Neural Network (ANN), was set up to predict the water quality index. Based on the model performed on the remote sensing water quality data, it is inferred that NARNET (Coefficient of determination-R2:0.9911, Root Mean Square Error-RMSE:1.693 and Sum of Squares of Error-SSE:14.33) provides significant results in predicting WQI. Therefore, the combined remote sensing technology with artificial intelligence plays a pivotal role in water resource management, which helps in reducing the pollution level in surface water bodies.

Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping

Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping

Downscaling MODIS spectral bands using deep learning

ABSTRACT MODIS sensors are widely used in a broad range of environmental studies, many of which involve joint analysis of multiple MODIS spectral bands acquired at disparate spatial resolutions. To extract land surface information from multi-resolution MODIS spectral bands, existing studies often downscale lower resolution (LR) bands to match the higher resolution (HR) bands based on simple interpolation or more advanced statistical modeling. Statistical downscaling methods rely on the functional relationship between the LR spectral bands and HR spatial information, which may vary across different land surface types, making statistical downscaling methods less robust. In this paper, we propose an alternative approach based on deep learning to downscale 500 m and 1000 m spectral bands of MODIS to 250 m without additional spatial information. We employ a superresolution architecture based on an encoder decoder network. This deep learning-based method uses a custom loss function and a self-attention layer to preserve local and global spatial relationships of the predictions. We compare our approach with a statistical method specifically developed for downscaling MODIS spectral bands, an interpolation method widely used for downscaling multi-resolution spectral bands, and a deep learning superresolution architecture previously used for downscaling satellite imagery. Results show that our deep learning method outperforms on almost all spectral bands both quantitatively and qualitatively. In particular, our deep learning-based method performs very well on the thermal bands due to the larger scale difference between the input and target resolution. This study demonstrates that our proposed deep learning-based downscaling method can maintain the spatial and spectral fidelity of satellite images and contribute to the integration and enhancement of multi-resolution satellite imagery.

A development of the Enhanced Built-up and Bareness Index (EBBI) based on combination of multi-resolution Landsat 8 and Sentinel 2 MSI images

Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.

Multiresolution Satellite-Derived Bathymetry in Shallow Coral Reefs: Improving Linear Algorithms with Geographical Analysis

da Silveira, C.B.L.; Strenzel, G.M.R.; Maida, M.; Araujo, T.C.M., and Ferreira, B.P., 2020. Multiresolution satellite-derived bathymetry in shallow coral reefs: Improving linear algorithms with geographical analysis. Journal of Coastal Research, 36(6), 1247–1265. Coconut Creek (Florida), ISSN 0749-0208.Bathymetric maps are one of the first steps for most hydrological and ecological studies of the seascape, as depth is a determinant factor in the distribution of organisms, patterns of wave exposure, and coastal circulation. The coral reefs of Tamandare-Brazil, located at Costa dos Corais marine protected area (MPA), encompass a mosaic of interconnected habitats of complex geomorphology, including coral reefs, algal, and seagrass beds. These coastal habitats are subjected to chronic impacts such as sedimentation, reef erosion, and increasing human use, leading to habitat loss. Despite their social and ecological importance and the conservation measures in place, bathymetric and habitat maps of this coast are lacking. Indeed, in situ surveys are not always feasible in shallow coral reef areas. The present study offers a detailed bathymetric mapping of the area using multiresolution satellite imagery. One Landsat-8 (December 2016) and one WorldView-03 (February 2017) imagery were used to derive medium (30-m) and high-resolution (2-m) bathymetry of the study area. Single-beam echo sounder surveys were performed to obtain field data to calibrate the depth-retrieving algorithms: linear model, ratio band model, principal component analyses of the transformed bands, and geographically weighted regressions (GWR). For both resolution datasets, results showed that the algorithms' RMS were significantly improved by the GWR technique (RMS > 0.9) because of adaptability to the bottom heterogeneity found in complex areas such as coral reefs. Specific geomorphological reef zones were recognizable in the resulting bathymetric maps, such as intrareef lagoon, reef crest, fore-reef, and reef flat. This research concluded that affordable methods such as single-beam data coupled with satellite imagery through GWR can provide the required inputs for mapping shallow areas with complex relief. Such results may be further used to habitat mapping, necessary to inform the multiple-use zonation foreseen in MPA management plans.

Decadal biomass and area changes in a multi-species meadow in Singapore: application of multi-resolution satellite imagery

AbstractDue to the dearth of information regarding current and changing health of seagrass habitat in the Indo-Pacific region, prior research into global trends of seagrass habitat health has included little data from this region, even though it contains the highest abundance and species diversity of seagrass globally. This study evaluates the suitability of four satellite sensors [Worldview-2 (WV2), Advanced Land Imager (ALI), Enhanced Thematic Mapper+ (ETM+), Operational Land Imager (OLI)] for determining trends in seagrass habitat extent over the past decade in Singapore’s largest seagrass meadow, and thus contributes incrementally to the data available for regional or global analyses of seagrass habitat health. Using all four sensors, we find that seagrass bed extent at Pulau Semakau, Singapore, declined 37% from 2001 to 2015 at an average rate of 3.9% year−1. Using very high spatial resolution satellite images, we calculate that, although bed extent decreased 18% from April 2011 to June 2013, median meadow biomass increased, indicating that complex meadow dynamics may be mediating seagrass response to anthropogenic and environmental pressures. From a technological perspective, we find that, despite their lower spatial resolution, freely available satellite images can be used to measure the extent of a narrow, multi-species seagrass bed and to determine decadal trends reliably.

Forest cover mapping in post-Soviet Central Asia using multi-resolution remote sensing imagery

Despite rapid advances and large-scale initiatives in forest mapping, reliable cross-border information about the status of forest resources in Central Asian countries is lacking. We produced consistent Central Asia forest cover (CAFC) maps based on a cost-efficient approach using multi-resolution satellite imagery from Landsat and MODIS during 2009–2011. The spectral-temporal metrics derived from 2009–2011 Landsat imagery (overall accuracy of 0.83) was used to predict sub-pixel forest cover on the MODIS scale for 2010. Accuracy assessment confirmed the validity of MODIS-based forest cover map with a normalized root-mean-square error of 0.63. A general paucity of forest resources in post-Soviet Central Asia was indicated, with 1.24% of the region covered by forest. In comparison to the CAFC map, a regional map derived from MODIS Vegetation Continuous Fields tended to underestimate forest cover, while the Global Forest Change product matched well. The Global Forest Resources Assessments, based on individual country reports, overestimated forest cover by 1.5 to 147 times, particularly in the more arid countries of Turkmenistan and Uzbekistan. Multi-resolution imagery contributes to regionalized assessment of forest cover in the world’s drylands while developed CAFC maps (available at https://data.zef.de/) aim to facilitate decisions on biodiversity conservation and reforestation programs in Central Asia.

Combined adjustment of multi-resolution satellite imagery for improved geo-positioning accuracy

Due to the widespread availability of satellite imagery nowadays, it is common for regions to be covered by satellite imagery from multiple sources with multiple resolutions. This paper presents a combined adjustment approach to integrate multi-source multi-resolution satellite imagery for improved geo-positioning accuracy without the use of ground control points (GCPs). Instead of using all the rational polynomial coefficients (RPCs) of images for processing, only those dominating the geo-positioning accuracy are used in the combined adjustment. They, together with tie points identified in the images, are used as observations in the adjustment model. Proper weights are determined for each observation, and ridge parameters are determined for better convergence of the adjustment solution. The outputs from the combined adjustment are the improved dominating RPCs of images, from which improved geo-positioning accuracy can be obtained. Experiments using ZY-3, SPOT-7 and Pleiades-1 imagery in Hong Kong, and Cartosat-1 and Worldview-1 imagery in Catalonia, Spain demonstrate that the proposed method is able to effectively improve the geo-positioning accuracy of satellite images. The combined adjustment approach offers an alternative method to improve geo-positioning accuracy of satellite images. The approach enables the integration of multi-source and multi-resolution satellite imagery for generating more precise and consistent 3D spatial information, which permits the comparative and synergistic use of multi-resolution satellite images from multiple sources.

Geometric Quality Assessment of Bundle Block Adjusted Mulit- Sensor Satellite Imageries

Abstract. The integration of multi-sensor earth observation data belonging to same area has become one of the most important input for resource mapping and management. Geometric error and fidelity between adjacent scenes affects large-area digital mosaic if the images/ scenes are processed independently. A block triangulation approach "Bundle Block Adjustment (BBA)" system has been developed at ADRIN for combined processing of multi-sensor, multi-resolution satellite imagery to achieve better geometric continuity. In this paper we present the evaluation results of BBA software along with performance assessment and operational use of products thus generated. The application evaluation deals with functional aspects of block-adjustment of satellite imagery consisting of data from multiple sources, i.e. AWiFs, LISS-3, LISS-4 and Cartosat-1 in various combinations as single block. It has provision for automatic generation of GCPs and tie-points using image metafile/ Rational Polynomial Coefficient's (RPC’s) and ortho/ merged/ mosaicked products generation. The study is carried out with datasets covering different terrain types (ranging from high mountainous area, moderately undulating terrain, coastal plain, agriculture fields, urban area and water-body) across Indian subcontinent with varying block sizes and spatial reference systems. Geometric accuracy assessment is carried out to figure out error propagation at scene based ortho/ merged products as well as block level. The experimental results confirm that pixel tagging, geometric fidelity and feature continuity across adjacent scenes as well as for multiple sensors reduced to a great extent, due to the high redundancy. The results demonstrate that it is one of the most affective geometric corrections for generating large area digital mosaic over High mountainous terrain using high resolution good swath satellite imagery, like Cartosat-1, with minimum human intervention.

Lineament extraction from multi-resolution satellite imagery: a pilot study on Wadi Bani Malik, Jeddah, Kingdom of Saudi Arabia

The study of structural lineaments is important for mineral exploration, geotectonic and geotechnical studies, and for the mitigation of geologic hazards. The present work deals with the extraction of lineaments from satellite imageries of different spatial resolutions as well as the analysis of these extracted lineaments. Wadi Bani Malik area located to the east of Jeddah city on the Red Sea coastal plain is chosen for such a study. Six types of digital satellite imagery data were used in the present study. These comprise satellite imagery of low spatial resolution (LSR) including Landsat MSS of 80-m resolution, Landsat TM of 30-m resolution, and Landsat TM of 25-m resolution; satellite imagery of moderate spatial resolution (MSR) including Landsat ETM+ panchromatic of 15-m resolution and SPOT panchromatic of 10-m resolution; and satellite imagery of high spatial resolution (HSR) including the Indian Remote Sensing satellite IRS data of 5-m resolution. As expected, the analysis of the extracted lineaments from different data sets shows that the imagery data of HSR of the Indian IRS data give the highest frequency of the extracted structural lineaments (N = 3,235), while the imagery data of LSR of the Landsat MSS data give the lowest frequency of the extracted lineaments (N = 89). The imagery data of MSR give moderate frequency (N = 1,643) in average. Due to the present study, it is recommended to use the imagery data of HSR and MSR for the extraction of structural lineaments for detailed and regional studies, respectively. The imagery data of LSR are not recommended for such studies due to the fact that most of the real structural lineaments framework cannot be extracted; accordingly, it is not useful in the analyses of lineaments for geological purposes.

A multi-resolution satellite imagery approach for large area mapping of ericaceous shrubs in Northern Quebec, Canada

Invasive ericaceous shrubs (e.g. Kalmia angustifolia, Rhododendron groenlandicum, Vaccinium spp.) may reduce the regeneration and early growth of black spruce (Picea mariana) seedlings, the most economically important boreal tree species in Quebec. Our study focused, therefore, on developing a method for mapping ericaceous shrubs from satellite images. The method integrates very high resolution satellite imagery (IKONOS) to guide classifiers applied to medium resolution satellite imagery (Landsat-TM). An object-oriented image classification approach was applied using Definiens eCognition software. An independent ground survey revealed 80% accuracy at the very high spatial resolution. We found that the partial use (70%) of classified polygons derived from the IKONOS images were an effective way to guide classification algorithms applied to the Landsat-TM imagery. The results of this latter classification (78.4% overall accuracy) were assessed by the remaining portion (30%) of unused very high resolution classified polygons. We further validated our method (65.5% overall accuracy) by assessing the correspondence of an ericaceous cover classification scheme done with a Landsat-TM image and results of our ground survey using an independent set of 275 sample plots. Discrimination of ericaceous shrub cover from other land cover types was achieved with precision at both spatial resolutions with producer accuracies of 87.7% and 79.4% from IKONOS and Landsat, respectively. The method is weaker for areas with sparse cover of ericaceous shrubs or dense tree cover. Our method is adapted, therefore, for mapping the spatial distribution of ericaceous shrubs and is compatible with existing forest stand maps.