Search Results (17)

Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to develop a deep learning convolutional neural net (CNN) model (a UNet-based workflow) to automatically detect and characterize RTSs from VHSR imagery. We aimed to understand: (1) the optimal combination of input image tile size (array size) and the CNN network input size (resizing factor/spatial resolution) and (2) the interoperability of the trained UNet models across heterogeneous study sites based on a limited set of training samples. Hand annotation of RTS samples, CNN model training and testing, and interoperability analyses were based on two study areas from high-Arctic Canada: (1) Banks Island and (2) Axel Heiberg Island and Ellesmere Island. Our experimental results revealed the potential impact of image tile size and the resizing factor on the detection accuracies of the UNet model. The results from the model transferability analysis elucidate the effects on the UNet model due the variability (e.g., shape, color, and texture) associated with the RTS training samples. Overall, study findings highlight several key factors that we should consider when operationalizing CNN-based RTS mapping over large geographical extents. Full article

Rapid global warming is catalyzing widespread permafrost degradation in the Arctic, leading to destructive land-surface subsidence that destabilizes and deforms the ground. Consequently, human-built infrastructure constructed upon permafrost is currently at major risk of structural failure. Risk assessment frameworks that attempt to study this issue assume that precise information on the location and extent of infrastructure is known. However, complete, high-quality, uniform geospatial datasets of built infrastructure that are readily available for such scientific studies are lacking. While imagery-enabled mapping can fill this knowledge gap, the small size of individual structures and vast geographical extent of the Arctic necessitate large volumes of very high spatial resolution remote sensing imagery. Transforming this ‘big’ imagery data into ‘science-ready’ information demands highly automated image analysis pipelines driven by advanced computer vision algorithms. Despite this, previous fine resolution studies have been limited to manual digitization of features on locally confined scales. Therefore, this exploratory study serves as the first investigation into fully automated analysis of sub-meter spatial resolution satellite imagery for automated detection of Arctic built infrastructure. We tasked the U-Net, a deep learning-based semantic segmentation model, with classifying different infrastructure types (residential, commercial, public, and industrial buildings, as well as roads) from commercial satellite imagery of Utqiagvik and Prudhoe Bay, Alaska. We also conducted a systematic experiment to understand how image augmentation can impact model performance when labeled training data is limited. When optimal augmentation methods were applied, the U-Net achieved an average F1 score of 0.83. Overall, our experimental findings show that the U-Net-based workflow is a promising method for automated Arctic built infrastructure detection that, combined with existing optimized workflows, such as MAPLE, could be expanded to map a multitude of infrastructure types spanning the pan-Arctic. Full article

Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agricultural area in central China covered by WorldView-2 images is selected to demonstrate our approach. This approach involves the task of distinguishing eight crop-level agricultural land use types. To this end, six widely used individual ML classifiers are evaluated. We further improved their performance by independently implementing bagging and stacking ensemble learning (EL) techniques. The results show that the bagging models improved the performance of unstable classifiers, but these improvements are limited. In contrast, the stacking models perform better, and the Stacking #2 model (overall accuracy = 83.91%, kappa = 0.812), which integrates the three best-performing individual classifiers, performs the best of all of the built models and improves the classwise accuracy of almost all of the land use types. Since classification performance can be significantly improved without adding costly data collection, stacking-ensemble mapping approaches are valuable for the spatial management of complex agricultural areas. We also demonstrate that using geometric and textural features extracted from VHSR images can improve the accuracy of parcel-level smallholder crop mapping. The proposed framework shows the great potential of combining EL technology with VHSR imagery for accurate mapping of smallholder crops, which could facilitate the development of parcel-level crop identification systems in countries dominated by smallholder agriculture. Full article

The application of deep learning techniques, especially deep convolutional neural networks (DCNNs), in the intelligent mapping of very high spatial resolution (VHSR) remote sensing images has drawn much attention in the remote sensing community. However, the fragmented distribution of urban land use types and the complex structure of urban forests bring about a variety of challenges for urban land use mapping and the extraction of urban forests. Based on the DCNN algorithm, this study proposes a novel object-based U-net-DenseNet-coupled network (OUDN) method to realize urban land use mapping and the accurate extraction of urban forests. The proposed OUDN has three parts: the first part involves the coupling of the improved U-net and DenseNet architectures; then, the network is trained according to the labeled data sets, and the land use information in the study area is classified; the final part fuses the object boundary information obtained by object-based multiresolution segmentation into the classification layer, and a voting method is applied to optimize the classification results. The results show that (1) the classification results of the OUDN algorithm are better than those of U-net and DenseNet, and the average classification accuracy is 92.9%, an increase in approximately 3%; (2) for the U-net-DenseNet-coupled network (UDN) and OUDN, the urban forest extraction accuracies are higher than those of U-net and DenseNet, and the OUDN effectively alleviates the classification error caused by the fragmentation of urban distribution by combining object-based multiresolution segmentation features, making the overall accuracy (OA) of urban land use classification and the extraction accuracy of urban forests superior to those of the UDN algorithm; (3) based on the Spe-Texture (the spectral features combined with the texture features), the OA of the OUDN in the extraction of urban land use categories can reach 93.8%, thereby the algorithm achieved the accurate discrimination of different land use types, especially urban forests (99.7%). Therefore, this study provides a reference for feature setting for the mapping of urban land use information from VHSR imagery. Full article

Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth science remote sensing applications focus on geo object detection and classification from multispectral (MS) imagery. MS imagery includes RGB and narrow spectral channels from near- and/or middle-infrared regions of reflectance spectra. The central objective of this exploratory study is to understand to what degree MS band statistics govern DLCNN model predictions. We scaffold our analysis on a case study that uses Arctic tundra permafrost landform features called ice-wedge polygons (IWPs) as candidate geo objects. We choose Mask RCNN as the DLCNN architecture to detect IWPs from eight-band Worldview-02 VHSR satellite imagery. A systematic experiment was designed to understand the impact on choosing the optimal three-band combination in model prediction. We tasked five cohorts of three-band combinations coupled with statistical measures to gauge the spectral variability of input MS bands. The candidate scenes produced high model detection accuracies for the F1 score, ranging between 0.89 to 0.95, for two different band combinations (coastal blue, blue, green (1,2,3) and green, yellow, red (3,4,5)). The mapping workflow discerned the IWPs by exhibiting low random and systematic error in the order of 0.17–0.19 and 0.20–0.21, respectively, for band combinations (1,2,3). Results suggest that the prediction accuracy of the Mask-RCNN model is significantly influenced by the input MS bands. Overall, our findings accentuate the importance of considering the image statistics of input MS bands and careful selection of optimal bands for DLCNN predictions when DLCNN architectures are restricted to three spectral channels. Full article

Offering remarkable biodiversity, coastal salt marshes also provide a wide variety of ecosystem services: cultural services (leisure, tourist amenities), supply services (crop production, pastoralism) and regulation services including carbon sequestration and natural protection against coastal erosion and inundation. The consideration of this coastal protection ecosystem service takes part in a renewed vision of coastal risk management and especially marine flooding, with an emerging focus on “nature-based solutions.” Through this work, using remote-sensing methods, we propose a novel drone-based spatial modeling methodology of the salt marsh hydrodynamic attenuation at very high spatial resolution (VHSR). This indirect modeling is based on in situ measurements of significant wave heights (Hm₀) that constitute the ground truth, as well as spectral and topographical predictors from VHSR multispectral drone imagery. By using simple and multiple linear regressions, we identify the contribution of predictors, taken individually, and jointly. The best individual drone-based predictor is the green waveband. Dealing with the addition of individual predictors to the red-green-blue (RGB) model, the highest gain is observed with the red edge waveband, followed by the near-infrared, then the digital surface model. The best full combination is the RGB enhanced by the red edge and the normalized difference vegetation index (coefficient of determination (R²): 0.85, root mean square error (RMSE): 0.20%/m). Full article

The quantitative characterization of landscape structure is critical to assess conservation, and monitor and manage biodiversity. The Mediterranean Basin is a biodiversity hotspot that illustrates the strong relationship between biodiversity and the complexity of the landscape mosaic. Our objective was to test the relevance of two textural indices and one radiometric index (the normalized difference vegetation index (NDVI)) to characterize vegetation structure. These indices could be used as indicators of vegetation composition and organization of four vertical strata when derived from airborne and Pléiades space-borne VHSR imagery. More specifically, we analyzed the influence of the spatial resolution and the radiometric information on the characterization of the landscape structure. Our results indicated that NDVI information at 0.5 m spatial resolution was necessary to be able to incorporate the heterogeneity of vegetation structure. Indices derived from lower resolution NDVI images or different radiometric information than airborne images also proved to be sensitive to vegetation fragmentation and composition. NDVI images brought out details on ligneous/herbs patterns while panchromatic image brought out more details on herbs/bare soil patterns. Combined textural and NDVI indices show strong potential for vegetation structure understanding, allowing detailed mapping. NDVI information shows good potential for applications related to landscape closure dynamics; related habitat degradation indicators caused by shrub encroachment. Panchromatic derived information, on the other hand, provides information relevant in applications focusing grazing management. Full article

The use of Very High Spatial Resolution (VHSR) imagery in remote sensing applications is nowadays a current practice whenever fine-scale monitoring of the earth’s surface is concerned. VHSR Land Cover classification, in particular, is currently a well-established tool to support decisions in several domains, including urban monitoring, agriculture, biodiversity, and environmental assessment. Additionally, land cover classification can be employed to annotate VHSR imagery with the aim of retrieving spatial statistics or areas with similar land cover. Modern VHSR sensors provide data at multiple spatial and spectral resolutions, most commonly as a couple of a higher-resolution single-band panchromatic (PAN) and a coarser multispectral (MS) imagery. In the typical land cover classification workflow, the multi-resolution input is preprocessed to generate a single multispectral image at the highest resolution available by means of a pan-sharpening process. Recently, deep learning approaches have shown the advantages of avoiding data preprocessing by letting machine learning algorithms automatically transform input data to best fit the classification task. Following this rationale, we here propose a new deep learning architecture to jointly use PAN and MS imagery for a direct classification without any prior image sharpening or resampling process. Our method, namely $MultiResoLCC$ , consists of a two-branch end-to-end network which extracts features from each source at their native resolution and lately combine them to perform land cover classification at the PAN resolution. Experiments are carried out on two real-world scenarios over large areas with contrasted land cover characteristics. The experimental results underline the quality of our method while the characteristics of the proposed scenarios underline the applicability and the generality of our strategy in operational settings. Full article

The microtopography associated with ice-wedge polygons governs many aspects of Arctic ecosystem, permafrost, and hydrologic dynamics from local to regional scales owing to the linkages between microtopography and the flow and storage of water, vegetation succession, and permafrost dynamics. Wide-spread ice-wedge degradation is transforming low-centered polygons into high-centered polygons at an alarming rate. Accurate data on spatial distribution of ice-wedge polygons at a pan-Arctic scale are not yet available, despite the availability of sub-meter-scale remote sensing imagery. This is because the necessary spatial detail quickly produces data volumes that hamper both manual and semi-automated mapping approaches across large geographical extents. Accordingly, transforming big imagery into ‘science-ready’ insightful analytics demands novel image-to-assessment pipelines that are fueled by advanced machine learning techniques and high-performance computational resources. In this exploratory study, we tasked a deep-learning driven object instance segmentation method (i.e., the Mask R-CNN) with delineating and classifying ice-wedge polygons in very high spatial resolution aerial orthoimagery. We conducted a systematic experiment to gauge the performances and interoperability of the Mask R-CNN across spatial resolutions (0.15 m to 1 m) and image scene contents (a total of 134 km²) near Nuiqsut, Northern Alaska. The trained Mask R-CNN reported mean average precisions of 0.70 and 0.60 at thresholds of 0.50 and 0.75, respectively. Manual validations showed that approximately 95% of individual ice-wedge polygons were correctly delineated and classified, with an overall classification accuracy of 79%. Our findings show that the Mask R-CNN is a robust method to automatically identify ice-wedge polygons from fine-resolution optical imagery. Overall, this automated imagery-enabled intense mapping approach can provide a foundational framework that may propel future pan-Arctic studies of permafrost thaw, tundra landscape evolution, and the role of high latitudes in the global climate system. Full article

Remote sensing tools (RS) can contribute to a better understanding of the diversity of natural and semi-naturals habitats, their spatial distribution, and their conservation status. RS can also provide a generic set of derived indicators to support local to regional habitat monitoring. Here we propose a set of synthetic continuous textural indices computed from high spatial resolution airborne images for the characterization of vegetation structure in very heterogeneous landscape mosaics. These indices are based on Fourier-based textural ordination (FOTO) of very-high-resolution images. We investigate the relationship between textural indices and a set of common landscape metrics derived from vegetation maps, identifying four strata of interest: bare soil, herbs, low ligneous, and high ligneous. We identify two continuous textural indices, the first one being related to vegetation strata fragmentation and the second being related to the dominance of high ligneous. The combination of these two textural indices with the Normalized Difference Vegetation Index (NDVI) provides a synoptic and accurate overview of the spatial organization of the different vegetation strata. The methodological approach presented herein has a generic value in response to national conservation targets in the context of mapping relevant habitat indicators. Full article

Detection of built-up areas from Very High Spatial Resolution (VHSR) remote sensing images is a critical step in urbanization monitoring. This paper presents a method for extracting built-up areas from VHSR remote sensing imagery by using feature-level-based fusion of right angle corners, right angle sides and road marks. This method has six main steps. First, line segments are detected. Second, the Harris corner points are detected. Third, the right-angle corners and right-angle sides are determined by cross-verification of the above detected Harris corners and line segments. Fourth, the potential road marks are detected by the template matching method. Fifth, a built-up index image is constructed. Finally, the built-up areas are extracted through a binary thresholding of the above index image. Three satellite images with wide coverage are employed for evaluating the above proposed method. The experimental results suggest that the proposed method outperforms the classic PanTex method. On average, the completeness and the quality of the proposed method are respectively 17.94% and 13.33% better than those of the PanTex method, while there is no great difference between the two methods on the correctness. Full article

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification. Full article

Very high spatial resolution (VHSR) stereo-imagery-derived digital surface models (DSM) can be used to generate digital elevation models (DEM). Filtering algorithms and triangular irregular network (TIN) densification are the most common approaches. Most filter-based techniques focus on image-smoothing. We propose a new approach which makes use of integrated object-based image analysis (OBIA) techniques. An initial land cover classification is followed by stratified land cover ground point sample detection, using object-specific features to enhance the sampling quality. The detected ground point samples serve as the basis for the interpolation of the DEM. A regional uncertainty index (RUI) is calculated to express the quality of the generated DEM in regard to the DSM, based on the number of samples per land cover object. The results of our approach are compared to a high resolution Light Detection and Ranging (LiDAR)-DEM, and a high level of agreement is observed—especially for non-vegetated and scarcely-vegetated areas. Results show that the accuracy of the DEM is highly dependent on the quality of the initial DSM and—in accordance with the RUI—differs between the different land cover classes. Full article

The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked “without adaptation” and “with adaptation” on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational “image-to-assessment pipeline” that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census. Full article

Whilst most hedgerow functions depend upon hedgerow structure and hedgerow network patterns, in many ecological studies information on the fragmentation of hedgerows network and canopy structure is often retrieved in the field in small areas using accurate ground surveys and estimated over landscapes in a semi-quantitative manner. This paper explores the use of radar SAR imagery to (i) detect hedgerow networks; and (ii) describe the hedgerow canopy heterogeneity using TerraSAR-X imagery. The extraction of hedgerow networks was achieved using an object-oriented method using two polarimetric parameters: the Single Bounce and the Shannon Entropy derived from one TerraSAR-X image. The hedgerow canopy heterogeneity estimated from field measurements was compared with two backscattering coefficients and three polarimetric parameters derived from the same image. The results show that the hedgerow network and its fragmentation can be identified with a very good accuracy (Kappa index: 0.92). This study also reveals the high correlation between one polarimetric parameter, the Shannon entropy, and the canopy fragmentation measured in the field. Therefore, VHSR radar images can both precisely detect the presence of wooded hedgerow networks and characterize their structure, which cannot be achieved with optical images. Full article