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Biological soil crusts (biocrusts) occur in drylands globally where they support ecosystem functioning by increasing soil stability, reducing dust emissions and modifying soil resource availability (e.g. water, nutrients). Determining biocrust condition and extent across landscapes continues to present considerable challenges to scientists and land managers. Biocrusts grow in patches, cover vast expanses of rugged terrain and are vulnerable to physical disturbance associated with ground‐based mapping techniques. As such, remote sensing offers promising opportunities to map and monitor biocrusts. While satellite‐based remote sensing has been used to detect biocrusts at relatively large spatial scales, few studies have used high‐resolution imagery from Unmanned Aerial Systems (UAS) to map fine‐scale patterns of biocrusts. We collected sub‐centimeter, true color 3‐band imagery at 10 plots in sagebrush and pinyon‐juniper woodland communities in a semiarid ecosystem in the southwestern US and used object‐based image analysis (OBIA) to segment and classify the imagery into maps of light and dark biocrusts, bare soil, rock and various vegetation covers. We used field data to validate the classifications and assessed the spatial distribution and configuration of different classes using fragmentation metrics. Map accuracies ranged from 46 to 77% (average 65%) and were higher in pinyon‐juniper (average 70%) versus sagebrush (average 60%) plots. Biocrust classes showed generally high accuracies at both pinyon‐juniper plots (average dark crust = 70%; light crust = 80%) and sagebrush plots (average dark crust = 69%; light crust = 77%). Point cloud density, sun elevation and spectral confusion between vegetation cover explained some differences in accuracy across plots. Spatial analyses of classified maps showed that biocrust patches in pinyon‐juniper plots were generally larger, more aggregated and contiguous than in sagebrush plots. Pinyon‐juniper plots also had greater patch richness and a lower Shannon evenness index than sagebrush plots, suggesting greater soil cover heterogeneity in this plant community type. Biological soil crusts (biocrusts) contribute to ecosystem functioning by increasing soil stability, reducing dust emissions and modifying soil resource availability. In this study, we used high‐resolution 3‐band Unmanned Aerial Systems (UAS) imagery to map fine‐scale patterns of biocrusts.

Very high‐resolution (VHR) image data, including from unmanned aerial vehicle (UAV) platforms, are increasingly acquired for wildlife surveys. Animals or structures they build (e.g. nests) can be photointerpreted from these images, however, automated detection is required for more efficient surveys. We developed semi‐automated analyses to map white‐bellied sea eagle (Haliaeetus leucogaster) nests in VHR aerial photographs of the Houtman Abrolhos Islands, Western Australia, an important breeding site for many seabird species. Nest detection is complicated by high environmental heterogeneity at the scale of nests (~1–2 m), the presence of many features that resemble nests and the variability of nest size, shape and context. Finally, the rarity of nests limits the availability of training data. These challenges are not unique to wildlife surveys and we show how they can be overcome by an innovative integration of object‐based image analyses (OBIA) and the powerful machine learning one‐class classifier Maxent. Maxent classifications using features characterizing object texture, geometry and neighborhood, along with limited object color information, successfully identified over 90% of high quality nests (most weathered and unusually shaped nests were also detected, but at a slightly lower rate) and labeled <2% of objects as candidate nests. Although this overestimates the occurrence of nests, the results can be visually screened to rule out all but the most likely nests in a process that is simpler and more efficient than manual photointerpretation of the full image. Our study shows that semi‐automated image analyses for wildlife surveys are achievable. Furthermore, the developed strategies have broad relevance to image processing applications that seek to detect rare features differing only subtly from a heterogeneous background, including remote sensing of archeological remains. We also highlight solutions to maximize the use of imperfect or uncalibrated image data, such as some UAV‐based imagery and the growing body of VHR imagery available in Google Earth and other virtual globes. Very high‐resolution image data are increasingly acquired for wildlife surveys. It has been difficult to detect nests of the white‐bellied sea eagle (Haliaeetus leucogaster) from image data, but these challenges were overcome by an innovative integration of object‐based image analyses (OBIA) and the powerful machine learning one‐class classifier Maxent. The strategies developed have broad relevance to image processing applications that seek to detect rare features differing only subtly from a heterogeneous background, and can be applied to rigorous analyses of uncalibrated image data, for instance available in virtual globes.

Water body extraction is an important part of water resource management and has been the topic of a number of research works related to remote sensing for over two decades. Extracting water bodies from satellite images with a pixel-based method or indexes cannot eliminate other objects that have a low albedo, such as shadows and built-up areas. Since their spectral differences cannot be separated, in this paper a method that combines a pixel-based index and object-based method has been used on a Sentinel-2 satellite image with a resolution of 10 m. The method uses image segmentation on a multispectral image containing 13 bands. It also uses indexes used for extracting water bodies, such as the Normalized Difference Water Index (NDWI). Two study areas with different characteristics have been chosen, one mountainous and one urban region, both of them located in Macedonia. Using object-based techniques and pixel-based indexes, such as NDWI, the results from the NDWI have been improved by a kappa value of more than 0.5.

Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success.

Object-based image analysis (OBIA) has been widely used for land use and land cover (LULC) mapping using optical and synthetic aperture radar (SAR) images because it can utilize spatial information, reduce the effect of salt and pepper, and delineate LULC boundaries. With recent advances in machine learning, convolutional neural networks (CNNs) have become state-of-the-art algorithms. However, CNNs cannot be easily integrated with OBIA because the processing unit of CNNs is a rectangular image, whereas that of OBIA is an irregular image object. To obtain object-based thematic maps, this study developed a new method that integrates object-based post-classification refinement (OBPR) and CNNs for LULC mapping using Sentinel optical and SAR data. After producing the classification map by CNN, each image object was labeled with the most frequent land cover category of its pixels. The proposed method was tested on the optical-SAR Sentinel Guangzhou dataset with 10 m spatial resolution, the optical-SAR Zhuhai-Macau local climate zones (LCZ) dataset with 100 m spatial resolution, and a hyperspectral benchmark the University of Pavia with 1.3 m spatial resolution. It outperformed OBIA support vector machine (SVM) and random forest (RF). SVM and RF could benefit more from the combined use of optical and SAR data compared with CNN, whereas spatial information learned by CNN was very effective for classification. With the ability to extract spatial features and maintain object boundaries, the proposed method considerably improved the classification accuracy of urban ground targets. It achieved overall accuracy (OA) of 95.33% for the Sentinel Guangzhou dataset, OA of 77.64% for the Zhuhai-Macau LCZ dataset, and OA of 95.70% for the University of Pavia dataset with only 10 labeled samples per class.

In order to optimize the application of herbicides in weed-crop systems, accurate and timely weed maps of the crop-field are required. In this context, this investigation quantified the efficacy and limitations of remote images collected with an unmanned aerial vehicle (UAV) for early detection of weed seedlings. The ability to discriminate weeds was significantly affected by the imagery spectral (type of camera), spatial (flight altitude) and temporal (the date of the study) resolutions. The colour-infrared images captured at 40 m and 50 days after sowing (date 2), when plants had 5–6 true leaves, had the highest weed detection accuracy (up to 91%). At this flight altitude, the images captured before date 2 had slightly better results than the images captured later. However, this trend changed in the visible-light images captured at 60 m and higher, which had notably better results on date 3 (57 days after sowing) because of the larger size of the weed plants. Our results showed the requirements on spectral and spatial resolutions needed to generate a suitable weed map early in the growing season, as well as the best moment for the UAV image acquisition, with the ultimate objective of applying site-specific weed management operations.

Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes. Here, we adopted a lightweight quadcopter as an aerial survey tool and object-based image analysis (OBIA) workflow to demonstrate the strength of such methods in producing very high spatial resolution maps of sensitive marine habitats. Therefore, three different coastal environments were mapped using the autonomous flight capability of a lightweight UAV equipped with a fully stabilized consumer-grade RGB digital camera. In particular we investigated a Posidonia oceanica seagrass meadow, a rocky coast with nurseries for juvenile fish, and two sandy areas showing biogenic reefs of Sabelleria alveolata. We adopted, for the first time, UAV-based raster thematic maps of these key coastal habitats, produced after OBIA classification, as a new method for fine-scale, low-cost, and time saving characterization of sensitive marine environments which may lead to a more effective and efficient monitoring and management of natural resources.

The increased feature space available in object-based classification environments (e.g., extended spectral feature sets per object, shape properties, or textural features) has a high potential of improving classifications. However, the availability of a large number of derived features per segmented object can also lead to a time-consuming and subjective process of optimizing the feature subset. The objectives of this study are to evaluate the effect of the advanced feature selection methods of popular supervised classifiers (Support Vector Machines (SVM) and Random Forest (RF)) for the example of object-based mapping of an agricultural area using Unmanned Aerial Vehicle (UAV) imagery, in order to optimize their usage for object-based agriculture pattern recognition tasks. In this study, several advanced feature selection methods were divided into both types of classifiers (SVM and RF) to conduct further evaluations using five feature-importance-evaluation methods and three feature-subset-evaluation methods. A visualization method was used to measure the change pattern of mean classification accuracy with the increase of features used, and a two-tailed t-test was used to determine the difference between two population means for both repeated ten classification accuracies. This study mainly contribute to the uncertainty analysis of feature selection for object-based classification instead of the per-pixel method. The results highlight that the RF classifier is relatively insensitive to the number of input features, even for a small training set size, whereby a negative impact of feature set size on the classification accuracy of the SVM classifier was observed. Overall, the SVM Recursive Feature Elimination (SVM-RFE) seems to be an appropriate method for both groups of classifiers, while the Correlation-based Feature Selection (CFS) is the best feature-subset-evaluation method. Most importantly, this study verified that feature selection for both classifiers is crucial for the evolving field of Object-based Image Analysis (OBIA): It is highly advisable for feature selection to be performed before object-based classification, even though an adverse impact could sometimes be observed from the wrapper methods.

There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google Earth TM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing).

The increased development of computer vision technology combined with the increased availability of innovative platforms with ultra-high-resolution sensors, has generated new opportunities and fields for investigation in the engineering geology domain in general and landslide identification and characterization in particular. During the last decade, the so-called Unmanned Aerial Vehicles (UAVs) have been evaluated for diverse applications such as 3D terrain analysis, slope stability, mass movement hazard and risk management. Their advantages of detailed data acquisition at a low cost and effective performance identifies them as leading platforms for site-specific 3D modelling. In this study, the proposed methodology has been developed based on Object-Based Image Analysis (OBIA) and fusion of multivariate data resulted from UAV photogrammetry processing in order to take full advantage of the produced data. Two landslide case studies within the territory of Greece, with different geological and geomorphological characteristics, have been investigated in order to assess the developed landslide detection and characterization algorithm performance in distinct scenarios. The methodology outputs demonstrate the potential for an accurate characterization of individual landslide objects within this natural process based on ultra high-resolution data from close range photogrammetry and OBIA techniques for landslide conceptualization. This proposed study shows that UAV-based landslide modelling on the specific case sites provides a detailed characterization of local scale events in an automated sense with high adaptability on the specific case site.