Interpretation Of Aerial Imagery Research Articles

Geomorphological Insights to Analyze the Kinematics of a DSGSD in Western Sicily (Southern Italy)

Deep-Seated Gravitational Slope Deformations (DSGSDs) are common in many geological environments, and due to their common limited displacement rate, they can remain unrecognized for a long time. Among the most significant events in Sicily is the Mt. San Calogero DSGSD. To contribute to a better understanding of its characteristics, including the geologic setting promoting its development, ongoing kinematics, and mechanism, a specific analysis was completed. In this paper, the results of this analysis, based on a three-folded strategy, are provided and interpreted in the context of DSGSD predisposing conditions and controlling factors. Especially, field observations associated to visual interpretation of aerial imagery were used for the identification and mapping of main geological features and landforms, high-resolution X-Band DInSAR data enabled researchers to fully characterize the deformational behavior of the slope, while a reduced complexity slope stability analysis allowed them to reconstruct the deep geometry of the DSGSD. Results from the analysis indicate that the DSGSD of Mt. San Calogero is composed of three blocks corresponding to fault-bounded tectonic elements and characterized by a specific kinematics and sensitivity to external forcing (i.e., rainfall), multiple landslides are associated to the DSGSD in the area and the deep geometry of the DSGSD is concave upward and resemble the characteristics of a rotational slide. The interpretation of the results suggests that the formation and the deformation of the Mt. San Calogero DSGSD are linked with the local and regional fault systems related to the Sicilian orogen, while shallow landslides are triggered, in clayey terrains, mostly by rainfalls. In addition, the integrated approach reveals that active tectonics and rainfalls in the San Calogero massive relief are the main driving forces of its different deformation behavior.

Satellite detection of canopy-scale tree mortality and survival from California wildfires with spatio-temporal deep learning

Mapping forest disturbances is paramount to carbon monitoring, estimating environmental drivers, and developing strategies to enhance forest resilience. Existing forest change products from Landsat and Sentinel-2 have improved our understanding of large-scale disturbance patterns; however, their relatively coarse spatial resolution (10 to 30-m) leads to the use of mixed pixels which constrains their application for detecting heterogeneous survival or mortality outcomes occurring at the level of individual trees or canopies. PlanetScope multispectral imagery at 3-m and near-daily frequency offers new capabilities to detect and monitor diverse tree mortality patterns following disturbance across landscapes. This research proposes a framework to detect canopy-scale (3 × 3-m) tree/shrub mortality and survival using PlanetScope monthly time series. A 3D Spatio-Temporal Convolutional Neural Network (ST-CNN) deep learning model was designed to fully utilize the spatial context and the temporal change unique to canopy survival and mortality from the PlanetScope time series. As a crucial component for training robust and scalable deep learning models, a large set of labels was collected via a semi-automatic workflow by combining pre-disturbance lidar crown segmentation and post-disturbance aerial imagery interpretation. We applied the framework to detect canopy-scale mortality and survival following 15 large wildfires in California from 2018–2021. We sampled 1,176 384 × 384-m scenes from burned areas with pre-fire aerial lidar, containing >1.8M tree and shrub canopy polygons labeled as dead or alive following wildfire. Evaluated with an independent testing dataset, the optimized ST-CNN model detects heterogeneous patterns representing survival and mortality outcomes at 3-m resolution which accurately align with observed/labeled data. Tree mortality detection accuracy was high and stable in the Sierra Nevada and North Coast Mountains ecoregions (user’s = 83%–86%; producer’s = 81%–82%), but decreased slightly within the sparser Central Foothills and South Coast and Mountains (user’s = 77%–81%; producer’s = 58%–61%) often due to confusion between shrub and tree mortality. Producer’s accuracy of tree mortality and survival increased with canopy height and remained stable (>75%) on canopies taller than 11-m. Further, a sensitivity analysis demonstrates the performance benefits of using spatial and/or temporal convolutions in the ST-CNN architecture for model prediction. Lastly, we demonstrate the scalability of the ST-CNN for regional-scale application on all large 2020 wildfires in California (∼1.6 Mha burn area). The wall-to-wall post-fire maps showed an overall 3-m tree mortality rate of 58.8%, ranging from 32% to 94% among individual fires. The trained ST-CNN provides an ecologically detailed estimation of the pre-disturbance forest composition (trees, shrubs, non-woody) and their outcomes (survival or mortality) post-disturbance. These data will improve higher resolution monitoring and assessment of forest disturbance impacts, allow for better understanding of forest vulnerability, and support forest management strategies and actions.

Thematic interpretation of aerial photographs of forest areas based on conceptual modeling

Earth remote sensing (ERS) is one of the main sources of information about the earth's surface. With the development of unmanned aerial vehicles (UAVs), it became possible to take aerial photographs with high spatial resolution, which can be used to more accurately identify objects. The purpose of the study is to increase the reliability of aerial imagery interpretation by developing a methodology for automated image processing based on conceptual modeling. An analysis of the methods for thematic interpretation of images showed that none of them provides sufficient quality of segmentation without additional adjustment to the subject area. The technique of conceptual modeling of subject tasks is applied, which ensures the adequacy of syntactic representations (including various images) and makes it possible to control the logic of solving problems and reduce the number of errors at the stage of its software implementation. The description of the information system for thematic interpretation of images, which implements the developed methodology, is given. An assessment was made of the reliability of thematic interpretation of images of forest areas. It is shown that the developed technique allowed to improve the identification of forest objects on aerial photographs by 18.6%. The development of this technique can be carried out to determine the characteristics of identifiable trees: age, species, height, and wood stock.

Detecting trends in seagrass cover through aerial imagery interpretation: Historical dynamics of a Posidonia oceanica meadow subjected to anthropogenic disturbance

Over the last century, P. oceanica meadows have undergone significant regression along Mediterranean coasts due to anthropogenic disturbances. Using an integrated approach based on historical aerial photography interpretation in GIS environment and Bayesian modelling, we mapped the upper limits of a P. oceanica meadow and identified the most influential disturbance factors acting along the coast of Giglio Island, where several local human-mediated impacts have historically co-occurred from 1968 to 2013. Model selection based on the DIC criterion suggested that the presence of the impacts is suitable for describing the seagrass coverage variation. Similarities in P. oceanica cover within 13 investigated zones were highlighted when considering the most relevant impacts, such as harbour expansion, mining, and anchoring. The detected adverse effects indicate the need for implementing management actions focusing on the present and past sources of impact to reduce their effect on P. oceanica beds actively.

GEOLOGICAL INTERPRETATION OF LANDSAT IMAGERY OF NIGERIA’S HAWAL PRECAMBRIAN BASEMENT COMPLEX TERRAIN

The Hawal Basement Complex is located in Nigeria’s NE region. Satellite imagery used in this study covers longitude 12º 00’ to 13º 30’E, and latitudes 9º 30’ to 11º 00’N. The aim of the study is to decipher the regional geologic structures based on analysis and to determine the mineralization implications from the landsat lineament data. The total land surface area covered is about 23,674.88 km2 and includes part of the north sector of Yola sedimentary Rift Basin. The data consists of LANDSAT TM and SPOT XS scenes collated as mosaics. The imagery was monoscopically studied. Classical aerial imagery interpretation techniques of mapping lineaments and tracing regional geological boundaries were used. A total of 368 lineaments were mapped and analyzed using multi-graphical analytical techniques. The results are presented as isolinear contour maps (isodensity and lineaments intersection maps). The isodensity lineament maps show two main areas of lineament concentrations, namely Shani area lying westward and Michika-Mubi area to the east. Reasons advanced for the preponderance of lineaments in parts of the study area are the combined effects of isostatic basement uplift and lithospheric stretching during the formation of Yola Rift and Chad Basin respectively, which caused large scale crustal deformations. Also, Precambrian and Cenozoic magmatic activities that caused emplacements of older granites and basaltic bodies respectively in the area contributed to fracturation of the crust. Fewer lineaments are observed over the sedimentary Rift zone and this is attributed to masking effect of the sediments on basement fractures. The lineament intersection map shows a prominent NE-SW zone of highest intersection which is parallel to the general flow direction of the Hawal River. Prominent lineament directions are N-S (0-20º), and NE-SW (40º- 60º), which are Pan African Orogenic directions. Aerogeological studies of this type are important in national economic development.

Earth Observation and GIS-Based Analysis for Landslide Susceptibility and Risk Assessment

Landslides can cause severe problems to the social and economic well-being. In order to effectively mitigate landslide hazards, the development of detailed susceptibility maps is required, towards implementing targeted risk management plans. This study aims to create detailed landslide susceptibility (LS) and landslide risk (LR) maps of the Sperchios River basin by applying an expert semi-quantitative approach that integrates the Geographic Information Systems (GIS)-based multicriteria analysis and Earth Observation (EO) data. Adopting the analytic hierarchy process (AHP) for a weighted linear combination (WLC) approach, eleven evaluation parameters were selected. The results were validated using a historic landslide database, enriched with new landslide locations mapped by satellite and aerial imagery interpretation and field surveys. Moreover, the landslide risk map of the area was also developed, based on the LS delineation, considering additionally the anthropogenic exposure and overall vulnerability of the area. The results showed that the most susceptible areas are located at the west and south-west regions of the basin. The synergistic use of GIS-based analysis and EO data can provide a useful tool for the design of natural hazards prevention policy at highly susceptible to risk landslide risk areas.

Mapping Kenyan Grassland Heights Across Large Spatial Scales with Combined Optical and Radar Satellite Imagery

Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.

Grossflächige Klassifikation von Gebüschwald mit Fernerkundungsdaten

Large-scale classification of shrub forest with remote sensing data Information on shrub forest distribution and development is important for a range of forestry- and ecologically-related questions, but current and area-wide datasets have been characterized by limited availability. In this study, the mapping of shrub forests dominated by green alder, mountain pine and hazel for the canton of Grison was investigated, based on available nationwide remote sensing data. Satellite data from Sentinel-1 and Sentinel-2, as well as a vegetation height and an elevation model were used. Training areas provided by the canton and supplemented by aerial imagery interpretation were used for a supervised classification with Random Forest, a decision tree-based machine learning algorithm. Independent validation of the results was carried out with data from the National Forest Inventory (NFI). Green alder and mountain pine forests were classified with high accuracy of 92.1% respectively 86.7%, whereas for hazel shrub forests, the internal model accuracy was only 66.7%. The resulting area expansion of the shrub forest was comparable with findings based on the NFI. A direct comparison with the NFI aerial imagery interpretation points revealed major discrepancies. The main reason for this is the different degree of spatial detail. However, NFI areas with a high percentage of shrubs were reliably classified as shrub forest. The method presented here underscores the potential of remote sensing data available throughout Switzerland for an essentially objective, costefficient and large-scale mapping of shrub forests with an accuracy applicable in practice.

Using object‐based image analysis with multi‐temporal aerial imagery and LiDAR to detect change in temperate intertidal habitats

Abstract Intertidal habitat maps are needed at both fine and coarse scales to monitor change and inform conservation and management, but current methods of field survey and expert interpretation of aerial imagery can be time‐consuming and subjective. Object‐based image analysis (OBIA) of remote sensing data is increasingly employed for producing habitat or land cover maps. Users create automated workflows to segment imagery, creating ecologically meaningful objects, which are then classified based on their spectral or geometric properties, relationships to other objects and contextual data. This study evaluates the change‐detection capability of OBIA in the intertidal environment by developing and comparing two OBIA methods for quantifying change in extent and distribution of habitats from freely available multi‐temporal aerial imagery and LiDAR data. Despite considerable variability in the data, pre‐ and post‐classification change detection methods had sufficient accuracy (mean overall accuracy from 70.5 to 82.6%) to monitor deviation from a background level of natural environmental fluctuation. This insight into spatial and temporal patterns of natural cyclical change and their detectability by OBIA could inform use of remote sensing for regular, rapid coastal assessment, providing an alert system to direct survey resources to areas of ecologically relevant change.

Application of change detection techniques in geomorphological evolution of coastal areas. Example: Mouth of the River Ebro (period 1957–2013)

This study aims to test the effectiveness of the changes detection techniques to determine the evolution of the various geomorphological units identified in Ebro River mouth, in order to establish their evolutionary trends and to explain the changes between 1957 and 2013 years. To evaluate the most significant changes in the Ebro River mouth, the different geomorphological units present in the area were obtained by interpretation of aerial imagery. It has been noted that the period 1957–1984 was more dynamic and intense than the period 1984–2013, indicating a loss of capacity of the processes causing changes, and the shoreline is evolving toward a more stable morphology. It was determined that the most important processes that took place were mainly marine erosion processes and anthropogenic expansion. On the characterization in the entire period studied, it was determined that growth or decrease processes (net change) are as relevant in the region as processes which include location changes (swap). The change analysis methodology allows analyzing the evolution of coastal geomorphology, since it allows the determination and characterization of the principal transitions amongst the identified geomorphological units, and, therefore, to obtain the main processes that dominate the area, which is particularly relevant on highly vulnerable environments, such as coastal deltaic areas.

Impacts of surface water diversions for marijuana cultivation on aquatic habitat in four northwestern California watersheds.

Marijuana (Cannabis sativa L.) cultivation has proliferated in northwestern California since at least the mid-1990s. The environmental impacts associated with marijuana cultivation appear substantial, yet have been difficult to quantify, in part because cultivation is clandestine and often occurs on private property. To evaluate the impacts of water diversions at a watershed scale, we interpreted high-resolution aerial imagery to estimate the number of marijuana plants being cultivated in four watersheds in northwestern California, USA. Low-altitude aircraft flights and search warrants executed with law enforcement at cultivation sites in the region helped to validate assumptions used in aerial imagery interpretation. We estimated the water demand of marijuana irrigation and the potential effects water diversions could have on stream flow in the study watersheds. Our results indicate that water demand for marijuana cultivation has the potential to divert substantial portions of streamflow in the study watersheds, with an estimated flow reduction of up to 23% of the annual seven-day low flow in the least impacted of the study watersheds. Estimates from the other study watersheds indicate that water demand for marijuana cultivation exceeds streamflow during the low-flow period. In the most impacted study watersheds, diminished streamflow is likely to have lethal or sub-lethal effects on state-and federally-listed salmon and steelhead trout and to cause further decline of sensitive amphibian species.

Geomorphological and geoforensic interpretation of maps, aerial imagery, conditions of diggability and the colour-coded RAG prioritization system in searches for criminal burials

Abstract The objective of this paper is to draw attention to the use of air photographs, diggability surveys and the RAG (Red–Amber–Green) prioritization system during police ground searches for burials. The acquisition, analysis and interpretation of aerial imagery by a geologist may provide a useful reconnaissance technique to help delineate and prioritize search areas. A diggability survey may provide information on the ease and efficiency with which the ground may be dug and reinstated by an offender. This is influenced by the depth of the soils, the geology, groundwater, obstructions, the digging implements used, the ability of the offender, the nature of item being buried and the time frames involved. The results of a diggability survey may conveniently be presented as a RAG, map which can help in prioritizing the search. The RAG system appears to have been used independently by geologists, police/law enforcement and the military, and has evolved differently and independently since the early part of the 1900s. These methods have been applied to law enforcement searches for graves and other buried objects as demonstrated by operational case examples.

Automated segmentation of vegetation structure units in a Mediterranean landscape

In Mediterranean regions, the combination of disturbances, life histories, plant regeneration traits, and microhabitat variability form highly heterogeneous vegetation mosaics which shift in space and time. Consequently, structure-based forest management is emerging as a superior alternative to management of vegetation formations in such areas. Delineation of management units in these areas is often based on manual interpretation of aerial imagery coupled with field surveys. Here, we propose an alternative approach that is based on segmentation of remotely sensed height and cover maps derived from light detection and ranging (LiDAR) imagery. A large suite of alternative segmentation maps was generated using multiresolution segmentation (MS) with different parameters, and an area-fit approach used to select the map that most successfully captured a reference set of structural units delineated manually. We assessed the feasibility of this approach in a nature reserve in northern Israel, compared the resulting map with a traditional vegetation formations map, and explored the performance of the segmentation algorithm under various parameter combinations. Pronounced differences between the structure and formation maps highlight the suitability of this approach as an alternative to the existing methods of delineating vegetation units in Mediterranean systems, and possibly in other systems as well.

Yield Mapping and GIS for Root Crops

I will show how yield mapping data, aerial photography data, and other agronomic data (fertility, soil parameters) can be integrated into a Geographical Information System (GIS) and give a “feel” of the value of these tools to look at crop production as a whole. The capability of GIS in handling and displaying several layers of georeferenced data leads naturally to a decisionmaking process quite similar to the one used in traditional photo interpretation of aerial imagery. This approach can be very valuable for farm managers and consultants in crop production.

Automating knowledge acquisition for aerial image interpretation

The interpretation of aerial imagery requires substantial knowledge about the scene under consideration. Knowledge about the type of scene-airport, suburban housing development, urban city-aids in low-level and intermediate level image analysis and will drive high-level interpretation by constraining search for plausible consistent scene models. Collecting and representing large knowledge bases requires specialized tools. In this paper we describe the organization of a set of tools for interactive knowledge acquisition of scene primitives and spatial constraints for interpretation of aerial imagery. These tools include a user interface for interactive knowledge acquisition, an automated knowledge compiler that transforms schemabased assertions into productions that are directly executable by our interpretation system, and a performance analysis tool that generates a critique of the final interpretation. The generality of these tools is demonstrated by the generation of rules for a new task, suburban house scenes, and the analysis of a set of imagery by our interpretation system.

EXTRAPOLATION IN TIME OF REFERENCE DATA ON THE CONDITION OF NATURAL FEATURES: THE EXAMPLE OF WINTER WHEAT

The author outlines a method for extrapolating reference spectral data describing the condition of certain crops obtained for a particular point in time to periods for which reliable remote sensing data are not available. Examination of the correspondence between crop “condition classes,” identified through visual interpretation of aerial imagery, and actual yields/biomass for a number of phases of crop development of winter wheat for several crop years provides basic information for a probability matrix of the condition of a crop at times B, C, and D based on previous conditions at times A, B, and C. Translated from: Vestnik Moskovskogo Universiteta, geografiya, 1988, No. 4, pp. 73-79.

Rule-Based Interpretation of Aerial Imagery

In this paper, we describe the organization of a rule-based system, SPAM, that uses map and domain-specific knowledge to interpret airport scenes. This research investigates the use of a rule-based system for the control of image processing and interpretation of results with respect to a world model, as well as the representation of the world model within an image/map database. We present results on the interpretation of a high-resolution airport scene wvhere the image segmentation has been performed by a human, and by a region-based image segmentation program. The results of the system's analysis is characterized by the labeling of individual regions in the image and the collection of these regions into consistent interpretations of the major components of an airport model. These interpretations are ranked on the basis of their overall spatial and structural consistency. Some evaluations based on the results from three evolutionary versions of SPAM are presented.

An evaluation of landscape units for watershed similarity analyses

The landscape unit approach to land classification was applied through the use of aerial imagery interpretation to Guadalupe Mountains National Park, Texas. The delineated units were evaluated for uniqueness on the basis of their percentage composition in three attributes: vegetative cover, slope class and geologic parent material. Major watersheds within the park were then characterized in terms of their percentage composition with respect to the delineated landscape units. Simultaneously, watersheds were characterized using traditional hydrologic inventory and description data such as: areal extent, Miller's circularity ratio, basin perimeter, drainage density, main channel length, mean channel slope, maximum—minimum elevations, vegetative composition, geologic composition and slope class composition. Using the two different data bases (i.e. traditional approach variables and landscape unit composition) a similarity index computer program was then employed to examine all possible pairs of watersheds and to determine the twenty most similar pairs in each case. These pairs were further examined to develop clusters of highly similar watersheds. Comparison of the similar watershed lists and the clusters developed from the two data bases indicated very favorable results. The time and effort to obtain clusters of highly similar watersheds by means of the hydrologic variable analyses approach exceeded the landscape unit approach by a factor of ten. This reduction in cost coupled with the favorable comparison of the final results indicates a potential for increased use of the landscape unit analysis approach to identify drainage basins which could be expected to respond more or less uniformly to particular management prescriptions.