High-resolution Aerial Photographs Research Articles

The Chaochou active fault in southern Taiwan: Characteristics and geomorphological significance as a reverse fault

The Chaochou Fault in southern Taiwan has long been recognized as an active fault, but its exact location is still uncertain. In this study, we focused on the middle part of the Chaochou Fault, where a flight of fluvial terraces was deformed and preserved. High-resolution aerial photos were first used to observe and map these terraces and their vicinity, followed by comprehensive field investigations including RTK-GPS surveys for high-resolution topographic profiles of the deformed terraces and OSL sample collections for obtaining the ages of the terraces. A series of active faults, consisting of Fault A (“FA”), Fault B (“FB”), and Fault Bb (“FBb”), are collectively named the Chaochou Active Fault Zone. The fault zone is recognized and characterized by range-facing scarps, frontal flexural scarps, and discontinuous slopes deformed by FA, FB, and FBb, respectively. The repeated activities of FA were recorded by the incremental fault scarp heights from young to old terraces. Considering the highest fault scarp height of 65.9 ± 6 m observed in T1 and its OSL age of 35.3 ± 4.3 ka, the long-term fault slip rate of FA is about 2 mm/yr.

Estimation of Oil Palm Tree Quantity Using Oil Palm Ecognition and SAGA GIS Based on UAV Imagery (Case Study: Dumai, Riau)

Abstract Oil palm is a plantation commodity in Indonesia that drives the growth of the national economy. The growth of oil palm is rapidly expanding every year, thus requiring efficient land monitoring strategies. Tree counting is one technique used to monitor current conditions in vast plantation areas. Utilizing UAV technology in agricultural mapping is the best option as a tool for capturing high-resolution aerial photos. The objective of this research is to automatically count the number of oil palm trees by comparing two methods and algorithms. GEOBIA (Geography-Based Image Analysis) and Deep Learning classification are methods used for automatic object counting. In this study, high-resolution aerial photo data was utilized, making object detection much more feasible. The research applied the Template Matching and Watershed Segmentation algorithm models in a sample area of 32.5 hectares divided into two blocks: Unknown-2 and E-1. It can be concluded that both methods, with their respective algorithm models, are sufficiently relevant for use in the automatic counting process of oil palm trees. This is evidenced by the accuracy test error values, which are not more than 15%, indicating that the counted oil palm tree results can be used for further data analysis.

The Effect of Land Cover Change on Urban Heat Island Phenomenon Based on Remote Sensing in Probolinggo City

Probolinggo City is located in East Java Province, Indonesia. Based on BPS data for the City of Probolinggo in 2023, it shows an increase in population growth rate of 1.7% annually. The development of the number and increase in the activity of the population of Probolinggo City certainly affects the increase in the number of built-up areas to meet human needs. An increase in the number of built-up land has led to changes in the environment and urban land use, characterized by declining air quality and global warming due to the increasing phenomenon of Urban Heat Island (UHI). This research was conducted to determine the spatial distribution of surface temperature and the Urban Heat Island (UHI) phenomenon and the effect of land cover on the UHI phenomenon. The research was conducted using UHI analysis using the Landsat 8 OLI remote sensing approach to obtain surface temperature values and high-resolution aerial photographs through data extraction. Based on the data processing results carried out over five years in 2013, 2017, and 2022, it is known that high and very high temperatures dominate the surface temperature of Probolinggo City. In addition, there is an effect of land cover on surface temperature based on the results of a simple linear regression analysis.

Colonisation patterns and reef growth of the invasive serpulid Ficopomatus enigmaticus in a Greek coastal lagoon

The reef-building serpulid polychaete Ficopomatus enigmaticus is an invasive species in brackish waters in tropical and temperate zones worldwide and often has massive impacts on ecosystems and anthropogenic activities. In Prokopos Lagoon in Greece, the species has constructed large reefs over the past few decades which occupy a large area of the lagoon. In this study, we used high-resolution aerial photography to map the distribution of these reefs, and conducted a one-year field survey to investigate settlement and growth parameters, including abundance, biomass and body length, using artificial settlement panels. We correlated our findings with physicochemical measurements, specifically focusing on temperature, salinity and dissolved oxygen. Salinity exhibited marked and rapid fluctuations, ranging from less than 2 to over 42 Practical Salinity Units (PSU). These variations did not affect the adult population, but settlement only occurred at salinities above approximately 10 PSU and only when water temperatures surpassed 18–20 °C. Settlement was reduced when temperatures were high (> 28 °C) but was not affected by high salinity. The maximum abundance measured was 880 000 individuals m−2, and reefs attained a weight of 18 kg m−2 in 11 months. This study contributes further to understanding the species’ environmental tolerances and life cycle, which in turn may help to design future management measures.

Agricultural land abandonment and its impact on soil erosion in the Madi Watershed, Gandaki Province, Nepal

Agricultural land abandonment is the crucial issue of land use /land cover change in many parts of the world and it is directly related to soil erosion in the mountain areas. Nepal has also faced heavy agricultural land abandonment from the mountain regions in the last two decades. This study is concerned with agricultural land abandonment and its impact on soil erosion in the Madi Watershed, Gandaki Province of Nepal. This study used high-resolution aerial photography and Google Earth images to map cultivated land and its abandonment in 1995 and 2020. The Revised Universal Soil Loss Equation (RUSLE) model has been used to estimate soil erosion. Studies revealed that more than 40% of the agricultural land was abandoned from 1995 to 2020 and there is higher spatial variability of abandonment intensity. High altitude and steeper slope areas have a higher intensity of abandonment than in lower altitude and lower slope gradients. There is a significant impact of abandonment of agricultural land on reducing soil erosion from 1995 to 2020. The average rate of soil erosion was quite high in 1995 but decreased to 51.5 percent in 2020. There are higher rates of reduction in soil erosion at higher altitudes and marginal lands like steeper slopes and the areas having lower solar radiation because of the higher proportion of agricultural land abandonment. Thus, it can be concluded that agricultural land abandonment during the last 25 years significantly impacts soil erosion control in the study area.

A project-based view of urban dynamics: Analyzing ‘leapfrogging’ and fringe development in Israel

Analyzing urban pattern dynamics based on construction projects, we classify them into three types - infilling, fringe, and leapfrogging, and focus on the role of leapfrogging projects as seeds for new developments, leading to uncontrolled urban sprawl. To study the leapfrogging phenomenon, we investigate the sprawl of three Israeli cities - Netanya, Haifa, and Safed over 54 years from 1964 to 2018 and conduct a country-wide analysis of the urban sprawl of all 66 Israeli municipalities between 2013 and 2018. Our analysis is based on a country-wide GIS database of roads, buildings, other infrastructure elements, and development plans, as well as high-resolution aerial photos covering the investigated areas and periods. We uncover and characterize a positive feedback mechanism of rapid leapfrogging developments that attract further developments in their proximity and emphasize the potential of leapfrogging development to force divergence from statutory development plans.

LR Aerial Photo Categorization by Cross-Resolution Perceptual Knowledge Propagation.

There are hundreds of high-and low-altitude earth observation satellites that asynchronously capture massive-scale aerial photographs every day. Generally, high-altitude satellites take low-resolution (LR) aerial pictures, each covering a considerably large area. In contrast, low-altitude satellites capture high-resolution (HR) aerial photos, each depicting a relatively small area. Accurately discovering the semantics of LR aerial photos is an indispensable technique in computer vision. Nevertheless, it is also a challenging task due to: 1) the difficulty to characterize human hierarchical visual perception and 2) the intolerable human resources to label sufficient training data. To handle these problems, a novel cross-resolution perceptual knowledge propagation (CPKP) framework is proposed, focusing on adapting the visual perceptual experiences deeply learned from HR aerial photos to categorize LR ones. Specifically, by mimicking the human vision system, a novel low-rank model is designed to decompose each LR aerial photo into multiple visually/semantically salient foreground regions coupled with the background nonsalient regions. This model can: 1) produce a gaze-shifting path (GSP) simulating human gaze behavior and 2) engineer the deep feature for each GSP. Afterward, a kernel-induced feature selection (FS) algorithm is formulated to obtain a succinct set of deep GSP features discriminative across LR and HR aerial photos. Based on the selected features, the labels from LR and HR aerial photos are collaboratively utilized to train a linear classifier for categorizing LR ones. It is worth emphasizing that, such a CPKP mechanism can effectively optimize the linear classifier training, as labels of HR aerial photos are acquired more conveniently in practice. Comprehensive visualization results and comparative study have validated the superiority of our approach.

Manifold-Regularized Feature Selector for High-Resolution Aerial Photographs Categorization

Manifold-Regularized Feature Selector for High-Resolution Aerial Photographs Categorization

20th-Century hurricanes leave long-lasting legacies on tropical forest height and the abundance of a dominant wind-resistant palm.

Projected increases in hurricane intensity under a warming climate will have profound effects on many forest ecosystems. One key challenge is to disentangle the effects of wind damage from the myriad factors that influence forest structure and species distributions over large spatial scales. Here, we employ a novel machine learning framework with high-resolution aerial photos, and LiDAR collected over 115 km2 of El Yunque National Forest in Puerto Rico to examine the effects of topographic exposure to two hurricanes, Hugo (1989) and Georges (1998), and several landscape-scale environmental factors on the current forest height and abundance of a dominant, wind-resistant species, the palm Prestoea acuminata var. montana. Model predictions show that the average density of the palm was 32% greater while the canopy height was 20% shorter in forests exposed to the two storms relative to unexposed areas. Our results demonstrate that hurricanes have lasting effects on forest canopy height and composition, suggesting the expected increase in hurricane severity with a warming climate will alter coastal forests in the North Atlantic.

Characterizing post-fire delayed tree mortality with remote sensing: sizing up the elephant in the room

BackgroundDespite recent advances in understanding the drivers of tree-level delayed mortality, we lack a method for mapping delayed mortality at landscape and regional scales. Consequently, the extent, magnitude, and effects of delayed mortality on post-fire landscape patterns of burn severity are unknown. We introduce a remote sensing approach for mapping delayed mortality based on post-fire decline in the normalized burn ratio (NBR). NBR decline is defined as the change in NBR between the first post-fire measurement and the minimum NBR value up to 5 years post-fire for each pixel. We validate the method with high-resolution aerial photography from six wildfires in California, Oregon, and Washington, USA, and then compare the extent, magnitude, and effects of delayed mortality on landscape patterns of burn severity among fires and forest types.ResultsNBR decline was significantly correlated with post-fire canopy mortality (r2 = 0.50) and predicted the presence of delayed mortality with 83% accuracy based on a threshold of 105 NBR decline. Plots with NBR decline greater than 105 were 23 times more likely to experience delayed mortality than those below the threshold (p < 0.001). Delayed mortality occurred across 6–38% of fire perimeters not affected by stand-replacing fire, generally affecting more areas in cold (22–41%) and wet (30%) forest types than in dry (1.7–19%) types. The total area initially mapped as unburned/very low-severity declined an average of 38.1% and generally persisted in smaller, more fragmented patches when considering delayed mortality. The total area initially mapped as high-severity increased an average of 16.2% and shifted towards larger, more contiguous patches.ConclusionsDifferences between 1- and 5-year post-fire burn severity maps depict dynamic post-fire mosaics resulting from delayed mortality, with variability among fires reflecting a range of potential drivers. We demonstrate that tree-level delayed mortality scales up to alter higher-level landscape patterns of burn severity with important implications for forest resilience and a range of fire-driven ecological outcomes. Our method can complement existing tree-level studies on drivers of delayed mortality, refine mapping of fire refugia, inform estimates of habitat and carbon losses, and provide a more comprehensive assessment of landscape and regional scale fire effects and trends.

Dynamics of subsurface soil erosion in a semiarid region: A time-series study of sinkhole area and morphology

Subsurface soil erosion causes irreversible damage to agricultural fields by generating elongated pipes and elliptical sinkholes that can further develop into rills and gullies. However, despite their severe consequences, there is still limited available quantitative information on spatiotemporal dynamics of soil pipes and sinkholes, especially in semiarid regions; evidence of subsurface erosion rates is based on a small number of time points during a sampling period that is usually limited to a few successive years. Here, temporal changes in sinkhole area, and their level of circularity and orientation were quantified over a decade (2010–2020), in loess-derived soils in the semiarid Negev desert, Israel. Data were measured using high-resolution aerial photographs (1 pixel = 15 cm length), and were related to high-temporal-resolution rainfall data. The results revealed that: (1) soil pipe collapse is related to early winter rainfall events more than to mid or end-of-winter events, possibly due to the development of annual herbaceous vegetation that shields the soil and increases its resistance to erosion; (2) sinkhole and soil pipe morphological development and orientation are related to the orientation of stream banks and the tension cracks developed along those banks; and (3) changes in sinkhole area are related to catastrophic rainfall events and not to gradually accumulated annual rainfall.

Cascading hazards from two recent glacial lake outburst floods in the Nyainqêntanglha range, Tibetan Plateau

Glacial lake outburst floods (GLOFs) are a major natural hazard in high mountain regions and have caused significant loss and damage. In the past decade, two lakes (Ranzeria Co in 2013 and Jiweng Co in 2020) burst out within a single river basin in the Nyainqêntanglha range of the Tibetan Plateau. These GLOFs destroyed local infrastructure and vegetation, and eroded river banks. In this study, field measurements, satellite remote sensing, and hydrodynamic modelling were used to reveal the causes, mechanisms, and flow dynamics of both events. Field surveys in 2020 and 2022 including lake bathymetry, lake shorelines, high-resolution aerial photographs, geomorphological data, and downstream damage provided evidence to analyze the triggers and mechanisms involved in both cases. The results show that Ranzeria Co expanded by 123% in area between 1976 and 2012, and contracted by 53% in 2013 due to the outburst. The area of Jiweng Co grew by 54% from 1976 to 2020, and shrank by 57% after the dam breach. Lake levels decreased by 20.2 ± 0.3 m in 2013 for Ranzeria Co and by 25.8 ± 0.3 m in 2020 for Jiweng Co, corresponding to water volume losses of about 7.9 × 106 (68%) and 1.1 × 107 m3 (87%), respectively. Using the hydrodynamic model HEC-RAS, it was found that the GLOFs reached peak discharges of about 7,500 m3/s with an average velocity of 8.8 m/s at Ranzeria Co, and 5,100 m3/s with 3.7 m/s at Jiweng Co. The Ranzeria Co outburst involved a cascade of processes, including the collapse of the parent glacier, a displacement wave in the lake, and the overtopping of the moraine dam. The processes of the Jiweng Co outburst flood were more complex, including an unstable lateral moraine, landslides entering the lake, rising water level, dam erosion, and finally dam failure. The study provides important insights into the mechanisms of two GLOFs, which are key to assessing and managing the hazards of critical glacial lakes in the Tibetan Plateau and its surroundings.

Effects of sea-level rise on blue carbon stocks of mangrove ecosystems: insights from Pohnpei Island, Federated States of Micronesia

Carbon storage processes in mangrove ecosystems are summarized and future research directions are discussed based on findings from our long-term monitoring studies on Pohnpei Island in the Federated States of Micronesia. On Pohnpei, where coral reef-type mangrove forests dominate, Rhizophora communities maintain their habitat by accumulating mangrove peat at over 5 mm year-1 in response to rapid sea-level rise, but surface erosion is progressing in communities where the tree density of Rhizophora spp. has declined through succession. However, high-resolution aerial photographs taken by drones have identified trees with reduced vigor even in Rhizophora forests, and if sea-level rise occurs at a rate close to the IPCC's maximum prediction, then Rhizophora forests, which are valuable carbon storage sites due to mangrove peat accumulation, are likely to disappear. The impact of relative sea-level rise is determined by the sum of the rate of ground-level change by the external sediment budget and the rate of ground-level rise with mangrove peat accumulation. In the future, each region will need to conduct its own quantitative evaluation.

Estimation of the Forest Stand Biomass and Greenhouse Gas Emissions Using Lidar Surveys

At the research points, the relationship between the Normalized Difference Vegetation and Normalized Green Red Difference indices is characterized by a determination coefficient equal to 0.52. The estimation of the emission of carbon dioxide and nitrogen oxide in the forest air at an altitude of 40 m above the level of the soil cover during the growing season showed differences in their values during the daytime and at night. The results helped determine promising methods of inventory of the carbon landfill forest area for aboveground woody biomass assessment based on data obtained from several sources and land forest estimation research. The research involved: 1) integration of an unmanned aerial vehicle -based digital camera and lidar sensors in order to optimize the efficiency and cost of data collection; 2) taking advantage of high-resolution aerial photographs and sparse lidar point clouds using an information fusion approach and the ability to compensate for their shortcomings.

Accelerating the discovery of new Nasca geoglyphs using deep learning

We discuss an archaeological research of employing deep learning (DL) based object detection on high-resolution aerial photographs to discover Nasca geoglyphs, which have been designated as a UNESCO World Heritage Site. Owing to extremely limited archaeological ground truth data and their differences in scale and design, it is difficult to detect new geoglyphs merely training DL on the known geoglyphs. Therefore, we developed a pipeline of DL to mine such data and address the challenges unique to archaeology. With this approach, we identified four new geoglyphs in the northern area of the Nasca Pampa, namely: a humanoid, a pair of legs, a fish, and a bird. The geoglyphs got verified through on-site surveys. We could identify new geoglyph's candidates approximately 21 times faster than with the naked eye alone. The approach would be beneficial for the future of archaeology in a new paradigm of combining field survey and AI.

The Prospect of drone applications

As drones technology matures, it will be used in more and more applications in multiple fields. This paper focuses on the evaluation of the prospects for the application of drones. The evaluation is based on three criteria: the environment, engineering, and disaster management. Through analysis, this paper shows that drones have unique advantages in these three areas. Given the use of ground-based sensors for air and water quality testing, drones have a great positive effect on environmental management. It can also be used for large-scale engineering project management and disaster tracking with its HD video and numerous high-resolution aerial photos. However, drones need improved regulatory controls to address risks to safety, privacy, and security. Overall, this article describes the status of drone applications, which will become more popular with the enhancement of the regulatory environment.

Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data

Urban forests are highly heterogeneous; information about the combined effect of forest classification scale and algorithm selection on the estimation accuracy for urban forests remains unclear. In this study, we chose Chongming eco-island in the mega-city of Shanghai, a national experimental carbon neutral construction plot in China, as the study object. Remote sensing estimation models (simple regression models vs. machine learning models) of forest carbon density were constructed across different classification scales (all forests, different forest types, and dominant tree species) based on high-resolution aerial photographs and Sentinel-2A remote sensing images, and a large number of field surveys and optimal models were screened by ten-fold cross-validation. The results showed that (1) in early 2020, the total forest area and carbon storage of Chongming eco-island were 307.8 km2 and 573,123.6 t, respectively, among which the areal ratios and total carbon storage ratios of evergreen broad-leaved forest, deciduous broad-leaved forest, and warm coniferous forest were 51.4% and 53.3%, 33.5% and 32.8%, and 15.1% and 13.9%, respectively. (2) The average forest carbon density of Chongming eco-island was 18.6 t/ha, among which no differences were detected among the three forest types (i.e., 17.2–19.2 t/ha), opposite to what was observed among the dominant tree species (i.e., 14.6–23.7 t/ha). (3) Compared to simple regression models, machine learning models showed an improvement in accuracy performance across all three classification scales, with average rRMSE and rBias values decreasing by 29.4% and 53.1%, respectively; compared to the all-forests classification scale, the average rRMSE and rBias across the algorithms decreased by 25.0% and 45.2% at the forest-type classification scale and by 28.6% and 44.3% at the tree species classification scale, respectively. We concluded that refining the forest classification, combined with advanced prediction procedures, could improve the accuracy of carbon storage estimates for urban forests.

Monitoring the Kotychi Lagoon in Western Peloponnese, Greece, Using Remote Sensing Techniques and Environmental Assessment

The Kotychi Lagoon (western Peloponnese, Greece), together with other coastal wetlands such as Pappas, Prokopos and Lamia, is part of the wider area of the Strofylia Wetlands National Park and Protected Areas of Western Peloponnese, which has been protected by the Ramsar convention since 1971 and classified as a Natura 2000 area due to its high diversity and ecological value. This study aimed to estimate the water surface evolution of the Kotychi Lagoon using optical remote sensing techniques to record parameters and indices related to the geometrical and landscape orientation features for the 1945–2016 period and to evaluate the ecological and environmental status of the lagoon through in-situ measurement and sample analysis. High-resolution aerial photos at a scale of 1:30,000, orthomosaics, and satellite images were used and linear regression rates (LRR) were calculated to determine the changes. Seasonal sampling of water, sediment, and macrofaunal organisms in the lagoon was performed to monitor environmental and ecological parameters. The results showed that the lagoon’s water surface shrank during the 1945–2016 period, showing different rates in four segments. The shrinkage prevailed in the northeastern and southern parts of the lagoon, with rates of 17.75 and 6.46 m/year, respectively. The ecological status was determined to be poor, although nutrient input seemed to have been moderate over the study period. We conclude that applying remote sensing techniques using a combination of high-resolution images constitutes an effective and accurate method for the spatiotemporal mapping of wetlands. The ecological problems of the Kotychi Lagoon were highlighted and confirmed through complementary in-situ analyses. These problems need to be addressed by the respective managerial bodies in order to restore the lagoon to good ecological status.

Application of a Novel Multiscale Global Graph Convolutional Neural Network to Improve the Accuracy of Forest Type Classification Using Aerial Photographs

The accurate classification of forest types is critical for sustainable forest management. In this study, a novel multiscale global graph convolutional neural network (MSG-GCN) was compared with random forest (RF), U-Net, and U-Net++ models in terms of the classification of natural mixed forest (NMX), natural broadleaved forest (NBL), and conifer plantation (CP) using very high-resolution aerial photographs from the University of Tokyo Chiba Forest in central Japan. Our MSG-GCN architecture is novel in the following respects: The convolutional kernel scale of the encoder is unlike those of other models; local attention replaces the conventional U-Net++ skip connection; a multiscale graph convolutional neural block is embedded into the end layer of the encoder module; and various decoding layers are spliced to preserve high- and low-level feature information and to improve the decision capacity for boundary cells. The MSG-GCN achieved higher classification accuracy than other state-of-the-art (SOTA) methods. The classification accuracy in terms of NMX was lower compared with NBL and CP. The RF method produced severe salt-and-pepper noise. The U-Net and U-Net++ methods frequently produced error patches and the edges between different forest types were rough and blurred. In contrast, the MSG-GCN method had fewer misclassification patches and showed clear edges between different forest types. Most areas misclassified by MSG-GCN were on edges, while misclassification patches were randomly distributed in internal areas for U-Net and U-Net++. We made full use of artificial intelligence and very high-resolution remote sensing data to create accurate maps to aid forest management and facilitate efficient and accurate forest resource inventory taking in Japan.

Multi-hazard susceptibility mapping of cryospheric hazards in a high-Arctic environment: Svalbard Archipelago

Abstract. The Svalbard Archipelago represents the northernmost place on Earth where cryospheric hazards, such as thaw slumps (TSs) and thermo-erosion gullies (TEGs) could take place and rapidly develop under the influence of climatic variations. Svalbard permafrost is specifically sensitive to rapidly occurring warming, and therefore, a deeper understanding of TSs and TEGs is necessary to understand and foresee the dynamics behind local cryospheric hazards' occurrences and their global implications. We present the latest update of two polygonal inventories where the extent of TSs and TEGs is recorded across Nordenskiöld Land (Svalbard Archipelago), over a surface of approximately 4000 km2. This area was chosen because it represents the most concentrated ice-free area of the Svalbard Archipelago and, at the same time, where most of the current human settlements are concentrated. The inventories were created through the visual interpretation of high-resolution aerial photographs as part of our ongoing effort toward creating a pan-Arctic repository of TSs and TEGs. Overall, we mapped 562 TSs and 908 TEGs, from which we separately generated two susceptibility maps using a generalised additive model (GAM) approach, under the assumption that TSs and TEGs manifest across Nordenskiöld Land, according to a Bernoulli probability distribution. Once the modelling results were validated, the two susceptibility patterns were combined into the first multi-hazard cryospheric susceptibility map of the area. The two inventories are available at https://doi.org/10.1594/PANGAEA.945348 (Nicu et al., 2022a) and https://doi.org/10.1594/PANGAEA.945395 (Nicu et al., 2022b).