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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. [ABSTRACT FROM AUTHOR]

At 12:52 pm on September 5, 2022, an Ms 6.8 earthquake occurred in Luding County, Sichuan Province, China. Based on high-resolution aerial photographs and satellite imageries obtained after the earthquake, as well as field investigation, a total of 8685 earthquake-triggered landslides (EQTLs) were interpreted. The landslides covered an area of 30.7 km2, with a source area of 9.4 km2. These EQTLs were mainly distributed in areas with a seismic intensity of VIII and IX. Most of the landslides were small and medium in size, and their types included landslide, rockfall, and rock slump. Characteristic landslide distributions were found, EQTLs were distributed along the Xianshuihe fault, landslide area decreased gradually with an increased distance to the fault; EQTLs were distributed along the Daduhe River and roads; besides, landslide distribution was associated with ground deformation caused by the earthquake. EQTLs' characteristics indicated that, a large number of EQTLs were located near the foot of the slope; the full area of EQTLs and their source area followed a power function. This study concluded that Luding EQTLs were greater in number and area but relatively smaller in terms of affected area. Investigations on geo-hazards post-earthquake and risk assessment were proposed in the earthquake-stricken area to support the rehabilitation and reconstruction. [ABSTRACT FROM AUTHOR]

The aim of this study is to assess and compare two clustering algorithms, namely, the Wave-cluster and Density Based Spatial Clustering of Application with Noise (DBSCAN) algorithms, for landslide susceptibility assessment in Baota District, China. Based on historical reports, interpretation of aerial photographs and field survey reports, 293 were identified in the study area. Seven factors associated with landslide occurrence were prepared and used as inputs into the models for landslide susceptibility modelling. The models divided the study area grids into 478 and 465 subclasses, respectively, which were further grouped into five susceptibility levels using K-means algorithm, based on the landslide density values of the obtained subclasses. The resulting two models were validated and compared using area under the receiver operating characteristics (AUC-ROC) curve and statistical measures including sensitivity, specificity and accuracy. The analysis demonstrated that the Wave-cluster model outperformed the DBSCAN model with sensitivity, specificity and accuracy values of 88.05%, 89.2%, and 88.54%, respectively, while DBSCAN had sensitivity, specificity and accuracy values of 86.69%, 86.38%, and 86.56% respectively. In addition, the Wave-cluster model had higher value AUC of 0.823 than the DBSCAN model (0.808). This indicates that, the landslide susceptibility maps (LSMs) generated in this study had good performance for assessing landslide susceptibility in the study area and can be useful tools in assessing landslides, as the foundation for preventing and reducing landslide consequences for suitable social and economic development. [ABSTRACT FROM AUTHOR]

Mantled karst is characterized by solution processes in underground conditions, where karst evidence can be masked. In mantled karst environments, cover-collapse sinkhole can be considered one of the most hazardous elements. This study seeks to develop a methodological framework utilizing different techniques and approaches to understand cover-collapse sinkhole genesis and its likely evolution. The study area is located in the Conghua district of Guangzhou city, Guangdong Province, in the south-eastern region of China. A mapping procedure was introduced to combine data from aerial photographs and intensive field investigations. In addition, data interpretations from borehole drilling activities and different geophysical approaches were performed to reconstruct the Quaternary deposit features, rock head morphology, and karst features. During these investigations, the detailed typology, morphometry, and chronology inventory of 49 cover-collapse sinkholes were analysed, and three karst fissure zones covered by Quaternary soil were observed. In the study, the hydrogeological data suggested that karst aquifer pumping triggered the drop in groundwater levels. Cover-collapse sinkholes might be ascribed to erosion of the soil layers due to the groundwater level decline. [ABSTRACT FROM AUTHOR]

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir (TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree (GBDT), random forest (RF) and information value (InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area, 28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic (ROC) curves, the sensitivity, specificity, overall accuracy (OA), and kappa coefficient (KAPPA). The results showed that the GBDT, RF and InV models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR. [ABSTRACT FROM AUTHOR]

A rainfall-induced complex landslide occurred on April 4, 2013, in Wanzhou County, Three Gorges Reservoir, China. Approximately 1.5 × 106 m3 of earth and rock mass slid over a distance of ~ 30 m, destroying residential buildings and rural roads. We perform a comprehensive assessment of the cause of this landslide using field surveys, borehole drillings, and interpretations of aerial photographs. This landslide exhibited a complex failure mode in which the upper part of the earth slope first deformed and slid along geotechnical interfaces. This then triggered the slide of the lower part of the flysch bedding rock. In this study, the landslide mass was divided into nine separate blocks considering their internal geological features and movements. The study also shows that antecedent rainfall, in association with the geological and morphological features of the steep slope, the presence of weak interlayers, and human interventions such as slope excavations, jointly caused this disaster. This well-observed event study facilitates comprehensive recognition of the deformation characteristics and failure mechanisms of rainfall-induced complex landslides and also provides an insight into landslide hazard mitigation. [ABSTRACT FROM AUTHOR]

Data on the distribution and population size of the Near Threatened Tibetan antelope Pantholops hodgsonii are necessary to protect this species. Ground-based count surveys are usually carried out from a long distance to avoid disturbing the sensitive animals, and on calving grounds or along migration routes where they are seasonally concentrated. This can result in underestimation of population sizes if terrain features obstruct the view and high concentrations of animals make estimating numbers difficult. Here we test the efficacy of unmanned aerial vehicles (UAVs) for gathering population data for the Tibetan antelope. We conducted the study south of a known calving ground, at the foot of Sewu Snow Mountain, in the Chang Tang National Nature Reserve, China. The UAV did not appear to disturb the animals and resulted in more accurate counts than ground-based observations. A total of 23,063 Tibetan antelopes were identified in twelve orthoimages derived from c. 4,000 aerial photographs. In the first flight area 7,671 females and 4,353 calves were identified (proportion of calves: 36.2%). In the second flight area 7,989 females and 3,050 calves were identified (proportion of calves: 27.6%). Two flights over the same area revealed the direction and speed of moving Tibetan antelope groups. Image resolution, which can be controlled with flight planning, was an important factor in determining the animals' visibility in the photos. We found that UAV-based surveys outperformed ground-based surveys, and that larger UAVs are preferable for this application. [ABSTRACT FROM AUTHOR]

Despite the publicly accepted concept of 'smart growth' of urban areas and its wide implications in developed countries, there is less concern about this in a newly emerging economy like China. Yet, due to the unprecedented urbanization rates in many developing countries, how to control unordered urban sprawl is becoming a severe challenge to multiple levels of governments. This study is the first to comprehensively evaluate the spatial and temporal change of vacant land within the built‐up area of a large city in China. We used the core city of Shanghai as a case‐study to systematically investigate the spatial–temporal distribution of vacant land at the fine scale. The boundaries of vacant land patches and of other urban land use types were delineated using visual interpretation, based on 0.3‐m resolution aerial photos collected in 2000, 2005, and 2010. We find that (a) vacant land plays a very important role in the composition of the urban landscape of central Shanghai, accounting for 9.3%, 11.3%, and 10.4% of the core city area in 2000, 2005, and 2010, respectively; (b) there exists obvious spatial–temporal change of transformation between vacant land and other land use types during the 10 years studied; and (c) the considerable amounts of vacant land and its change dynamic have important policy implications for smart growth of cities in China. Making the best planning and management decisions about these vacant lands might be one promising smart growth principle for China's cities. [ABSTRACT FROM AUTHOR]

At 5: 39 AM on 24 June 2017, a huge landslide-debris avalanche occurred on Fugui Mountain at Xinmo village, Diexi town, Maoxian county, Sichuan province, China. The debris blocked the Songpinggou River for about 2 km, resulting in a heavy loss of both human lives and properties (10 deaths, 3 injuries, 73 missing, and 103 houses completely destroyed). The objectives of this paper are to understand the overall process and triggering factors of this landslide and to explore the affecting factors for its long term evolution before failure. Post event surveys were carried out the day after the landslide occurrence. Information was gathered from literature and on-site investigation and measurement. Topography, landforms, lithology, geological setting, earthquake history, meteorological and hydrological data of the area were analysed. Aerial photographs and other remote sensing information were used for evaluation and discussion. Eye witnesses also provided a lot of helpful information for us to understand the process of initiation, development and deposition. The depositional characteristics of the moving material as well as the traces of the movement, the structural features of the main scarp and the seismic waves induced by the slide are presented and discussed in detail in this paper. The results show that the mechanism of the landslide is a sudden rupture of the main block caused by the instability of a secondary block at a higher position. After the initiation, the failed rock mass at higher position overloaded the main block at the lower elevation and collapsed in tandem. Fragmentation of the rock mass occurred later, thus forming a debris avalanche with high mobility. This landslide case indicates that such seismic events could influence geological hazards for over 80 years and this study provides reference to the long term susceptibility and risk assessment of secondary geological hazards from earthquake. [ABSTRACT FROM AUTHOR]

Intelligent manufacturing is a major trend in manufacturing innovation around the world, and it is also the main direction and key breakthrough point for the transformation and upgrading of the manufacturing industry for a long time now and in the future. Here, we mainly study the role of intelligent manufacturing in defogging unmanned aerial vehicle (UAV) images and how to analyze UAV image defogging methods based on intelligent manufacturing. In recent years, with the continuous progress of UAV technology, UAV project has also been booming. Aerial photographing is one of the most widely used functions of UAV at present. However, when photographing images in severe weather environment such as haze, it will be affected by the absorption and scattering of light by a variety of different suspended substances in the environment, resulting in poor imaging quality, color distortion, fine pitch blur, and other adverse effects. For this reason, there is a large amount of research on the image haze removal of UAV in China. At present, the mainstream methods are based on non-vision sensor and physical model. However, due to the lack of haze removal effect and severe condition limitation in the practical application of the above methods, we explore an innovative set of automatic estimation haze removal method of atmospheric light based on this method, taking the principle of atmospheric estimation direction as the main research direction, and then through the steps of setting the global transmittance, calculating the atmospheric light amplitude, adjusting the image size, and automatically modifying the image condition threshold value, etc. The man–machine image is haze removal. To compare the advantages and disadvantages of the three methods, we choose standard deviation, information entropy, and objective evaluation method to analyze the results of the three methods. Data analysis shows that among the three haze removal methods, the atmospheric light automatic estimation haze removal method has greatly improved the overall haze removal effect. Compared with the previous two kinds of haze removal imaging in the haze environment, it can better guarantee the color balance degree, and also retain more image information, which makes the imaging clarity have a significant improvement, and the overall effect is more natural. This method has played a very good complementary role to the domestic UAV demisting technology. [ABSTRACT FROM AUTHOR]

The article discusses the damage caused by tornadoes and torrential rainstroms in China, providing images taken with the help of unmanned aerial vehicles (UAVs) and ground damage surveys. The tornadoes discussed include the July 23, 2016, Yancheng EF4 tornado, June 5, 2016, Wenchang tornado, and 2015 Typhoon Mujigae.

The aim of this study was to apply and to verify the use of artificial neural network (ANN) and weight of evidence (WoE) models to landslide susceptibility mapping in the Gongliu county, China, using a geographic information system (GIS). For this aim, in this study, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field surveys. A total of 163 landslides (70% out of 233 detected landslides) were randomly selected for model training, and the remaining 70 landslides (30%) were used for the model validation. Then, a total number of twelve landslide conditioning factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance to rivers, distance to roads, lithology, rainfall, normalized difference vegetation index (NDVI), and sediment transport index (STI), were used in the analysis. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors by ANN and WoE models. Finally the output maps were validated using the area under the curve (AUC) method. The validation results showed that the ANN model with a success rate of 82.51% and predictive accuracy of 77.31% performs better than WoE (success rate, 79.82%; predictive accuracy, 74.59%) model. Overall, both models showed almost similar results. Therefore, the two landslide susceptibility maps obtained were successful and can be useful for preliminary general land use planning and hazard mitigation purpose. [ABSTRACT FROM AUTHOR]

This study investigates the application of information value (InV) and logistic regression (LR) models for producing landslide susceptibility maps (LSMs) of the Zigui--Badong area near the Three Gorges Reservoir in China. This area is subject to anthropogenic influences because the reservoir's water level cyclically fluctuates between 145 and 175 m. In addition, the area suffers from extreme rainfall events due to the local climate and has experienced significant and widespread landslide events in recent years. In this study, a landslide inventory map was initially constructed using field surveys, aerial photographs, and a literature search of historical landslide records. Eight causative factors, including lithology, bedding structure, slope, aspect, elevation, profile curvature, plane curvature, and fractional vegetation cover, were then considered in the generation of LSMs by using the InV and LR models. Finally, the prediction performances of these maps were assessed through receiver operating characteristics (ROC) that utilized both success-rate and prediction-rate curves. The validation results showed that the area under the ROC curve for the InV model was 0.859 for the success-rate curve and 0.865 for prediction-rate curve; these results indicate the InV model surpassed the LR model (0.742 for success-rate curve and 0.740 for prediction-rate curve). Overall, the two models provided nearly similar results. The results of this study show that landslide susceptibility mapping in the Zigui--Badong area is viable with both approaches. [ABSTRACT FROM AUTHOR]

On April 20, 2013, a Mw 6.6 earthquake struck Lushan County of Sichuan Province, China, about 80 km southwest of the epicenter of the 12 May 2008 Wenchuan Mw 7.9 event. It triggered a large number of landslides in a broad area, causing causality and injuries and damages to roads, drains, dwellings, and infrastructures. Field investigations of coseismic landslides provide a basis for better understanding and illustrating the spatial distributions and hazards related to the Lushan earthquake-triggered landslides. Therefore, this paper presents results of field investigations of the Lushan earthquake-triggered landslides, which can be classified into two main categories of disrupted landslides and coherent landslides, and constitutes seven types. The disrupted landslides include five types: rock falls, rock slides, rock avalanches, soil falls, and soil slides; and the coherent landslides have two types: soil slumps and slow earth flows. All of the seven types of landslides can be observed in many field photos, aerial photographs, and/or satellite images. Among them, seven typical large-scale landslides, distributed in a northeast trending line consistent with the Shuangshi-Dachuan fault, which is presumably related with the earthquake, are described in detail. We prepared an original, emergency-based coseismic landslide inventory map based on visual interpretation of available post-earthquake aerial photographs and pre-earthquake satellite images, combined with results from emergency-based field investigations. The original inventory map registered 3884 landslides. Later, 11,761 more landslides were identified by much more careful and time-consuming visual interpretation based on the same aerial photographs and satellite images. Subsequently, all the 15,645 coseismic landslides were correlated with six common landslide impact factors, including elevation, slope angle, slope aspect, topographic position, stratum/lithology, and peak ground accumulation (PGA). These observational and compiled coseismic landslide data allow us to make subsequent compiling of a detailed and completed landslide inventory by visual interpretation of remote sensing images as well as further studies of landslides induced by the Lushan earthquake. [ABSTRACT FROM AUTHOR]

The 20 April 2013 Lushan earthquake with a magnitude of M s 7.0 induced widespread landsliding along the southern segment of the Longmen Shan fault zone. This paper presents a preliminary analysis of the characteristics of these co-seismic landslides triggered in an area of 13,323 km 2 . A total of 3810 individual landslide points were identified through an intensive field investigation and from digital aerial photographs taken after the Lushan earthquake. To facilitate the more detailed analysis on the areal frequency of co-seismic landslides as polygons, we selected an area of 732 km 2 around the epicenter that had the highest density of co-seismic landslides. The sizes of the landslides are, in general, much smaller than those that were triggered by the 2008 Wenchuan earthquake. The most common types of landslides in the study area were rock falls and shallow, disrupted landslides from steep slopes, which typically involved the top few meters of weathered bedrock and colluvium. In this study, the relation between the landslide distribution and five main factors was analyzed: fault proximity, epicenter proximity, peak ground acceleration (PGA), slope angle, and lithology. Our study results show that the landslide concentration tends to decrease with distance from the seismogenic fault, and the highest landslide concentration is located within a distance of 5 km. Regarding the distance to the epicenter, the largest landslide density is found at a distance between 5 and 10 km. The landslide concentration also increases with increasing slope angle until a maximum for the slope class 45°–55°. In addition, results show that slopes consisting of deeply weathered and fractured sandstones and mudstones were the most susceptible to co-seismic landslides. In this study, we found that the location of the hanging wall and the footwall of the fault did not seem to have an important effect on the landslide distribution. This is in contradiction to what has been reported in other earthquake areas such as the Wenchuan area. [ABSTRACT FROM AUTHOR]

After the 20 April 2013 Lushan M 6.6 earthquake occurred, investigation and identification of the seismogenic fault for this event have become a focused and debatable issue. This work prepared an initial landslide inventory map related to the Lushan earthquake based on field investigations and visual interpretation of high-resolution aerial photographs and provided evidence for solving the issue aforementioned. The analysis of three landslide-density profiles perpendicular to strike direction of the probable seismogenic fault shows that many landslides occurred on the footwall of the Shuangshi-Dachuan fault (SDF), without sudden change of landslide density near the fault. Very few landslides were detected near the Dayi fault (DF) and also no change of landslide density there. While obvious sudden change of landslide density appeared about 1-2 km from the northwest to the western Shangli fault (WSF), and the landslide density on the hanging wall of the fault is obviously higher than that of on the footwall. Therefore, we infer that the seismogenic fault for the Lushan earthquake is neither the SDF nor the DF, rather probably the WSF located between these two faults, which is an evident linear trace on the earth surface. Meanwhile, the coseismic slip did not propagate upward to the ground, implying the Lushan earthquake was spawned by a blind-thrust-fault beneath the WSF. [ABSTRACT FROM AUTHOR]

The Shuanghuaishu (SHS) site in China is one of the 100 most important archaeological discoveries over the past 100 years; its historical heritage can be traced directly back 5300 years. Understanding the early landscape of the site would provide important information about the origin of Chinese civilization. The SHS site is buried and surface traces are difficult to see; therefore, we attempted to reconstruct the early landscape of the site based on a current surface landscape model and environmental archaeological analysis. We created a modern three-dimensional (3D) landscape model of the study area from high spatial resolution unmanned aerial vehicle (UAV) aerial photographs and analysed the distance change between the Yellow River and SHS site in the past 60 years from CORONA and Landsat images. By combining environmental archaeological survey results, archaeological excavation data, relevant papers, and field measurements, we reconstructed the paleotopography of the SHS site during the Yangshao period (7000–5000 aBP). On this basis, 3D natural and human landscapes during the Yangshao period were rebuilt. The results show that (1) Satellite images acquired at different resolutions can provide multiscale spatial information about the site, and high-precision models of current conditions can be quickly generated from UAV aerial photography. (2) From 1960 to 2020, the shortest distance between the SHS site and the Yellow River was approximately 512 m. The location of bedrock on Mang Mountain can be used to infer the early extent of the northern terrace at the site. (3) Environmental archaeology provided information about the palaeoenvironment of the site area. By incorporating spatial information technology and 3D visualization, we can better restore the early landscape of the SHS site. Our work integrates environmental archaeology, field archaeology, and spatial technology, enabling data and modelling support for the visual interpretation of the SHS site. [ABSTRACT FROM AUTHOR]

Much of Hong Kong is currently undeveloped and consists of vegetated, relatively steep hillsides, which are considered to be natural terrain. However, in many of these areas there is evidence in old aerial photographs of man-made features, which for the last 30–40 years or so have been hidden by dense vegetation. Before the 1970s the vegetation was considerably less dense than today and much of the ground surface was bare and clearly apparent. Preliminary study of old aerial photographs indicates some 10 types of man-made features, including agricultural, military, mining, prospecting, and other activities. Some are fairly recent, but relatively surprising is the widespread evidence of old to ancient land management practices, particularly on the more remote, elevated steep terrain away from existing villages. These features include stone walls, stone structures and a variety of stone terraces and unsupported earthen step-form terraces, some of which may have their origins with pre-Chinese aboriginal hill tribes. Their distribution, nature and occurrence provide an insight into the history of human settlement in Hong Kong, which is generally unknown within the earth science and engineering communities. These man-made features occur over extensive areas, with many on steep slopes and in natural drainage lines, and with some stone walls and agricultural terraces in excess of 3 m in height. These features should be recognized during natural terrain landslide hazard assessments and other engineering geological and geotechnical studies. [ABSTRACT FROM AUTHOR]

Abstract: Support vector machine (SVM) modeling is based on statistical learning theory. It involves a training phase with associated input and target output values. In recent years, the method has become increasingly popular. The main purpose of this study is to evaluate the mapping power of SVM modeling in earthquake triggered landslide-susceptibility mapping for a section of the Jianjiang River watershed using a Geographic Information System (GIS) software. The river was affected by the Wenchuan earthquake of May 12, 2008. Visual interpretation of colored aerial photographs of 1-m resolution and extensive field surveys provided a detailed landslide inventory map containing 3147 landslides related to the 2008 Wenchuan earthquake. Elevation, slope angle, slope aspect, distance from seismogenic faults, distance from drainages, and lithology were used as the controlling parameters. For modeling, three groups of positive and negative training samples were used in concert with four different kernel functions. Positive training samples include the centroids of 500 large landslides, those of all 3147 landslides, and 5000 randomly selected points in landslide polygons. Negative training samples include 500, 3147, and 5000 randomly selected points on slopes that remained stable during the Wenchuan earthquake. The four kernel functions are linear, polynomial, radial basis, and sigmoid. In total, 12 cases of landslide susceptibility were mapped. Comparative analyses of landslide-susceptibility probability and area relation curves show that both the polynomial and radial basis functions suitably classified the input data as either landslide positive or negative though the radial basis function was more successful. The 12 generated landslide-susceptibility maps were compared with known landslide centroid locations and landslide polygons to verify the success rate and predictive accuracy of each model. The 12 results were further validated using area-under-curve analysis. Group 3 with 5000 randomly selected points on the landslide polygons, and 5000 randomly selected points along stable slopes gave the best results with a success rate of 79.20% and predictive accuracy of 79.13% under the radial basis function. Of all the results, the sigmoid kernel function was the least skillful when used in concert with the centroid data of all 3147 landslides as positive training samples, and the negative training samples of 3147 randomly selected points in regions of stable slope (success rate=54.95%; predictive accuracy=61.85%). This paper also provides suggestions and reference data for selecting appropriate training samples and kernel function types for earthquake triggered landslide-susceptibility mapping using SVM modeling. Predictive landslide-susceptibility maps could be useful in hazard mitigation by helping planners understand the probability of landslides in different regions. [Copyright &y& Elsevier]

The changes in hydraulic geometry of the Hwang River downstream of Hapcheon Dam have been investigated since 1982. The study reach is 45 km long from the Hapcheon Re-regulation Dam to the confluence with the Nakdong River, in South Korea. The geographic information system (GIS) analysis of a time series of aerial photographs taken in 1982, 1993 and 2004 showed that the non-vegetated active channel width decreased an average of 152 m (47% width reduction since 1982). The average median bed material size increased from 1.07 mm in 1983 to 5.72 mm in 2003, and the bed slope of the reach decreased from 94 to 85 cm/km from 1983 to 2003. The analysis of aerial photographs and field surveys shows that the 2004 channel width was asymptotically approaching the estimated equilibrium width from the Julien and Wargadalam (Julien, P.Y. and Wargadalam, J., 1995. Alluvial channel geometry: theory and applications. Journal of Hydraulic Engineering, 121 (4), 312-325) hydraulic geometry equations. An exponential model is proposed and is in good agreement with field measurements. The sediment transport model GSTAR-1D shows that the thalweg elevation will reach equilibrium around 2013-2015. The thalweg elevation simulated for 2013 is expected to remain almost identical to the predictions for 2023. The bed elevation changes should affect primarily the 20 km reach immediately downstream of the re-regulation dam. [ABSTRACT FROM AUTHOR]

In this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30-40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM + ) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30-32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85. [ABSTRACT FROM AUTHOR]

Google Earth uses aerial and satellite images from various sources as its background. It is designed to enable every person who owns a computer to easily 'fly' to aerial views of any location on the planet. However, just as with ordinary aerial or satellite images, there inevitably exist shadows in Google Earth images, making some ground objects obscured, even unidentifiable. In this paper, we describe a model we have developed for image shadow removal that uses radiative transfer theory in combination with prior knowledge to compensate for the lost information over shadow areas. We arbitrarily selected six images with severe shadows from Beijing, Shanghai, Hong Kong, Tokyo, New York and San Francisco, respectively, for algorithm testing. Our experimental results show that shadows in images can be considerably suppressed. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to determine the design typology, locations, and construction date of Gin Drinker's Line's pill-boxes built by the colonial government of Hong Kong, China. It states that the study examines post-war aerial photographs taken by R.C. Huntings and Great Britain Royal Air Force and conducts site visits to measure and locate the pill-boxes while consults relevant archive materials to assist in interpreting the findings. Results show that 50 out of 76 pill-boxes that were mapped have survived. It also mentions the use of aerial photography in conservation planning and historical research.

Oxytropis ochrocephala Bunge is an herbaceous perennial poisonous weed. It severely affects the production of local animal husbandry and ecosystem stability in the source region of Yellow River (SRYR), China. To date, however, the spatiotemporal distribution of O. ochrocephala is still unclear, mainly due to lack of high-precision observation data and effective methods at a regional scale. In this study, an efficient sampling method, based on unmanned aerial vehicle (UAV), was proposed to supply basic sampling data for species distribution models (SDMs, BIOMOD in this study). A total of 3232 aerial photographs were obtained, from 2018 to 2020, in SRYR, and the potential and future distribution of O. ochrocephala were predicted by an ensemble model, consisting of six basic models of BIOMOD. The results showed that: (1) O. ochrocephala mainly distributed in the southwest, middle, and northeast of the SRYR, and the high suitable habitat of O. ochrocephala accounted for 3.19%; (2) annual precipitation and annual mean temperature were the two most important factors that affect the distribution of O. ochrocephala, with a cumulative importance of 60.45%; and (3) the distribution probability of O. ochrocephala tends to increase from now to the 2070s, while spatial distribution ranges will remain in the southwest, middle, and northeast of the SRYR. This study shows that UAVs can potentially be used to obtain the basic data for species distribution modeling; the results are both beneficial to establishing reasonable management practices and animal husbandry in alpine grassland systems. [ABSTRACT FROM AUTHOR]

It is very important to acquire timely information on the spatial distribution of locust populations in order to bring a locust outbreak under control quickly. In this study, we propose a new method for estimating the Oriental migratory locust density using a Locust Density Index (LDI). This index is based on the change in vegetation before an outbreak and shortly after the outbreak, taking into account the pre-outbreak vegetation conditions as well. This method was applied to study three locust-infested sites in Huanghua City, Hebei Province in China during a 2002 outbreak. NDVI images were derived from radiometrically corrected multi-temporal Landsat TM/ETM+ data recorded during and after the outbreak. After radiometry of the post-outbreak NDVI image was standardized to that of its during-outbreak counterpart, their difference multiplied by the pre-outbreak NDVI (termed LDI) was used to map the density of locusts at five levels of no locust, low, moderate, high, and very high. Nearly 90 percent of the study sites had a low to moderate locust density. Results produced with the proposed ml method are similar to those observed on the ground with an accuracy of 88.9 percent. This proposed LDI method is easy to implement. It allows quick and accurate mapping of locust density from satellite data. [ABSTRACT FROM AUTHOR]

Remotely sensed data have been utilized for environmental change study over the past 30 years. Large collections of remote sensing imagery have made it possible for spatio-temporal analyses of the environment and the impact of human activities. This research attempts to develop both conceptual framework and methodological implementation for land cover change detection based on medium and high spatial resolution imagery and temporal trajectory analysis. Multi-temporal and multi-scale remotely sensed data have been integrated from various sources with a monitoring time frame of 30 years, including historical and state-of-the-art high-resolution satellite imagery. Based on this, spatio-temporal patterns of environmental change, which is largely represented by changes in land cover (e.g., vegetation and water), were analysed for the given timeframe. Multi-scale and multi-temporal remotely sensed data, including Landsat MSS, TM, ETM and SPOT HRV, were used to detect changes in land cover in the past 30 years in Tarim River, Xinjiang, China. The study shows that by using the auto-classification approach an overall accuracy of 85-90% with a Kappa coefficient of 0.66-0.78 was achieved for the classification of individual images. The temporal trajectory of land-use change was established and its spatial pattern was analysed to gain a better understanding of the human impact on the fragile ecosystem of China's arid environment. [ABSTRACT FROM AUTHOR]

Very high resolution (VHR) satellite remote sensing systems are now capable of' providing imagery with similar spatial detail to aerial photography, but with superior spectral information. This research investigates the hypothesis that it should be possible to use multispectral IKONOS images to quantify urban vegetation, obtaining similar accuracy to that achieved from false colour aerial photographs. Two parameters. vegetation cover and vegetation density are used to represent biomass in the study area (Kowloon, Hong Kong), for which data is collected for 41 field quadrats. Regression equations relating the field measurements of vegetation density to image wavebands obtained similar high correlations For both image types and lower but significant correlations for vegetation cover. Vegetation density is a quantifiable measure of vegetation in multiple layers above ground. representing the total amount of biomass and is thus well able to indicate the diverse structural types of vegetation found in urban areas. Furthermore it can be accurately measured using the IKONOS green/red ratio (Chlorophyll Index). The superiority of the latter to the more commonly used Normalized Difference Vegetation Index (NDVI), is attributed to the sub- optimal timing of the imagery during the dry season, and its greater sensitivity to multiple layering within the vegetation canopy. A time and cost comparison between the two image types suggests that the use of IKONOS images is much more cost effective than acrial photographs for urban vegetation monitoring. [ABSTRACT FROM AUTHOR]

• We characterized QVP dynamics over 22 years in the Yellow River Delta, China. • QVPs were mapped based on an aerial photograph and satellite remote-sensing images. • Although numbers of QVPs remained virtually unchanged, total area increased. • The area and shape of QVPs could reach maximum extents within about three years. • The location of QVPs had a certain regularity, but the expansion is irregular. Because of a decrease in water provision from the Yellow River and the influence of various human actions, the Shandong Yellow River Delta Wetland, selected a Ramsar Wetland of International Importance in 2013, has undergone substantial degradation. Although some restoration work has been undertaken, more adaptive active restoration strategies should be developed based on researches on spontaneous revegetation in this region. In this regard, the quasi-circular vegetation patches (QVPs), characterized by a rapid rate of succession, are thus considered as ideal research targets. The spatial and temporal quantification of QVPs is not only fundamental to determining QVP formation mechanism but also contributes to the development of adaptive restoration strategies for the degraded ecosystems. Therefore, we examined QVP dynamics over seven time periods spanning 22 years using one aerial photograph and seven high-spatial resolution satellite images. We found that although the total number of QVPs remained almost unchanged, the total area had an increasing trend, and that the shape index ( SI i = Peremeter i 2 π Area i ) of QVPs tended to initially increase and subsequently decrease, with QVPs finally attaining an almost circular shape. Observations for the period between 2000 and 2016 indicated that the area and shape of QVPs could reach maximum extents within about three years. This knowledge could provide a valuable basis for studying the interactions of QVPs with environment, and the implications/consequences to ecosystems, e.g., mechanisms of the evolution of ecosystems, ecological services etc. We accordingly suggest that developing a long-term remote sensing monitoring and ground-based survey program would make a significant contribution to developing a nature-based solution for the degraded ecosystems in the Yellow River Delta, China. [ABSTRACT FROM AUTHOR]

• A multi-scale gully erosion monitoring method was developed. • Long-term gully morphology changes from 1959 to 2018 were monitored. • Impact of land use and topographic factors on gully-head retreat was evaluated. • Topographic thresholds of gully initiation were determined. Gully erosion has been widely regarded as an extreme process of land degradation around the world. Gully development rate and topographic thresholds obtained by measuring the morphological parameters of multiple gullies and their influencing factors are important indicators used to characterize gully activity and potential locations of gully initiation. However, existing gully measurement and observation methods are time-consuming and labor-intensive, and usually only a single or several gullies can be measured at a time. The objectives of this study are to develop a multi-scale gully erosion monitoring method by combining unmanned aerial vehicle (UAV) technology with historical aerial photogrammetry and a field survey, achieve the measurement of gully morphological parameters (length, width, depth, area, width-depth ratio, cross-sectional area and volume) and environmental factors (land use, slope gradient and drainage basin area) for numerous gullies in larger regions, and finally, solve the issue of the accurate calculation of regional topographic thresholds and gully development rates. We applied a multi-scale gully erosion monitoring method in the marginal zone of the Chinese Loess Plateau to investigate the gully changes from 1959 to 2018 and further identified long-term gully development patterns and their main control factors. We identified 46 permanent gullies via a field survey. Long-term developments in 30 permanent gullies were obtained using historical aerial photographs and UAV images. The topographic threshold relationship between the upslope drainage basin area (A) and local slope gradient (S) was determined using the remaining 16 incipient gullies derived from UAV images. The results indicated that the gully dimensions experienced significant expansion; the linear gully-head retreat was considerably stronger than the gully widening and deepening. The contribution of the eroded volume from the gully-head retreat to the total eroded volume was 6.1 times that of the gully sidewall retreat, indicating that gully-head retreat was the primary mechanism for gully development during the study period. The area of the gully-head drainage basin played a more significant role than the slope gradient and land use to explain the gully-head retreat. The S-A topographic threshold relationship was S = 0.0478 A −0.4915, implying that overland flow erosion was the dominant gully erosion process in the study area. Our results provide the gully development rate and topographic threshold relationship in the marginal zone of the Loess Plateau and clarify the potential location of gully development, while proposing that effective reduction of the drainage basin area can aid in curbing gully erosion. This study suggests a multi-scale gully erosion monitoring method for rapidly and precisely obtaining long-term gully development characteristics in various regions. [ABSTRACT FROM AUTHOR]

Highly realistic visualizations of seismic dynamic responses of building clusters are critical for earthquake safety education. To this end, a photo-realistic visualization method of the seismic dynamic responses of urban building clusters is proposed based on oblique aerial photography. Specifically, a sparsification algorithm of aerial photograph footprints and the model optimization solutions are designed to reduce the size of a city model reconstructed by oblique aerial photography. A building segmentation algorithm based on Boolean operations and building footprints is designed to separate buildings from a reconstructed three-dimensional city model. A visualization algorithm for the seismic dynamic responses of building clusters is designed based on the Callback mechanism, by which the shaking process of building clusters can be realistically displayed according to the results of a city-scale nonlinear time-history analysis. New Beichuan City in China is adopted as a case study to visualize seismic dynamic response. The visualization produced by the proposed method is more realistic than that of the finite element method and can support decision making on earthquake safety actions. The outcome of this study provides well-founded and photo-realistic scenes of the seismic dynamic response of building clusters and has promising application prospects for earthquake safety education. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government. [ABSTRACT FROM AUTHOR]