Earth Images Research Articles

Design and on-orbit performance evaluation of Ethiopian earth observation satellite multispectral optical imaging payload

The Ethiopian Earth observation micro-satellite was successfully launched into a sun-synchronous orbit equipped with an optical multispectral imaging payload system. This optical multispectral imaging payload camera weighs 1.4 kg with four spectral bands: RGB and NIR, each having a unique spectral range. Furthermore, it has a field of view of 7.4°×0.3°, a relative F-number of 9.8°, and a spectral range of 0.45∼0.9μm. The present article covers the design and on-orbit performance analysis of the ETRSS-1 electro-optical imaging payload system, which is based on the COTS compact and unobscured off-axis three-mirror anastigmatic system for high-resolution imaging. Additionally, selecting an appropriate imaging sensor, optomechanical architecture, and performance characteristics for optical instruments is investigated to evaluate spatial, spectral, and radiometric requirements and the payload’s on-orbit performance and capability.

Biodiversity of coral reefs and associated seagrass beds in Quy Nhon Bay, Binh Dinh Province

The distribution of critical marine habitats in Quy Nhon Bay, Binh Dinh Province, was assessed with the combination of resolution of Google Earth images and local knowledge through local consultations and ground truthing using small boats to check the sites supporting coral reefs and seagrass beds with local involvement. Detailed surveys on species diversity and conditions of marine habitats were conducted at 24 sites of coral reefs and four sites of seagrass beds at three sites (Nhon Ly, Nhon Hai, Ghenh Rang, and Nhon Chau) in 2016–2017. More than 152 ha of coral reefs, 5 ha of seagrass beds, and 720 species belonging to 7 key groups (290 species of fishes; reef-building corals: 194, molluscs: 123, seaweeds: 47, echinoderms: 40, crustaceans: 20 and seagrasses: 6 species) associated with the above habitats were recorded in the Bay. Among them, 92 species were facing different threats, in which 3 species were endangered (EN), including branching corals (Alveopora minuta), flat pen shells (Atrina vexillum), and top shells (Tectus pyramis); 38 species at vulnerable (VU); and 51 species at near threatened (NT) following the IUCN Red List and Vietnam Red Book. In general, the coral reefs and seagrass beds at Bai Dua and Seo Island (Nhon Ly); Kho Nho Island, Bo Dap (Nhon Hai); S. Ngang Island, Bai Nam and Bai Nom of Dat Island (Ghenh Rang); Da Ban, Bai Lang, Bai Lang Ba and Ong Gia Island (Nhon Chau) supported higher diversity and better conditions than other sites. Establishing restricted zones at these sites is an urgent need, but it also holds the premise of sustainable utilization and conservation of resources in the future.

Assessing Geospatial Accuracy in Mapping Applications: A Focus on Google Earth

Google Earth, among other online mapping platforms, offers an interactive mapping platform that has become indispensable for academic and research applications. It serves as a primary reference and a foundational tool for map creation, providing open-source, cost-free imagery that meets the user needs of the mapping community. As a contemporary repository of high-resolution images of Earth's landmass, Google Earth has vast potential for scientific exploration and remains an underexploited resource. Its rapid expansion and consistent reliability make it a favored source for mapping and routing tasks. However, this research underscores the crucial aspect of Google Earth's positional accuracy, which is at the heart of this study. A comparative analysis between the positional accuracy of Google Earth and traditional ground surveying maps was conducted. The Wilcoxon rank test and quantitative methods were used to evaluate coordinate discrepancies, revealing significant discrepancies between the two datasets. This study aims to provide a rigorous assessment of Google Earth's utility and accuracy in scientific and academic contexts, emphasizing its role and reliability as a critical resource for researchers and practitioners in the field of mapping. The results revealed displacement changes in both the northing and the easting coordinates. For the northing coordinates, the displacement increases when moving eastward and decreases when moving westward. For the easting coordinates, the displacement increases when moving northward and decreases when moving southward. This pattern highlights spatial discrepancies and the varying impact of location on the dataset's accuracy, emphasizing the need for targeted corrections to enhance data accuracy. These key findings provide valuable insights that could significantly contribute to optimizing mapping practices and efficiently exploiting this vast, yet underexplored, digital resource. Doi: 10.28991/CEJ-2024-010-08-012 Full Text: PDF

On detailed representation of flood defences and flow-wave coupling in coastal flood modelling

Coastal flooding is projected to become more severe over the 21st century, necessitating effective adaptation, which in turn requires detailed local scale information that can only be provided by detailed numerical modelling. The current lack of information on flood protection measures and the high resource requirements of traditional hydrodynamic models presents concurrent challenges for detailed coastal flood modelling. But how comprehensive do the representation of coastal flood defences and hydrodynamic forcing need to be for adequately accurate modelling of coastal flooding? Here, we attempt to answer this question through strategic numerical simulations of the flooding that occurred at Île de Ré (France) during the Xynthia storm (2010), using the flexible mesh model Delft3D FM, with an over-land grid resolution of ~10 m. The model is validated against the flood extents observed in Île de Ré during Xynthia. We use three levels of detail in flood defence representation: a 5 m resolution DEM (i.e. base case DEM), the same 5 m DEM augmented with defences extracted from a 1 m DEM and Google Earth images (i.e. moderately augmented DEM), and the moderately augmented DEM further augmented with in-situ measurements of flood defences (i.e. highly augmented DEM). Simulations with these three DEMs are performed with and without flow-wave coupling (thus, 6 simulations in total), and results are analysed in terms of four flood indicators: maximum flood depths, flood extents, flood current velocities and flood damages. Our analysis indicates that both detailed representation of flood defences and the inclusion of waves have substantial effects on coastal flood modelling at local scale, with the former having a more pronounced effect. The return on the investment in implementing highly detailed in-situ measurements to represent flood defences appears to be low in this case, and adequately accurate results are obtained with a moderately augmented DEM. The combined effect of using the moderately augmented DEM together with waves, relative to using the base case DEM without waves, is to decrease maximum flood depths (up to 2 m), flood extent (by ~10%), maximum current velocities (in ~50% flooded area) and total flood damage (by ~27% or ~€ 188 million).

Kajian Ruang Terbuka Biru Guna Menunjang Optimasi Ecosytem Services (Lokasi Kota Bandar Lampung)

One of the methods to maintain the balance of the perkotaan environment is to maintain its blue open space. There have been a lot of research on Green Open Space, but not on Blue Open Space (BOS). However, BOS also has a very important function and moreover, the ecosystem services provided by BOS can give positive implications. This research is aims to identify the existing BOS area in Bandar Lampung City. The data used in this research are Sentinel-2 satellite images for the extraction of BOS using AWEI on the Google Earth Engine platform, administrative data for the study area, field survey data for validation of the extraction results, Google Earth images for the delineation of existing BOS objects, and river stream data for the river objects of BOS. The methods used for obtaining BOS objects are machine learning-based supervised classification on Google Earth Engine, delineation, buffering, and overlay. The results of the identification of existing RTB in Bandar Lampung city are 206,01 ha consisting of embung/reservoirs, lakes, and rivers.

Adapting LoRa Ground Stations for Low-latency Imaging and Inference from LoRa-enabled CubeSats

Recent years have seen the rapid deployment of low-cost CubeSats in low-Earth orbit, many of which experience significant latency (several hours) from the time information is gathered to the time it is communicated to the ground. This is primarily due to the limited availability of ground infrastructure that is bulky to deploy and expensive to rent. This article explores the opportunity in leveraging the extensive terrestrial LoRa infrastructure as a solution. However, the limited bandwidth and large amount of Doppler on CubeSats precludes these LoRa links to communicate rich satellite Earth images—instead, the CubeSats can at best send short messages. This article details our experience in designing LoRa-based satellite ground infrastructure that requires software-only modifications to receive packets from LoRa-enabled CubeSats recently launched by our team. We present Vista, a communication system that adapts encoding onboard the CubeSat and decoding configuration on commercial LoRa ground stations to allow images to be communicated. We perform a detailed evaluation of Vista by leveraging wireless channel measurements from a recent CubeSat (2021), and show that Vista can achieve 55.55% lower latency in retrieving data with 12.02 dB improvement in packet retrieval in the presence of terrestrial interference. We then evaluate Vista on a case study on land-use classification over images transmitted over the CubeSat link to further demonstrate a 4.56 dB improvement in image PSNR and 1.38× increase in classification accuracy over baseline approaches.

AnimalEnvNet: A Deep Reinforcement Learning Method for Constructing Animal Agents Using Multimodal Data Fusion

Simulating animal movement has long been a central focus of study in the area of wildlife behaviour studies. Conventional modelling methods have difficulties in accurately representing changes over time and space in the data, and they generally do not effectively use telemetry data. Thus, this paper introduces a new and innovative deep reinforcement learning technique known as AnimalEnvNet. This approach combines historical trajectory data and remote sensing images to create an animal agent using deep reinforcement learning techniques. It overcomes the constraints of conventional modelling approaches. We selected pandas as the subject of our research and carried out research using GPS trajectory data, Google Earth images, and Sentinel-2A remote sensing images. The experimental findings indicate that AnimalEnvNet reaches convergence during supervised learning training, attaining a minimal mean absolute error (MAE) of 28.4 m in single-step prediction when compared to actual trajectories. During reinforcement learning training, the agent has the capability to replicate animal locomotion for a maximum of 12 iterations, while maintaining an error margin of 1000 m. This offers a novel approach and viewpoint for mimicking animal behaviour.

Mapping rice-crayfish co-culture (RCC) fields with Sentinel-1 and -2 time series in China's primary crayfish production region Jianghan Plain

Crayfish is a high-risk invasive species with devastating impacts on freshwater ecosystems. Meanwhile, nicknamed “little lobster”, it is a popular food in many countries including China. The crayfish production in China increased from 1.13 to 2.39 million tons in 2017–2020, accounting for 97% global production. This phenomenal increase is attributed to the expansion of the rice-crayfish co-culture (RCC) farming mode whose area increased by 123% from 0.57 to 1.26 million ha in 2017–2020. However, the fast expansion of RCC is undertaken in an uncontrolled and unregulated manner, referred by some researchers as a “blind expansion”. It raises wide concerns on ecological risks (crayfish can escape in high-magnitude floods), endangerment of riverbanks (crayfish burrows), food security (reduced rice production), excessive water consumption, and greenhouse gas (methane) emission. It is thus urgent to accurately map the spatial distributions of RCC fields using satellite remote sensing data, so as to assess the ecological and environmental impacts and risks, and to better regulate the expansion. However, there are currently no practically-scalable approaches to reliably map RCC fields in large areas. In particular, there lack the knowledge on the relationship between satellite observations and on-ground biophysical processes in RCC fields. In this study, we conducted field surveys in RCC fields, and in particular, the daily water levels in RCC fields were measured for the complete year of 2020. The comparison of annual water-level time series and satellite-NDVI time series, combined with the RCC farming information collected in surveys, reveals how satellite observations vary in correspondences to on-ground biophysical processes in RCC fields; and importantly, it provides information on how RCC fields can be efficiently distinguished from other land covers using satellite data. Based on that, we propose an approach to map RCC fields from annual Sentinel-2 optical-wavelength and Sentinel-1 Synthetic Aperture Radar (SAR) time series, utilizing the annual water-occurrence frequency (AWF) and characteristic phenological features derived from the satellite data. This method was demonstrated in Jianghan Plain, the primary crayfish production region in China with an area of approximately 37,000 km2. A total of 273,365 ha (2733.65 km2) RCC field area in year 2020 was mapped, which accounted for 24.6% of the whole plain's cropland area (approximately 11,100 km2), meaning a significant proportion of the rice paddies were converted to RCC fields. The RCC mapping accuracies were validated using the samples collected in field surveys and also from Google Earth images, and was compared with the state-of-practice RCC mapping method using bi-seasonal optical-wavelength satellite images. The proposed method obtained 93.8% overall accuracy and 0.91 kappa coefficient, and outperformed the compared bi-seasonal method. The proposed method is scalable for large-area and multi-annual applications. Future research regarding improvements and applications is recommended.

LONEStar: The Lunar Flashlight Optical Navigation Experiment

This paper documents the results from the highly successful Lunar flashlight Optical Navigation Experiment with a Star tracker (LONEStar). Launched in December 2022, Lunar Flashlight (LF) was a NASA-funded technology demonstration mission. After a propulsion system anomaly prevented capture in lunar orbit, LF was ejected from the Earth-Moon system and into heliocentric space. NASA subsequently transferred ownership of LF to Georgia Tech to conduct an unfunded extended mission to demonstrate further advanced technology objectives, including LONEStar. From August to December 2023, the LONEStar team performed on-orbit calibration of the optical instrument and a number of different OPNAV experiments. This campaign included the processing of nearly 400 images of star fields, Earth and Moon, and four other planets (Mercury, Mars, Jupiter, and Saturn). LONEStar provided the first on-orbit demonstrations of heliocentric navigation using only optical observations of planets. Of special note is the successful in-flight demonstration of (1) instantaneous triangulation with simultaneous sightings of two planets with the LOST algorithm and (2) dynamic triangulation with sequential sightings of multiple planets.

Comparing Frequency Ratio and Analytical Hierarchy Models for Landslide Susceptibility in the Dharan Sub-Metropolitan Region of Eastern Nepal

This study compares the application of Frequency Ratio (FR) (statistical/quantitative) and Analytical Hierarchy Process (AHP) (heuristic/semi-quantitative) for landslide susceptibility analysis in Dharan Sub-Metropolitan Region of eastern Nepal. Nine different thematic layers including slope, aspect, curvature, geology, land use, distance to roads, distance to thrusts, topographic position index (TPI), and distance to streams were used for analysis. A landslide inventory map was prepared by identifying the landslides in recent Google Earth images, and it was then verified by field observations. The resulting susceptibility map with different susceptibility levels was validated using the Area Under the Curve (AUC) method, and the resulting AUC values were determined to be 78% for FR and 76.8% for AHP. Both methods offer a reliable strategy for landslide susceptibility mapping with a good prediction rate. The FR model enclosed 41%, 31%, 20%, 7% and 1% of the area as very low, low, moderate, high, and very high susceptibility, respectively, whereas AHP showed 21%, 24%, 27%, 21% and 7% of the area for the respective susceptibility levels. The specific zones identified within the range from very low to very high susceptibility provide valuable insights for local authorities, planners, and decision-makers, allowing them to identify areas susceptible to landslides and implement mitigation measures to aid in targeted risk management efforts.

Landslide susceptibility assessment in Addi Arkay, Ethiopia using GIS, remote sensing, and AHP

Landslides account for the breakdown of natural topographies, impacting many mountainous areas and leading to loss of lives and damaged infrastructure. This research aims to generate a reliable landslide susceptibility zonation map employing geospatial and Analytical Hierarchy Processes (AHP) in Addi Arkay Woreda, North Gondar Zone, Amhara Regional State, northern Ethiopia. The present study uses remote sensing data, geographic information system (GIS) tools, AHP, and weighted linear combination (WLC) models to analyze multiple environmental variables, including slope, aspect, curvature, lithology, soil texture, topographic wetness index (TWI), and rainfall. As per the results, around 186.12 km2 (13.26%) of the total study area is under very high landslide susceptibility and 140.85 km2 (10.05%) under very low susceptibility. Using Google Earth images for inaccessible areas, 121 landslide inventories were identified through fieldwork. Of these inventories, 85 were used to train the model and 36 for testing. The performance of the AHP model was validated by the Receiver Operating Characteristics (ROC) curve (0.75), which indicates good predictive accuracy for identifying landslide-prone areas. These findings are essential to regional land use planning, hazard mitigation, and landslide prevention efforts. Additionally, this study contributes to the scientific understanding of landslide dynamics in the Northwestern highlands of Ethiopia and offers a methodological framework that can be applied to other regions with similar geological and climatic conditions.

Marine debris characteristics in various coastal typologies in the Gunungkidul coastal area of Yogyakarta—Indonesia

Marine debris is one of the environmental issues that can be brought on by rapid human development and activity, including marine tourism, like what happened in the coastal area of Gunungkidul, Indonesia. The issue of marine debris might decrease the environmental quality and impact tourism in Gunungkidul. This study aims to identify the characteristics of coastal typologies in Gunungkidul and analyze their influence on the characteristics of marine debris. Google Earth images and the Digital Elevation Model were used to interpret and classify the typology across the study area based on their physical characteristics. Marine debris samples, either macro or meso-sized, were collected using the transect method for each beach. Based on the imagery interpretation combined with elevation data from DEMNAS, there are three typologies in the Gunungkidul coastal area, i.e., pocket, non-pocket, and cliff beaches. Cliff typology was eliminated and only pocket and non-pocket beaches were used as marine debris sample collection locations. Hence, four beaches were chosen as study areas, i.e., Seruni, Drini, Sepanjang, and Sedahan; each represents each coastal typology. About 193 macro-sized and 217 meso-sized debris were found in our research area, totaling 1380.31 g. Compared to non-pocket beaches, pocket beaches contain more marine debris. The higher slopes of the pocket beach typology will trap marine debris and make it challenging to reenter the water. On pocket beaches, oceanographic processes concentrate marine debris in one location. Still, on non-pocket beaches, marine debris can disperse and return to the sea more readily due to the cycling of currents near the coast.

Design of an Airborne Low-Light Imaging System Based on Multichannel Optical Butting

For the purpose of achieving long-range, high-resolution, and ultra-wide-swath airborne earth imaging at extremely low-light levels (0.01 Lux), a low-light imaging system built on multi-detector optical butting was researched. Having decomposed the system’s specifications and verified its low-light imaging capability, we proposed to employ an optical system with a large relative aperture and low distortion and achieve imaging through the field-of-view (FOV) butting facilitated by eight 1080P high-sensitivity scientific complementary metal-oxide semiconductor (SCMOS) detectors. This paper elaborates on the design concept of the mechanical configuration of the imaging system; studies the calculation method of the structural parameters of the reflection prism; provides mathematical expressions for geometric parameters, such as the length and width of the splicing prism; and designs in detail the splicing structure of six reflection prisms for eight-channel beam splitting. Based on the design and computational results, a high-resolution, wide-swath imaging system for an ambient illuminance of 0.01 Lux was developed. Exhibiting a ground sampling distance (GSD) of 0.5 m (at a flight height of 5 km), this low-light imaging system keeps the FOV overlap ratio between adjacent detectors below 3% and boasts an effective image resolution of 4222 × 3782. The results from flight testing revealed that the proposed imaging system is capable of generating wide-swath, high-contrast resolution imagery under airborne and low-light conditions. As such, the way the system is prepared can serve as a reference point for the development of airborne low-light imaging devices.

Appraisal of Active Tectonics: An Insight from the Morphotectonic Study of Drainage Basins and OSL Dating in the Kangra Area, Himachal Pradesh

ABSTRACT A morphotectonic investigation of the drainage basins and an estimation of the rate of upliftment have been conducted in the vicinity of Palampur, located in the Kangra district of Himachal Pradesh. The assessment utilized the geomorphic index of active tectonics (IAT) and the Optically Stimulated Luminescence (OSL) dating technique. The study area is situated within the epicentral zone of the 1905 Kangra earthquake and is drained by three significant perennial tributaries of the Beas river, namely Baner, Neogal, and Awa. To evaluate the tectonic influence on these drainage basins, Digital Elevation Model (DEM) data with a resolution of 30m, Survey of India toposheets, and Google Earth images were processed using ArcGIS and Global Mapper software. The findings of this investigation indicate that the drainage basins have been impacted by tectonic activity, resulting in the formation of asymmetrical, elongated basins with deep V-shaped valleys and active mountain fronts. The hypsometric integral values derived from the study suggest that these basins have reached an equilibrium stage. Originating from elevations exceeding 4500m in the Dhauladhar range, these rivers, like other Himalayan rivers, partake in active downcutting and erosional processes, as observed in the presence of terraces. By employing the OSL dating technique, the terraces of the Neogal river were dated, enabling the calculation of an upliftment rate ranging from 0.6 ± 0.03 mm/year to 1.7 ± 0.1 mm/year for the area.

Geospatial Assessment of Impacts of Sand Mining Activities in Zanzibar

This study assessed the impacts of sand mining activities in Zanzibar by using geospatial technology, focusing on three villages in the North 'B' district of Unguja: Donge-Muwanda, Mchangani, and Misufini. Geospatial technology was employed, utilizing two distinct datasets to analyse the spatial extent of environmental degradation due to sand mining. The first dataset comprised of geographic coordinates of sampled mining sites, acquired through field survey by using the Global Positioning System (GPS). The second dataset involved satellite imagery obtained from the Google Earth application. These datasets were utilized to find the area, volume, and depth of mining sites and create three-dimensional (3D) models for each sand mining site by using ArcMap software and Surfer 19 software. The Google Earth images from 2020, 2021, 2022, and 2023 were utilized to determine changes in vegetation cover before and after the mining activities in the study area. The key finding of the study indicates that there is a high extent of environmental degradation caused by intensive sand mining activities, impacting both the community and environment in surrounding sand mining areas. The study suggests that the results should be used to emphasize the importance of existing policies, guidelines, and laws related to sand mining.

Geospatial assessment of the efficiency of waste disposal facilities in Lugbe, FCT

Solid waste consists of substances or objects that are disposed of and are mostly found where there are human activities, these substances are accumulations of waste materials generated from domestic, commercial, and industrial operations that are deemed unworthy or rejected and required to be disposed of. The study aims to assess the efficiency of waste disposal facilities in Lugbe FCT, with specific objectives to; evaluate the spatial distribution of waste collection sites (dumpsites) in the area, evaluate the proximity of dumpsites (waste collection sites) to the residential areas, and evaluate the level of appropriate use of waste bins in the study area. Both primary and secondary data were used for this study. The primary data used are the X and Y coordinates of dumpsite locations and questionnaire administration while the secondary data includes; the Google Earth image. Nearest neighbour analysis was performed on the dumpsite's coordinate locations to affirm its spatial distribution pattern in the study area. Buffering analysis was performed on the dumpsite locations with 300m standard to identify the proximity of dumpsites to buildings while the descriptive statistical method was used to analyze the retrieved administered questionnaires to assess the level of the appropriate use of waste bins in the study area. The result of the dispersed spatial distribution of the dumpsites and the result of the proximity analysis performed on dumpsites to the residential area revealed that buildings were found around almost all the dumpsites except the Park Estate dumpsite where no building is found around the dumpsite. At the level of waste bin appropriateness, all the waste bin types were adopted but 72% of waste bin types are appropriate.

Application of optical images and analytical hierarchy process model for solid waste dumping site analysis in Kombolcha town, Northeastern, Ethiopia

Improper open dumping of solid wastes caused different serious problems in fast-growing towns in Ethiopia like Kombolcha town. This unsuitable solid waste dumping management brings health challenges by spreading diseases, contaminating water, polluting air, and spreading of different disease-causing insects such as mosquitoes. Therefore, selecting suitable solid waste dumping site analysis (SWDSA) is very essential to minimize these negative effects related to improper solid waste dumping in such towns. The primary goal of this study is to use optical images and the AHP model to find appropriate solid waste dumping sites in Kombolcha Town. The nine governing factors such as soil texture, geology, faults, groundwater well points (GWWPs, rivers, roads, built-up, Land use land cover (LULC) and slopes, were considered to achieve the aim of this study. These factors were extracted and delineated from the various optical data such as Landsat 8 images, digital elevation model, Google Earth images, geological map, soil map, and also field survey. The factor classes and factors were ranked and weighted utilizing a comparison matrix according to importance and finally by applying the AHP spatial analysis extension tool in Arc GIS software the SWDSA map of the study area was generated. Therefore, as a result of this study about 19.4 km2 (22.8 %), 22.8 km2 (26.8 %), 20.5 km2 (24.1 %), 16.1 km2 (18.9 %), and 6.2 km2 (7.3 %) of the study area falls in not suitable, less suitable, moderately suitable, suitable and highly suitable ranges respectively. Based on the result of this further analysis, D is the 1st suitable site with a score is 83.6 %, site B is the 2nd suitable site is a score is 83.2 %, site C is the 3rd suitable site with a score of 65.1 % and site A is the last suitable site and scored 60.4 %. Therefore, this study strongly recommends that the waste management municipal office of Kombolcha town and other concerned government and non-government bodies apply and consider these identified the best suitable solid waste dumping sites.

GIS-based statistical analysis for landslide susceptibility evaluation and zonation mapping: A case from Blue Nile Gorge, Gohatsion-Dejen road corridor, Central Ethiopia

Globally, landslides in mountainous regions are considered to be major geo-hazards which are frequent, intensive and devastating that result in extensive destruction to people's lives, land, property and other infrastructure. To mitigate landslide-associated risks, identification, evaluation, and delineation of susceptible areas is essential for proper strategic planning and management. The present study was conducted in Blue Nile Gorge in the highlands of Central Ethiopia, about 185 km from Addis-Ababa, along the Gohatsion-Dejen road corridor. Landslides are frequently reported in the study area, particularly during the rainy season. The objective of the present study was to evaluate landslide susceptibility and to prepare a landslide susceptibility zoning (LSZ) map. For this purpose a statistical information value (IV) model was used that took into account various factors including slope material, elevation, slope, aspect, land-use and land-cover, proximity to the road, and proximity to streams. Each of these factors was classified based on different conditions like landslide concentration, topography, geology, and land-use and land-cover types. The landslide inventory mapping was conducted through field observations and Google Earth image interpretation. Later, the influence of each causative factor on past landslides was used to calculate their respective IVs. The statistical IVs were further used to evaluate the landslide susceptibility. The LSZ map produced from this study shows that 22.3 % of the study area falls into a very high susceptibility zone, 23.1 % into a high, 18.8 % in moderate, 23.1 % into a low, and the remaining 12.8 % into a low susceptibility zone. The LSZ map was validated using the Receiver Operating Characteristics Curve (ROC), landslide density index (LDI), and past landslide data. All validation methods showed acceptable results. The prepared LSZ map can be used for safe land-use planning of the area.

Assessing Many Image Processing Products Retrieved from Sentinel-2 Data to Monitor Shallow Landslides in Agricultural Environments

Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (OBARPs) are often comparable to those of the pixels surrounding them (SBARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of OBARPs and SBARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments.

Assessing Industrial Past and Cultural Changes in Industrial Lands along the Hangzhou Section of the Grand Canal in China

The Grand Canal has been fundamental in supporting Chinese industrialization, as evidenced in the great changes witnessed across industrial lands, but few studies have approached this issue from a cultural perspective. After an extensive industrial recession in the early 21st century, canal cities like Hangzhou have seen changes in the cultural functions of industrial lands, relocating, transforming, and demolishing factories and other constructions to develop new cultural spaces and products. A deep analysis of these cultural changes is, therefore, quite helpful to appreciate the features of and reasons for Chinese national policy setting in the cultural sections of the Grand Canal, such as the UNESCO World Heritage-recognized purpose of the Grand Canal, etc. Therefore, by focusing on the Hangzhou section of the Canal, and via the integration of high-resolution Google Earth images with a 1.5 spatial resolution, we dynamically monitored the land use of the 10 specifically selected industrial lands and interpretated the various uses of the areas from 2000 to 2020. Then, with archival data from 1950 to 2024 and fieldwork conducted over 1 year, and in consultation with major experts, we designed a mark sheet with factors and scores to comprehensively assess cultural changes in those lands. Consequently, the history of industrialization along the Hangzhou section was thoroughly analyzed with these investigations and data, and the reasons for and influence of cultural changes were presented in detail; subsequently, the outstanding problems were addressed, and suggestions were offered. This study contributes to a better understanding of Chinese cultural policy regarding cities along the Canal after the industrial decline, especially the critical implication of cultural loss within these cities. In addition, it also provides novel perspectives for cultural research, heritage conservation, and urban planning of canals or waterfront spaces for world heritage.