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History of remote sensing studies of the Moon and asteroids changed when lunar samples were returned by the Apollo 11 mission and many meteorites were discovered on Antarctica starting in 1969. Discovery of the isotopic similarity between lunar and terrestrial materials led us to the giantimpact model to form the Moon. In addition, the existence and nature of space weathering were also discovered in 1993 by analyzing the Apollo samples. Another change occurred in 2010 when the Hayabusa spacecraft returned particles of asteroid Itokawa that proved the identity between many S-type asteroids and ordinary chondrites and the existence of space weathering similar to the Moon. The second sample return from asteroids occurred in 2020 when the Hayabusa2 spacecraft returned samples of Ctype asteroid Ryugu. In spite of some expectations, it was a pristine CI1 chondrite material that was free from terrestrial contaminations suffered by known CI1 chondrite meteorites. Sample return missions drastically improved the accuracy of our knowledge on the raw materials of solar system planets and will surely keep revealing the secrets behind the birth of this special planet Earth. This part of history also teaches us that scientists should proclaim the truth against denial or persecution by others.

지난 10년간 우리나라에서 발생한 자연재해의 88%는 호우와 태풍에 의해 발생했으며 정량적 피해 를 산출하기 어려운 가뭄피해 또한 광역적으로 발생하고 있다. 본 연구에서는 한반도를 포함한 동아 시아 지역에 홍수, 가뭄 등의 수재해 정보를 모니터링하고 예측하기 위한 수재해 정보 플랫폼과 홍수/가뭄 모니터링 시스템을 개발하였다. 수재해 정보 플랫폼은 인공위성, 레이더, 지상관측 자료를 활용하여 홍수와 가뭄 등의 수재해를 모니터링 할 수 있는 시스템을 갖추고 있다. 본 플랫폼에는 피해규모가 상대적으로 큰 도시지역을 대상으로 고해상도 레이더 기반 홍수 모니터링 시스템이 운 영되고 있으며 위성기반의 임진강 접경지역의 홍수 모니터링 시스템과 한반도를 포함한 광역적 가 뭄 감시 및 예측 시스템이 운영되고 있다. 수재해 정보 플랫폼의 홍수/가뭄 모니터링 시스템을 통해 국민의 생명과 재산을 보호하고 홍수, 가뭄 등 수재해의 선제적 대응이 가능한 해결책이 되길 기대 한다.

88% of the natural disasters in South Korea over the past ten years were caused by heavy rains and typhoons. In addition, drought damage, which is challenging to estimate quantitative damage, occurs in a wide area. In this study, a water hazard information platform was developed to monitor and predict water hazard information such as drought and flood in a wide area of East Asia, including the Korean Peninsula. The water hazard information platform is equipped with a system that can monitor water disasters such as drought and floods by using satellite, radar, automatic weather station data. This platform has an X-band radar system for predicting and monitoring urban floods with a significant damage scale. The Imjin River flood prediction system in the border area is operating for flood monitoring. The drought monitoring and forecasting system is operating using satellite-based in a wide area. Through the water hazard information platform, it is expected to protect the lives and property of the people and as a critical solution for a preemptive response to water hazards such as floods and droughts.

Malaysia possesses about 1.56 million ha of Peat Swamp Forest (PSF). The PSF safeguard enormous biological diversity, while providing crucial benefits for the sustainable development of human communities. Numbers of threatened plant species are associated with the PSF, including the commercially important Gonystylus bancanus timber. To prevent significant losses of biodiversity, it is important to manage the PSF for both biological conservation and sustainable use. Equally important is to restore all degraded PSF in an attempt to ensure the PSF ecosystem is suitable for the vegetation to grow and rehabilitate back to the normal condition. Prior to plan any forest restoration program, there is a need to properly map the degraded PSF in order to estimate the forest conditions and determine the vegetations status. Most of the time this need to be done at a landscape level and requires a technology that can provide accurate, timely and reliable information for the planner to make decision. This paper describes a study using geospatial technology in combination with ground survey to classify the degraded PSF in South East Pahang Peat Swamp Forest (SEPPSF), Malaysia, into different degree of vegetation classes. With map accuracy of about 83%, the technique proved to be useful in delineating the different degree of PSF degradation from which the information can be used to properly plan forest restoration program in the area. The final output which is in the form of map can be used in developing a Restoration Master Plan for the degraded PSF areas.

This study was conducted to compare the intensity of forest fire damage based on the distance from the center of forest roads in five regions where forest roads were established within areas affected by forest fires in 2023. The Difference Normalized Burn Ratio (dNBR) was utilized to quantify the intensity of forest fire damage. The results showed that the intensity of forest fire damage increased with distance from the center of the forest road, and a statistically significant difference was observed in the average dNBR values when compared to the overall fireaffected area (p<0.001). Additionally, differences were also found in the classification of burn severity levels. Regarding burn severity classifications, the proportions of ‘Unburned’ and ‘Low severity’ areas decreased as the distance from the forest road center increased, while the proportions of ‘Moderate high severity’ and ‘High severity’ areas increased. Overall, the intensity of forest fire damage in the periphery of forest roads was lower compared to the broader fire-affected regions, as indicated by average dNBR values and burn severity levels. However, a direct causal relationship between the presence of forest roads and the reduction in forest fire damage intensity in their periphery was not established. Further investigations, including satellite imagery analysis and on-site verification, are deemed necessary to better elucidate the distribution and intensity of forest fire damage in these areas.

One of the areas most at risk of forest fires in Turkey is the Mediterranean region. Although the reasons for fire outbreaks change over the years, they result in the loss of forest assets in the area due to fire. Fires also cause major ecological damage. Forest fires reduce or eliminate many forms of vegetation from the land surface. The Kavaklıdere-Muğla-Yatağan-Yılanlı fire, which occurred between 2 and 8 August 2021, damaged a large area. Therefore, the study aims to investigate the land use and land cover (LULC) changes in 2020, 2021 and 2024 in Kavaklıdere district, Muğla. In this study, six LULC classes, agriculture (A), bare + other (BO), forest (F), urban (U), water (W) and burned areas (BA), were obtained using Sentinel-2 satellite imagery with the random forest classification technique on the Google Earth Engine (GEE) platform. In addition, land surface temperature (LST) data were obtained using the splitwindow algorithm on Landsat-8 data. The results show that LULC changes are clearly in fireaffected areas and that LST values are particularly high in burned areas compared to other classes. The results demonstrate the effectiveness of remote sensing techniques in monitoring changes in land cover and surface temperature following fire.

Although mechanical harvesting equipment used in forestry operations increases productivity, it also causes significant physical impacts on the soil. Among the most prominent of these effects are road deformations and wheel rut depths caused by tractors and similar vehicles. Such deformations can disrupt the physical structure of the soil, increase the risk of erosion, and damage forest ecosystems. In this study, wheel rut depths formed during ground-based skidding operations were comparatively analyzed using both manual measurement techniques and imagery obtained via Unmanned Aerial Vehicles (UAVs). The fieldwork was conducted during forestry production activities in the Dağtekke Forest Management Chief. Manual measurements were performed using a ruler and lath along skidding trails, and the same areas were captured using UAVs and converted into digital surface models. The results obtained from both methods were compared using correlation analysis and paired sample t-tests. The findings revealed a high level of consistency between UAV-based and manual measurements, indicating that UAV technology offers a fast, contactless, and reliable alternative for monitoring. This study provides valuable insights into precision soil monitoring and assessing the environmental impacts of mechanical equipment in forestry operations.

In heterogeneous landscapes, high-resolution land cover classification is vital for planning, ecological monitoring, and green infrastructure management. This study evaluates the performance of five machine learning algorithms: Decision Tree (DT), Naïve Bayes, Support Vector Machine (SVM), Random Tree (RT), and K-Nearest Neighbors (KNN), using UAV multispectral imagery and object-based image analysis (OBIA). Five scenarios were designed to compare algorithm accuracy. Additionally, a sixth scenario applied the best-performing algorithm to a feature subset selected through Recursive Feature Elimination (RFE), to examine the effect of feature optimization. RT achieved the highest overall classification accuracy (76.75%) and Kappa coefficient (0.7066), while SVM showed limited performance in complex environments. Height features contributed most to accuracy improvements, followed by spectral and geometric features. In class-specific analysis, the Naïve Bayes algorithm yielded the highest Producer’s Accuracy (90.86%) for forest-type land cover but had a lower User’s Accuracy (70.41%), indicating overclassification. In contrast, RT showed more balanced performance (PA = 87.09%, UA = 85.71%), suggesting greater reliability. The results demonstrate the benefits of integrating algorithm selection with feature optimization to improve classification accuracy in complex settings. This approach provides methodological insights for fine-scale mapping of vegetated areas and supports future applications in landscape monitoring and urban green space assessment.

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approximately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

Fire, being primarily a natural phenomenon, is impossible to control, although it is feasible to map the forest fire risk zone, minimizing the frequency of fires. The spread of a fire starting in any stand in a forest can be predicted, given the burning conditions. The natural cover of the land and the safety of the population may be threatened by the spread of forest fires; thus, the prevention of fire damage requires early discovery. Satellite data and geographic information system (GIS) can be used effectively to combine different forest-fire-causing factors for mapping the forest fire risk zone. This study mainly focuses on mapping forest fire risk in the Madikhola watershed. The primary causes of forest fires appear to be human negligence, uncontrolled fire in nearby forests and agricultural regions, and fire for pastoral purposes which were used to evaluate and assign risk values to the mapping process. The majority of fires, according to MODIS events, occurred from December to April, with March recording the highest occurrences. The Risk Zonation Map, which was prepared using LULC, Forest Type, Slope, Aspect, Elevation, Road Proximity, and Proximity to Water Bodies, showed that a High Fire Risk Zone comprised 29% of the Total Watershed Area, followed by a Moderate Risk Zone, covering 37% of the total area. The derived map products are helpful to local forest managers to minimize fire risks within the forests and take proper responses when fires break out. This study further recommends including the fuel factor and other fire-contributing factors to derive a higher resolution of the fire risk map.

Rapid urbanization and population growth have caused substantial land use land cover (LULC) change in the Kathmandu valley. The lack of temporal and geographical data regarding LULC in the middle mountain region like Kathmandu has been challenging to assess the changes that have occurred. The purpose of this study is to investigate the changes in LULC in Chandragiri Municipality between 1996 and 2017 using geographical information system (GIS) and remote sensing. Using Landsat imageries of 1996 and 2017, this study analyzed the LULC change over 21 years. The images were classified using the Maximum Likelihood classification method and post classified using the change detection technique in GIS. The result shows that severe land cover changes have occurred in the Forest (11.63%), Built-up areas (3.68%), Agriculture (-11.26%), Shrubland (-0.15%), and Bareland (-3.91%) in the region from 1996 to 2017. This paper highlights the use of GIS and remote sensing in understanding the changes in LULC in the south-west part of Kathmandu valley.

Satchari National Park is one of the most biodiverse forest in Bangladesh and home of many endangered flora and fauna. 206 tons of CO2 per hectare is sequestrated in this national park every year which helps to mitigate climate issues. As people living near the area are dependent on this forest, degradation has become a regular phenomenon destroying the forest biodiversity by altering its forest cover. So, it is important to map land cover quickly and accurately for the sustainable management of Satchari National Park. The main objective of this study was to obtain information on land cover change using remote sensing data. Combination of unsupervised NDVI classification and supervised classification using maximum likelihood is followed in this study to find out land cover map. The analysis showed that the land cover is gradually converting from one land use type to another. Dense forest becoming degraded forest or bare land. Although it was slowed down by the establishment of ‘National Park’ on the study site, forecasting shows that it is not enough to mitigate forest degradation. Legal steps and proper management strategies should be taken to mitigate causes of degradation such as illegal felling.

This study was performed to construct tree species classification map according to three information types (spectral information, texture information, and spectral and texture information) by altitude (30 m, 60 m, 90 m) using the unmanned aerial vehicle images and the object-based classification method, and to evaluate the concordance rate through field survey data. The object-based, optimal weighted values by altitude were 176 for 30 m images, 111 for 60 m images, and 108 for 90 m images in the case of Scale while 0.4/0.6, 0.5/0.5, in the case of the shape/color and compactness/smoothness respectively regardless of the altitude. The overall accuracy according to the type of information by altitude, the information on spectral and texture information was about 88% in the case of 30 m and the spectral information was about 98% and about 86% in the case of 60 m and 90 m respectively showing the highest rates. The concordance rate with the field survey data per tree species was the highest with about 92% in the case of Pinus densiflora at 30 m, about 100% in the case of Prunus sargentii Rehder tree at 60 m, and about 89% in the case of Robinia pseudoacacia L. at 90 m.

This study aimed to predict changes in forest area using a probability density function, in order to promote effective forest management in the area north of the civilian control line (known as the Minbuk area) in Korea. Time series analysis (2010 and 2016) of forest area using land cover maps and accessibility expressed by distance covariates (distance from buildings, roads, and civilian control line) was applied to a probability density function. In order to estimate the probability density function, mean and variance were calculated using three methods: area weight (AW), area rate weight (ARW), and sample area change rate weight (SRW). Forest area increases in regions with lower accessibility (i.e., greater distance) from buildings and roads, but no relationship with accessibility from the civilian control line was found. Estimation of forest area change using different distance covariates shows that SRW using distance from buildings provides the most accurate estimation, with around 0.98-fold difference from actual forest area change, and performs well in a Chi-Square test. Furthermore, estimation of forest area until 2028 using SRW and distance from buildings most closely replicates patterns of actual forest area changes, suggesting that estimation of future change could be possible using this method. The method allows investigation of the current status of land cover in the Minbuk area, as well as predictions of future changes in forest area that could be utilized in forest management planning and policymaking in the northern area.

Sarawak is the largest state in Malaysia that covers 37.5% of the total land area. Multitemporal satellite images of Landsat and SPOT were used to examine deforestation and forest fragmentation in Sarawak between 1990 and 2009. Supervised classification with maximum likelihood classifier was used to classify the land cover types in Sarawak. The overall accuracies of all classifications were more than 80%. Our results showed that forests were reduced at 0.62% annually during the two decades. The peat swamp forest suffered a tremendous loss of almost 50% between 1990 and 2009 especially at coastal divisions due to intensified oil palm plantation development. Fragmentation analysis revealed the loss of about 65% of the core area of intact forest during the change period. The core area of peat swamp forest had almost completely disappeared during the two decades.

Remote sensing image fusion plays a crucial role in enhancing image information. However, the limitations of existing fusion technologies in terms of computational resources and storage capacity make real-time processing difficult. Therefore, a lightweight fusion method based on knowledge distillation is proposed for vehicle remote sensing image fusion. The knowledge distillation technology is used to transfer the complex teacher model knowledge to the lightweight student model, which realizes the significant reduction of model complexity while maintaining high fusion accuracy. Experimental results show that the proposed method performs well on DroneVehicle dataset and the model weight is only 0.641M. 2025 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

We analyze a remote sensing system in the Internet of things, where uncoordinated nodes send status updates to a common receiver to achieve information freshness, quantified through age of information. We consider a finite horizon scheduling over a random multiple access channel, where colliding messages are lost. We show that nodes must adopt a further randomization to deviate from identical schedules and escape collision deadlocks. Moreover, we discuss the impact of feedback availability if, due to, e.g., energy expenditure, it decreases the number of transmission opportunities.

Hyperspectral unmixing (HU) is crucial for extracting material information from hyperspectral images (HSI) obtained through remote sensing. Although linear unmixing methods are widely used due to their simplicity, they only address linear mixing effects. Nonlinear mixing models, while more complex, often focus solely on the nonlinear aspects affecting individual pixels. However, in practice, light reflected from materials within a pixel experiences linear and nonlinear interactions, necessitating a hybrid mixing model (HMM) that leverages spatial and spectral information. This work proposes a novel deep learning-based autoencoder (AE) with dual-stream decoders to enhance spectral unmixing. Our approach employs multitask learning (MTL) to process spatial and spectral information concurrently. Specifically, one decoder stream performs linear unmixing from HSI patches, while the other stream utilizes fully connected layers to capture and model the nonlinear interactions within the data. By integrating linear and nonlinear information, our method improves the accuracy of unmixing the mixed spectrum. We validate the effectiveness of our architecture on three real-world HSI datasets and compare its performance against various baseline methods. Experimental results consistently demonstrate that our approach outperforms existing methods, as evidenced by superior spectral angle distance (SAD) and mean squared error (MSE) metrics.

Mechanized timber harvesting operations often cause soil disturbance, such as compaction and rutting. The extent of soil disturbance is highly dependent on environmental factors and operation methods. As soil disturbance has a long-term impact on forest productivity, it is critical to assess its effects. The objective of this study was to investigate soil surface deformation caused by forest machinery traffic in a steep slope clear-cut area and to compare manual methods with remote sensing. Following the timber harvesting operation design, we established the experimental treatments representing the number and direction of forest machinery passes (1D, 1-downward; 1U, 1-upward; 3R, 3-round-trip; 5R, 5-round-trip). Soil rut depth and cross-section were manually measured using pinboard and estimated by mobile LiDAR system (MLS) and unmanned aerial vehicle structure from motion algorithm (UAV SfM). There was a significant difference in soil rut depth based on the number of passes (p = 0.00), while no significant difference based on the direction of passes. Rut depth in 1D (22.2cm) was significantly higher than 3R (15.7cm), with no significant differences among 1D, 1U (20.0cm), and 5R (19.6cm) or among 3R, 1U, and 5R. These findings suggest that most soil disturbance occurs during the initial passes of forest machinery. The comparison of pinboard and MLS data revealed a significant relationship (R2 = 0.74, slope = 1.00, p = 0.00). Comparing pinboard and UAV SfM data, we found that MLS is more accurate than UAV SfM in assessing soil surface deformation (R2 = 0.60, slope = 0.81, p = 0.00). The results reveal the need to establish optimized driving routes for forest machinery to minimize soil disturbance and suggest the potential of using MLS and UAV SfM for future soil disturbance assessments.