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Modern hydrological modeling frequently incorporates global remote sensing or reanalysis products for multivariate calibration. Although these datasets significantly contribute to model accuracy, the inherent uncertainties in the datasets and multivariate calibration present challenges in the modeling process. To address this issue, this study introduces an adaptive, process-wise fitting framework for the iterative multivariate calibration of hydrological models using global remote sensing and reanalysis products. A distinctive feature is the "kinship" concept, which defines the relationship between model parameters and hydrological processes, highlighting their impacts and connectivity within a directed graph. The framework subsequently develops an enhanced particle swarm optimization (PSO) algorithm for stepwise calibration of hydrological processes. This algorithm introduces a learning rate that reflects the parameter's kinship to the calibrated hydrological process, facilitating efficient exploration in search of suitable parameter values. This approach maximizes the performance of the calibrated process while ensuring a balance with other processes. To ease the impact of inherent uncertainties in the datasets, the Extended Triple Collocation (ETC) method, operating independently of ground truth data, is integrated into the framework to assess the simulation of the calibrated process using remote sensing products with inherent data uncertainty. This proposed approach was implemented with the SWAT model in both arid and humid basins. Five calibration schemes were designed and evaluated through a comprehensive comparison of their performance in three repeated experiments. The results highlight that this approach not only improved the accuracy of ET simulation across sub-basins but also enhanced the precision of streamflow at gauge stations, concurrently reducing parameter uncertainty. This approach significantly advances our understanding of hydrological processes, demonstrating the potential for both theoretical and practical applications in hydrology. [ABSTRACT FROM AUTHOR]

Space-based observations of the total lunar eclipse on 21 January 2019 were conducted using the geostationary Earth-orbiting satellite Gaofen-4 (GF-4). This study represents a pioneering effort to address the observational gap in full-disk lunar eclipse photometry from space. With its high resolution and ability to capture the entire lunar disk, GF-4 enabled both quantitative and qualitative analyses of the variations in lunar brightness, as well as spectra and color changes, across two spatial dimensions, from the whole lunar disk to resolved regions. Our results indicate that before the totality phase of the lunar eclipse, the irradiance of the Moon diminishes to below approximately 0.19% of that of the uneclipsed Moon. Additionally, we observed an increase in lunar brightness at the initial entry into the penumbra. This phenomenon is attributed to the opposition effect, providing scientific evidence for this unexpected behavior. To investigate detailed spectral variations, specific calibration sites, including the Chang'E-3 landing site, MS-2 in Mare Serenitatis, and the Apollo 16 highlands, were analyzed. Notably, the red-to-blue ratio dropped below 1 near the umbra, contradicting the common perception that the Moon appears red during lunar eclipses. The red/blue ratio images reveal that as the Moon enters Earth's umbra, it does not simply turn red; instead, a blue-banded ring appears at the boundary due to ozone absorption and the lunar surface composition. These findings significantly enhance our understanding of atmospheric effects on lunar eclipses and provide crucial reference information for the future modeling of lunar eclipse radiation, promoting the integration of remote sensing science with astronomy. [ABSTRACT FROM AUTHOR]

Snow and ice melting in the Upper Indus Basin (UIB) is crucial for regional water availability for mountainous communities. We analyzed glacier changes in the Astak catchment, UIB, from 2000 to 2020 using remote sensing techniques based on optical satellite images from Landsat and ASTER digital elevation models. We used a surface feature-tracking technique to estimate glacier velocity. To assess the impact of climate variations, we examined temperature and precipitation anomalies using ERA5 Land climate data. Over the past two decades, the Astak catchment experienced a slight decrease in glacier area (−1.8 km2) and the overall specific mass balance was −0.02 ± 0.1 m w.e. a−1. The most negative mass balance of −0.09 ± 0.06 m w.e. a−1 occurred at elevations between 2810 to 3220 m a.s.l., with a lesser rate of −0.015 ± 0.12 m w.e. a−1 above 5500 m a.s.l. This variation in glacier mass balance can be attributed to temperature and precipitation gradients, as well as debris cover. Recent glacier mass loss can be linked to seasonal temperature anomalies at higher elevations during winter and autumn. Given the reliance of mountain populations on glacier melt, seasonal temperature trends can disturb water security and the well-being of dependent communities. [ABSTRACT FROM AUTHOR]

The halfway point for the implementation of the United Nations Sustainable Development Goals (SDGs) was marked in 2023, as set forth in the 2030 Agenda. Geospatial technologies have proven indispensable in assessing and tracking fundamental components of each of the 17 SDGs, including climatological and ecological trends, and changes and humanitarian crises and socio-economic impacts. However, gaps remain in the capacity for geospatial and related digital technologies, like AI, to provide a deeper, more comprehensive understanding of the complex and multi-factorial challenges delineated in the SDGs. Lack of progress toward these goals, and the immense implementation challenges that remain, call for inclusive and holistic approaches, coupled with transformative uses of digital technologies. This paper reviews transdisciplinary, holistic, and participatory approaches to address gaps in ethics and diversity in geospatial and related technologies and to meet the pressing need for bottom-up, community-driven initiatives. Small-scale, community-based initiatives are known to have a systemic and aggregate effect toward macro-economic and global environmental goals. Cybernetic systems thinking approaches are the conceptual framework investigated in this study, as these approaches suggest that a decentralized, polycentric system—for example, each community acting as one node in a larger, global system—has the resilience and capacity to create and sustain positive change, even if it is counter to top-down decisions and mechanisms. Thus, this paper will discuss how holistic systems thinking—societal, political, environmental, and economic choices considered in an interrelated context—may be central to building true resilience to climate change and creating sustainable development pathways. Traditional and Indigenous knowledge (IK) systems around the world hold holistic awareness of human-ecological interactions—practicable, reciprocal relationships developed over time as a cultural approach. This cultural holistic approach is also known as Systemic Literacy, which considers how systems function beyond "mechanical" aspects and include political, philosophical, psychological, emotional, relational, anthropological, and ecological dimensions. When Indigenous-led, these dimensions can be unified into participatory, community-centered conservation practices that support long-term human and environmental well-being. There is a growing recognition of the criticality of Indigenous leadership in sustainability practices, as well as that partnerships with Indigenous peoples and weaving knowledge systems, as a missing link to approaching global ecological crises. This review investigates the inequality in technological systems—the "digital divide" that further inhibits participation by communities and groups that retain knowledge of "place" and may offer the most transformative solutions. Following the review and synthesis, this study presents cybernetics as a bridge of understanding to Indigenous systems thinking. As non-Indigenous scholars, we hope that this study serves to foster informed, productive, and respectful dialogues so that the strength of diverse knowledges might offer whole-systems approaches to decision making that tackle wicked problems. Lastly, we discuss use cases of community-based processes and co-developed geospatial technologies, along with ethical considerations, as avenues toward enhancing equity and making advances in democratizing and decolonizing technology. [ABSTRACT FROM AUTHOR]

At present, a variety of global remote sensing surface downwelling longwave radiation (SDL) products are used for atmospheric science research; however, there are few studies on the quantitative evaluation of differences among different SDL products. In order to evaluate the differences among different SDL products quantitatively, we have selected three commonly used SDL products—Clouds and the Earth's Radiant Energy System-Synoptic Radiative Fluxes and Clouds (CERES-SYN), the European Centre for Medium Range Weather Forecasts-Surface Radiation Budget (ECMWF-SRB) and the Global Energy and Water Exchanges Project-Surface Radiation Budget (GEWEX-SRB)—to comprehensively study in this paper. The results show that there are significant differences among the three SDL products in some areas, such as in the Arctic, the Antarctic, the Sahara, the Tibet Plateau, and Greenland. The maximum absolute root mean square error (RMSEab) in these areas is greater than 20 Wm−2, the maximum relative root mean square error (RMSEre) is greater than 20%, the maximum and minimum absolute mean bias error (MBEab) are about 20 Wm−2 and −20 Wm−2, respectively, and the maximum and minimum relative mean bias error (MBEre) are about 10% and −10%, respectively. Among the three SDL products, the difference between the ECMWF-SRB and GEWEX-SRB is the most significant. In addition, this paper also analyzed the differences among different SDL products based on three aspects. Firstly, the differences among the three SDL products show that there is significant seasonality, and the differences among different months may vary greatly. However, the differences are not sensitive to years. Secondly, there are some differences in cloud-forcing radiative fluxes (CFRFs) of different SDL products, which is also an important factor affecting the difference between different SDL products. Finally, in the process of converting high temporal resolution SDL products into monthly SDL products, data processing also affects the difference between different SDL products. [ABSTRACT FROM AUTHOR]

The rapid development of Earth observation technology has promoted the continuous accumulation of images in the field of remote sensing. However, a large number of remote sensing images still lack manual annotations of objects, which makes the strongly supervised deep learning object detection method not widely used, as it lacks generalization ability for unseen object categories. Considering the above problems, this study proposes a few-shot remote sensing image object detection method that integrates context dependencies and global features. The method can be used to fine-tune the model with a small number of sample annotations based on the model trained in the base class, as a way to enhance the detection capability of new object classes. The method proposed in this study consists of three main modules, namely, the meta-feature extractor (ME), reweighting module (RM), and feature fusion module (FFM). These three modules are respectively used to enhance the context dependencies of the query set features, improve the global features of the support set that contains annotations, and finally fuse the query set features and support set features. The baseline of the meta-feature extractor of the entire framework is based on the optimized YOLOv5 framework. The reweighting module of the support set feature extraction is based on a simple convolutional neural network (CNN) framework, and the foreground feature enhancement of the support sets was made in the preprocessing stage. This study achieved beneficial results in the two benchmark datasets NWPU VHR-10 and DIOR. Compared with the comparison methods, the proposed method achieved the best performance in the object detection of the base class and the novel class. [ABSTRACT FROM AUTHOR]

The agriculture sector is highly vulnerable to natural disasters and climate change, leading to severe impacts on food security, economic stability, and rural livelihoods. The use of geospatial information and technology has been recognized as a valuable tool to help farmers reduce the adverse impacts of natural disasters on agriculture. Remote sensing and GIS are gaining traction as ways to improve agricultural disaster response due to recent advancements in spatial resolution, accessibility, and affordability. This paper presents a comprehensive overview of the FAIR agricultural disaster services. It holistically introduces the current status, case studies, technologies, and challenges, and it provides a big picture of exploring geospatial applications for agricultural disaster "from farm to space". The review begins with an overview of the governments and organizations worldwide. We present the major international and national initiatives relevant to the agricultural disaster context. The second part of this review illustrates recent research on remote sensing-based agricultural disaster monitoring, with a special focus on drought and flood events. Traditional, integrative, and machine learning-based methods are highlighted in this section. We then examine the role of spatial data infrastructure and research on agricultural disaster services and systems. The generic lifecycle of agricultural disasters is briefly introduced. Eventually, we discuss the grand challenges and emerging opportunities that range from analysis-ready data to decision-ready services, providing guidance on the foreseeable future. [ABSTRACT FROM AUTHOR]

In order to conduct more thorough research on the structural characteristics of the atmosphere and the distribution and transmission of atmospheric pollution, the use of remote sensing technology for multi-dimensional detection of the atmosphere is needed. A light-weight, low-volume, low-cost, easy-to-use and low-maintenance mini Infrared Lidar (mIRLidar) sensor is developed for the first time. The model of lidar is established, and the key optical parameters of the mIRLidar are optimized through simulation, in which wavelength of laser, energy of pulse laser, diameter of telescope, field of view (FOV), and bandwidth of filter are included. The volume and weight of the lidar system are effectively reduced through optimizing the structural design and designing a temperature control system to ensure the stable operation of the core components. The mIRLidar system involved a 1064 nm laser (the pulse laser energy 15 μJ, the repetition frequency 5 kHz), a 100 mm aperture telescope (the FOV 1.5 mrad), a 0.5 nm bandwidth of filter and an APD, where the lidar has a volume of 200 mm × 200 mm × 420 mm and weighs about 13.5 kg. It is shown that the lidar can effectively detect three-dimensional distribution and transmission of aerosol and atmospheric pollution within a 5 km detection range, from Horizontal, scanning and navigational atmospheric measurements. It has great potential in the field of meteorological research and environmental monitoring. [ABSTRACT FROM AUTHOR]

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. [ABSTRACT FROM AUTHOR]

Most of the land use land cover classification methods presented in the literature have been conducted using satellite remote sensing images. High-resolution aerial imagery is now being used for land cover classification. The Global Learning and Observations to Benefit, the Environment land cover image database, is created by citizen scientists worldwide who use their handheld cameras to take a set of six images per land cover site. These images have clutter due to man-made objects, and the pixel uncertainties result in incorrect labels. The problem of accurate labeling of these land cover images is addressed. An integrated architecture that combines Unet and DeepLabV3 for initial segmentation, followed by a weighted fusion model that combines the segmentation labels, is presented. The land cover images with labels are used for training the deep learning models. The fusion model combines the labels of five images taken from the north, south, east, west, and down directions to assign a unique label to the image sets. 2916 GLOBE images have been labeled with land cover classes using the integrated model with minimal human-in-the-loop annotation. The validation step shows that our architecture of labeling the images results in 90.97% label accuracy. Our fusion model can be used for labeling large databases of land cover classes from RGB images. [ABSTRACT FROM AUTHOR]

Landslides are one of the most devastating and recurring natural disasters and have affected several mountainous regions across the globe. The Indian Himalayan region is no exception to landslide incidences affecting key economic sectors such as transportation and agriculture and often leading to loss of lives. As reflected in the global landslide dataset, most of the landslides in this region are rainfall triggered. The region is prone to 15% of the global rainfall-induced landslides, and thereby a review of the studies in the region is inevitable. The high exposure to landslide risk has made the Indian Himalayas receive growing attention by the landslides community. A review of landslides studies conducted in this region is therefore important to provide a general picture of the state-of-the-art, a reference point for researchers and practitioners working in this region for the first time, and a summary of the improvements most urgently needed to better address landslide hazard research and management. This article focuses on various studies ranging from forecasting and monitoring to hazard and susceptibility analysis. The various factors used to analyze landslide are also studied for various landslide zones in the region. The analysis reveals that there are several avenues where significant research work is needed such as the inclusion of climate change factors or the acquisition of basic data of highest quality to be used as input data for computational models. In addition, the review reveals that, despite the entire region being highly landslide prone, most of the studies have focused on few regions and large areas have been neglected. The aim of the review is to provide a reference for stakeholders and researchers who are currently or looking to work in the Indian Himalayas, to highlight the shortcomings and the points of strength of the research being conducted, and to provide a contribution in addressing the future developments most urgently needed to obtain a consistent advance in landslide risk reduction of the area. [ABSTRACT FROM AUTHOR]

This study used remotely sensed maps of nightlights to investigate the etiology of increasing disaster losses from hydrometeorological hazards in a data-scarce area. We explored trends in the probability of occurrence of hazardous events (extreme rainfall) and exposure of the local population as components of risk. The temporal variation of the spatial distribution of exposure to hydrometeorological hazards was studied using nightlight satellite imagery as a proxy. Temporal (yearly) and spatial (1 km) resolution make them more useful than official census data. Additionally, satellite nightlights can track informal (unofficial) human settlements. The study focused on the Samala River catchment in Guatemala. The analyses of disasters, using DesInventar Disaster Information Management System data, showed that fatalities caused by hydrometeorological events have increased. Such an increase in disaster losses can be explained by trends in both: (i) catchment conditions that tend to lead to more frequent hydrometeorological extremes (more frequent occurrence of days with wet conditions); and (ii) increasing human exposure to hazardous events (as observed by amount and intensity of nightlights in areas close to rivers). Our study shows the value of remote sensing data and provides a framework to explore the dynamics of disaster risk when ground data are spatially and temporally limited. [ABSTRACT FROM AUTHOR]

Segmentation, which is usually the first step in object-based image analysis (OBIA), greatly influences the quality of final OBIA results. In many existing multi-scale segmentation algorithms, a common problem is that under-segmentation and over-segmentation always coexist at any scale. To address this issue, we propose a new method that integrates the newly developed constrained spectral variance difference (CSVD) and the edge penalty (EP). First, initial segments are produced by a fast scan. Second, the generated segments are merged via a global mutual best-fitting strategy using the CSVD and EP as merging criteria. Finally, very small objects are merged with their nearest neighbors to eliminate the remaining noise. A series of experiments based on three sets of remote sensing images, each with different spatial resolutions, were conducted to evaluate the effectiveness of the proposed method. Both visual and quantitative assessments were performed, and the results show that large objects were better preserved as integral entities while small objects were also still effectively delineated. The results were also found to be superior to those from eCongnition's multi-scale segmentation. [ABSTRACT FROM AUTHOR]

TRMM Multi-satellite Precipitation Analysis (TMPA) satellite precipitation products have been utilized to quantify, forecast, or understand precipitation patterns, climate change, hydrologic models, and drought in numerous scientific investigations. The TMPA products recently went through a series of algorithm developments to enhance the accuracy and reliability of high-quality precipitation measurements, particularly in low rainfall environments and complex terrain. In this study, we evaluated four TMPA products (3B42: V6, V7temp, V7, RTV7) against 125 rain gauges in Northern Morocco to assess the accuracy of TMPA products in various regimes, examine the performance metrics of new algorithm developments, and assess the impact of the processing error in 2012. Results show that the research products outperform the real-time products in all environments within Morocco, and the newest algorithm development (3B42 V7) outperforms the previous version (V6), particularly in low rainfall and high-elevation environments. TMPA products continue to overestimate precipitation in arid environments and underestimate it in high-elevation areas. Lastly, the temporary processing error resulted in little bias except in arid environments. These results corroborate findings from previous studies, provide scientific data for the Middle East, highlight the difficulty of using TMPA products in varying conditions, and present preliminary research for future algorithm development for the GPM mission. [ABSTRACT FROM AUTHOR]

Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, the CropWatch system has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The approach adopts a hierarchical system covering four spatial levels of detail: global, regional, national (thirty-one key countries including China) and "sub-countries" (for the nine largest countries). The thirty-one countries encompass more that 80% of both production and exports of maize, rice, soybean and wheat. The methodology resorts to climatic and remote sensing indicators at different scales. The global patterns of crop environmental growing conditions are first analyzed with indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass. At the regional scale, the indicators pay more attention to crops and include Vegetation Health Index (VHI), Vegetation Condition Index (VCI), Cropped Arable Land Fraction (CALF) as well as Cropping Intensity (CI). Together, they characterize crop situation, farming intensity and stress. CropWatch carries out detailed crop condition analyses at the national scale with a comprehensive array of variables and indicators. The Normalized Difference Vegetation Index (NDVI), cropped areas and crop conditions are integrated to derive food production estimates. For the nine largest countries, CropWatch zooms into the sub-national units to acquire detailed information on crop condition and production by including new indicators (e.g., Crop type proportion). Based on trend analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. The hierarchical approach adopted by CropWatch is the basis of the analyses of climatic and crop conditions assessments published in the quarterly "CropWatch bulletin" which provides accurate and timely information essential to food producers, traders and consumers. [ABSTRACT FROM AUTHOR]

Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics (based on crop condition profiles). Since this type of method will generate false information if there are changes in crop rotation, cropping area or crop phenology, information on cropped/uncropped arable land is integrated to improve the accuracy of crop condition monitoring. The study proposes a new method to retrieve adjusted NDVI for cropped arable land during the growing season of winter crops by integrating 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data at 250-m resolution with a cropped and uncropped arable land map derived from the multi-temporal China Environmental Satellite (Huan Jing Satellite) charge-coupled device (HJ-1 CCD) images at 30-m resolution. Using the land map's data on cropped and uncropped arable land, a pixel-based uncropped arable land ratio (UALR) at 250-m resolution was generated. Next, the UALR-adjusted NDVI was produced by assuming that the MODIS reflectance value for each pixel is a linear mixed signal composed of the proportional reflectance of cropped and uncropped arable land. When UALR-adjusted NDVI data are used for crop condition assessment, results are expected to be more accurate, because: (i) pixels with only uncropped arable land are not included in the assessment; and (ii) the adjusted NDVI corrects for interannual variation in cropping area. On the provincial level, crop growing profiles based on the two kinds of NDVI data illustrate the difference between the regular and the adjusted NDVI, with the difference depending on the total area of uncropped arable land in the region. The results suggested that the proposed method can be used to improve the assessment of early crop condition, but additional evaluation in other major crop producing regions is needed to better assess the method's application in other regions and agricultural systems. [ABSTRACT FROM AUTHOR]

The precise and rapid estimation of grassland biomass is an important scientific issue in grassland ecosystem research. In this study, based on a field survey of 1205 sites together with biomass data of the Xilingol grassland for the years 2005-2012 and the "accumulated" MODIS productivity starting from the beginning of growing season, we built regression models to estimate the aboveground biomass of the Xilingol grassland during the growing season, then further analyzed the overall condition of the grassland and the spatial and temporal distribution of the aboveground biomass. The results are summarized as follows: (1) The unitary linear model based on the field survey data and "accumulated" MODIS productivity data is the optimum model for estimating the aboveground biomass of the Xilingol grassland during the growing period, with the model accuracy reaching 69%; (2) The average aboveground biomass in the Xilingol grassland for the years 2005-2012 was estimated to be 14.35 Tg, and the average aboveground biomass density was estimated to be 71.32 g∙m-2; (3) The overall variation in the aboveground biomass showed a decreasing trend from the eastern meadow grassland to the western desert grassland; (4) There were obvious fluctuations in the aboveground biomass of the Xilingol grassland for the years 2005-2012, ranging from 10.56-17.54 Tg. Additionally, several differences in the interannual changes in aboveground biomass were observed among the various types of grassland. Large variations occurred in the temperate meadow-steppe and the typical grassland; whereas there was little change in the temperate desert-steppe and temperate steppe-desert. [ABSTRACT FROM AUTHOR]

Grassland biomass is essential for maintaining grassland ecosystems. Moreover, biomass is an important characteristic of grassland. In this study, we combined field sampling with remote sensing data and calculated five vegetation indices (VIs). Using this combined information, we quantified a remote sensing estimation model and estimated biomass in a temperate grassland of northern China. We also explored the dynamic spatio-temporal variation of biomass from 2006 to 2012. Our results indicated that all VIs investigated in the study were strongly correlated with biomass (α < 0.01). The precision of the model for estimating biomass based on ground data and remote sensing was greater than 73%. Additionally, the results of our analysis indicated that the annual average biomass was 11.86 million tons and that the average yield was 604.5 kg/ha. The distribution of biomass exhibited substantial spatial heterogeneity, and the biomass decreased from the eastern portion of the study area to the western portion. The interannual biomass exhibited strong fluctuations during 2006-2012, with a coefficient of variation of 26.95%. The coefficient of variation of biomass differed among the grassland types. The highest coefficient of variation was found for the desert steppe, followed by the typical steppe and the meadow steppe. [ABSTRACT FROM AUTHOR]

Building a mathematical model of uneven illumination and contrast is difficult, even impossible. This paper presents a novel image balancing method for a satellite image. The method adjusts the mean and standard deviation of a neighborhood at each pixel and consists of three steps, namely, elimination of coarse light background, image balancing, and max-mean-min radiation correction. First, the light background is roughly eliminated in the frequency domain. Then, two balancing factors and linear transformation are used to adaptively adjust the local mean and standard deviation of each pixel. The balanced image is obtained by using a color preserving factor after max-mean-min radiation correction. Experimental results from visual and objective aspects based on images with varying unevenness of illumination and contrast indicate that the proposed method can eliminate uneven illumination and contrast more effectively than traditional image enhancement methods, and provide high quality images with better visual performance. In addition, the proposed method not only restores color information, but also retains image details. [ABSTRACT FROM AUTHOR]

To restore declining species, including Populus euphratica and other riparian communities, in the river ecosystem of the lower Tarim River, the ecological water conveyance project (EWCP), as a part of an integrated water resource management plan, was implemented in 2000. The EWCP aims to schedule and manage the water resources in the upper reaches and transfer water to the lower reaches by a series of intermittent water deliveries. The delivered water flows along a modified river channel and nourishes riparian communities by river overflow flooding. Since it began, it has caused a fierce debate over the response of riparian vegetation to the water conveyance scheme. This study focuses on the lower Tarim River, where Populus euphratica forests have undergone watering, due to the EWCP. Twelve Landsat sensor images and one IKONOS satellite imagery acquired between 1999 and 2009 were used to monitor the change in Populus euphratica forests. Bi-temporal change detection and temporal trajectory analysis were employed to represent the spatial pattern of the forest change. Field investigations were used to analyze the driving forces behind forest change from the perspectives of anthropogenic activities and natural forces. The results showed that Populus euphratica forest have been declining in area, which implies that ecological risks have been increased during the watering process. However, forests areas have increased in the regions where the water supply is abundant, and vice versa. [ABSTRACT FROM AUTHOR]

Wheat stripe rust poses a serious threat to wheat production. An effective prediction method is important for food security. In this study, we developed a prediction model for wheat stripe rust based on vegetation indices and meteorological features. First, based on time-series Sentinel-2 remote sensing images and meteorological data, wheat phenology (jointing date) was estimated using the harmonic analysis of time-series combined with average cumulative temperature. Then, vegetation indices were extracted based on phenological information. Meteorological features were screened using correlation analysis combined with independent t-test analysis. Finally, a random forest (RF) was used to construct a prediction model for wheat stripe rust. The results showed that the RF model using the input combination (phenological information-based vegetation indices and meteorological features) produced a higher prediction accuracy and a kappa coefficient of 88.7% and 0.772, respectively. The prediction model using phenological information-based vegetation indices outperformed the prediction model using single-date image-based vegetation indices, and the overall accuracy improved from 62.9% to 78.4%. These results indicated that the method combining phenological information-based vegetation indices and meteorological features can be used for wheat stripe rust prediction. The results of the prediction model can provide guidance and suggestions for disease prevention in the study area. [ABSTRACT FROM AUTHOR]

Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods. [ABSTRACT FROM AUTHOR]

Robust calibration and validation (Cal and Val) should guarantee the accuracy of the retrieved information, make the remote sensing data consistent and traceable, and maintain the sensor performance during the operational phase. The DRAGON program has set up many remote sensing research topics on various application domains. In order to promote the effectiveness of data modeling and interpretation, it is necessary to solve various challenges in Cal and Val for quantitative RS applications. This project in the DRAGON 4 program aims to promote the cooperation of the Cal and Val experts from European and Chinese institutes in Cal and Val activities, and several achievements have been obtained in the advanced on-orbit optical sensor calibration, as well as microwave remote sensor calibration and product generation. The outcomes of the project have benefited the related remote sensing modeling and product retrieval, and promoted the radiometric calibration network (RadCalNet) as an international operational network for calibration, intercalibration, and validation. Moreover, this project provided local governments with a more accurate OMI NO2 data in China, which were used to study the air quality control during APEC period, Parade period and G20 period. This will be of ongoing be value for monitoring atmospheric environmental quality and formulating pollution control strategies. [ABSTRACT FROM AUTHOR]

Due to differences in external imaging conditions, multispectral images taken at different periods are subject to radiation differences, which severely affect the detection accuracy. To solve this problem, a modified algorithm based on slow feature analysis is proposed for multispectral image change detection. First, single-band slow feature analysis is performed to process bitemporal multispectral images band by band. In this way, the differences between unchanged pixels in each pair of single-band images can be sufficiently suppressed to obtain multiple feature-difference images containing real change information. Then, the feature-difference images of each band are fused into a grayscale distance image using the Euclidean distance. After Gaussian filtering of the grayscale distance image, false detection points can be further reduced. Finally, the k-means clustering method is performed on the filtered grayscale distance image to obtain the binary change map. Experiments reveal that our proposed algorithm is less affected by radiation differences and has obvious advantages in time complexity and detection accuracy. [ABSTRACT FROM AUTHOR]

Using unmanned aerial vehicles (UAVs) for remote sensing has the advantages of high flexibility, convenient operation, low cost, and wide application range. It fills the need for rapid acquisition of high-resolution aerial images in modern photogrammetry applications. Due to the insufficient parallaxes and the computation-intensive process, dense real-time reconstruction for large terrain scenes is a considerable challenge. To address these problems, we proposed a novel SLAM-based MVS (Multi-View-Stereo) approach, which can incrementally generate a dense 3D (three-dimensional) model of the terrain by using the continuous image stream during the flight. The pipeline of the proposed methodology starts with pose estimation based on SLAM algorithm. The tracked frames were then selected by a novel scene-adaptive keyframe selection method to construct a sliding window frame-set. This was followed by depth estimation using a flexible search domain approach, which can improve accuracy without increasing the iterate time or memory consumption. The whole system proposed in this study was implemented on the embedded GPU based on an UAV platform. We proposed a highly parallel and memory-efficient CUDA-based depth computing architecture, enabling the system to achieve good real-time performance. The evaluation experiments were carried out in both simulation and real-world environments. A virtual large terrain scene was built using the Gazebo simulator. The simulated UAV equipped with an RGB-D camera was used to obtain synthetic evaluation datasets, which were divided by flight altitudes (800-, 1000-, 1200 m) and terrain height difference (100-, 200-, 300 m). In addition, the system has been extensively tested on various types of real scenes. Comparison with commercial 3D reconstruction software is carried out to evaluate the precision in real-world data. According to the results on the synthetic datasets, over 93.462% of the estimation with absolute error distance of less then 0.9%. In the real-world dataset captured at 800 m flight height, more than 81.27% of our estimated point cloud are less then 5 m difference with the results of Photoscan. All evaluation experiments show that the proposed approach outperforms the state-of-the-art ones in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics. [ABSTRACT FROM AUTHOR]

The identification of underground geohazards is always a difficult issue in the field of underground public safety. This study proposes an interactive visualization framework for underground geohazard recognition on urban roads, which constructs a whole recognition workflow by incorporating data collection, preprocessing, modeling, rendering and analyzing. In this framework, two proposed sampling point selection methods have been adopted to enhance the interpolated accuracy for the Kriging algorithm based on ground penetrating radar (GPR) technology. An improved Kriging algorithm was put forward, which applies a particle swarm optimization (PSO) algorithm to optimize the Kriging parameters and adopts in parallel the Compute Unified Device Architecture (CUDA) to run the PSO algorithm on the GPU side in order to raise the interpolated efficiency. Furthermore, a layer-constrained triangulated irregular network algorithm was proposed to construct the 3D geohazard bodies and the space geometry method was used to compute their volume information. The study also presents an implementation system to demonstrate the application of the framework and its related algorithms. This system makes a significant contribution to the demonstration and understanding of underground geohazard recognition in a three-dimensional environment. [ABSTRACT FROM AUTHOR]

The Dark Target aerosol algorithm was developed to exploit the information content available from the observations of Moderate-Resolution Imaging Spectroradiometers (MODIS), to better characterize the global aerosol system. The algorithm is based on measurements of the light scattered by aerosols toward a space-borne sensor against the backdrop of relatively dark Earth scenes, thus giving rise to the name "Dark Target". Development required nearly a decade of research that included application of MODIS airborne simulators to provide test beds for proto-algorithms and analysis of existing data to form realistic assumptions to constrain surface reflectance and aerosol optical properties. This research in itself played a significant role in expanding our understanding of aerosol properties, even before Terra MODIS launch. Contributing to that understanding were the observations and retrievals of the growing Aerosol Robotic Network (AERONET) of sun-sky radiometers, which has walked hand-in-hand with MODIS and the development of other aerosol algorithms, providing validation of the satellite-retrieved products after launch. The MODIS Dark Target products prompted advances in Earth science and applications across subdisciplines such as climate, transport of aerosols, air quality, and data assimilation systems. Then, as the Terra and Aqua MODIS sensors aged, the challenge was to monitor the effects of calibration drifts on the aerosol products and to differentiate physical trends in the aerosol system from artefacts introduced by instrument characterization. Our intention is to continue to adapt and apply the well-vetted Dark Target algorithms to new instruments, including both polar-orbiting and geosynchronous sensors. The goal is to produce an uninterrupted time series of an aerosol climate data record that begins at the dawn of the 21st century and continues indefinitely into the future. [ABSTRACT FROM AUTHOR]

In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring. [ABSTRACT FROM AUTHOR]

The manuscript reviews the current literature on scattering applications of RADAR (Radio Detecting And Ranging) in remote sensing and diagnostics. This paper gives prime features for a variety of RADAR applications ranging from forest and climate monitoring to weather forecast, sea status, planetary information, and mapping of natural disasters such as the ones caused by earthquakes. Both the fundamental parameters involved in scattering mechanisms of RADAR applications and the factors affecting RADAR performances are also discussed. [ABSTRACT FROM AUTHOR]

The Virginia Tech Formation Flying Testbed (VTFFTB) is a global navigation satellite system (GNSS)-based hardware-in-the-loop (HIL) simulation testbed for spacecraft formation flying with ionospheric remote sensing applications. Past applications considered only the Global Positioning System (GPS) constellation. The rapid GNSS modernization offers more signals from other constellations, including the growing European system—Galileo. This study presents an upgrade of VTFFTB with the incorporation of Galileo and the associated enhanced capabilities. By simulating an ionospheric plasma bubble scenario with a pair of LEO satellites flying in formation, the GPS-based simulations are compared to multi-constellation GNSS simulations including the Galileo constellation. A comparison between multi-constellation (GPS and Galileo) and single-constellation (GPS) shows the absolute mean and standard deviation of vertical electron density measurement errors for a specific Equatorial Spread F (ESF) scenario are decreased by 32.83% and 46.12% with the additional Galileo constellation using the 13 July 2018 almanac. Another comparison based on a simulation using the 8 March 2019 almanac shows the mean and standard deviation of vertical electron density measurement errors were decreased further to 43.34% and 49.92% by combining both GPS and Galileo data. A sensitivity study shows that the Galileo electron density measurements are correlated with the vertical separation of the formation configuration. Lower C/N 0 level increases the measurement errors and scattering level of vertical electron density retrieval. Relative state estimation errors are decreased, as well by utilizing GPS L1 plus Galileo E1 carrier phase instead of GPS L1 only. Overall, superior performance on both remote sensing and relative navigation applications is observed by adding Galileo to the VTFFTB. [ABSTRACT FROM AUTHOR]

Surface shortwave net radiation (SSNR) flux is essential for the determination of the radiation energy balance between the atmosphere and the Earth's surface. The satellite-derived intermediate SSNR data are strongly needed to bridge the gap between existing coarse-resolution SSNR products and point-based measurements. In this study, four different machine learning (ML) algorithms were tested to estimate the SSNR from the Landsat Thematic Mapper (TM)/ Enhanced Thematic Mapper Plus (ETM+) top-of-atmosphere (TOA) reflectance and other ancillary information (i.e., clearness index, water vapor) at instantaneous and daily scales under all sky conditions. The four ML algorithms include the multivariate adaptive regression splines (MARS), backpropagation neural network (BPNN), support vector regression (SVR), and gradient boosting regression tree (GBRT). Collected in-situ measurements were used to train the global model (using all data) and the conditional models (in which all data were divided into subsets and the models were fitted separately). The validation results indicated that the GBRT-based global model (GGM) performs the best at both the instantaneous and daily scales. For example, the GGM based on the TM data yielded a coefficient of determination value (R2) of 0.88 and 0.94, an average root mean square error (RMSE) of 73.23 W∙m-2 (15.09%) and 18.76 W·m-2 (11.2%), and a bias of 0.64 W·m-2 and –1.74 W·m-2 for instantaneous and daily SSNR, respectively. Compared to the Global LAnd Surface Satellite (GLASS) daily SSNR product, the daily TM-SSNR showed a very similar spatial distribution but with more details. Further analysis also demonstrated the robustness of the GGM for various land cover types, elevation, general atmospheric conditions, and seasons [ABSTRACT FROM AUTHOR]

Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult. In this study, a robust method to automatically extract structural and non-structural road edges based on a topological network of laser points between adjacent scan lines and auxiliary surfaces is proposed. The extraction of road and curb points was achieved mainly from the roughness of the extracted surface, without considering traditional thresholds (e.g., height jump, slope, and density). Five large-scale road datasets, containing different types of road curbs and complex road scenes, were used to evaluate the practicality, stability, and validity of the proposed method via qualitative and quantitative analyses. Measured values of the correctness, completeness, and quality of extracted road edges were over 95.5%, 91.7%, and 90.9%, respectively. These results confirm that the proposed method can extract road edges from large-scale MLS datasets without the need for auxiliary information on intensity, image, or geographic data. The proposed method is effective regardless of whether the road width is fixed, the road is regular, and the existence of pedestrians and vehicles. Most importantly, the proposed method provides a valuable solution for road edge extraction that is useful for road authorities when developing intelligent transportation systems, such as those required by self-driving vehicles. [ABSTRACT FROM AUTHOR]

Evaluating and monitoring forest areas during a restoration process is indispensable to estimate the success or failure of management intervention and to correct the restoration trajectory through adaptive management. However, the field measurement of several indicators in large areas can be expensive and laborious, and establishing reference values for indicators is difficult. The use of supervised classification techniques of high resolution images, combined with an expert system to generate management recommendations, can be considered promising tools for monitoring and evaluating restoration areas. The objective of the present study was to elaborate an expert system of management recommendation generation for areas under restoration, which were monitored by two different remote sensors: UAV (Unmanned Aerial Vehicle) and LiDAR (Light Detection and Ranging). The study was carried out in areas under restoration with about 54 ha and five years of implementation, owned by Fibria Celulose S.A. (recently acquired by Suzano S.A.), in the southern region of Bahia State, Brazil. We used images from Canon S110 NIR (green, red, near infrared) on UAV and LiDAR data compositions (intensity image, digital surface model, digital terrain model, normalized digital surface model). The monitored restoration indicator entailed land cover separated into three classes: Canopy cover, bare soil and grass cover. The images were classified using the Random Forest (RF) and Maximum Likelihood (ML) algorithms and the area occupied by each land cover classes was calculated. An expert system was developed in ArcGIS to define management recommendations according to the land cover classes, and then we compared the recommendations generated by both algorithms and images. There was a slight difference between the recommendations generated by the different combinations of images and classifiers. The most frequent management recommendation was "weed control + plant seedlings" (34%) for all evaluated methods. The image monitoring methods suggested by this study proved to be efficient, mainly by reducing the time and cost necessary for field monitoring and increasing the accuracy of the generated management recommendations. [ABSTRACT FROM AUTHOR]

Despite the low tree diversity and scarcity of the understory vegetation, the high morphological plasticity of mangrove trees induces, at the stand level, a very large variability of forest structures that need to be mapped for assessing the functioning of such complex ecosystems. Fully constrained linear spectral unmixing (FCLSU) of very high spatial resolution (VHSR) multispectral images was tested to fine-scale map mangrove zonations in terms of horizontal variation of forest structure. The study was carried out on three Pleiades-1A satellite images covering French island territories located in the Atlantic, Indian, and Pacific Oceans, namely Guadeloupe, Mayotte, and New Caledonia archipelagos. In each image, FCLSU was trained from the delineation of areas exclusively related to four components including either pure vegetation, soil (ferns included), water, or shadows. It was then applied to the whole mangrove cover imaged for each island and yielded the respective contributions of those four components for each image pixel. On the forest stand scale, the results interestingly indicated a close correlation between FCLSU-derived vegetation fractions and canopy closure estimated from hemispherical photographs (R2 = 0.95) and a weak relation with the Normalized Difference Vegetation Index (R2 = 0.29). Classification of these fractions also offered the opportunity to detect and map horizontal patterns of mangrove structure in a given site. K-means classifications of fraction indeed showed a global view of mangrove structure organization in the three sites, complementary to the outputs obtained from spectral data analysis. Our findings suggest that the pixel intensity decomposition applied to VHSR multispectral satellite images can be a simple but valuable approach for (i) mangrove canopy monitoring and (ii) mangrove forest structure analysis in the perspective of assessing mangrove dynamics and productivity. As with Lidar-based surveys, these potential new mapping capabilities deserve further physically based interpretation of sunlight scattering mechanisms within forest canopy. [ABSTRACT FROM AUTHOR]

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is a well-known meta-heuristic. It has been widely used in both research and engineering fields. However, the original PSO generally suffers from premature convergence, especially in multimodal problems. In this paper, we propose a double-group PSO (DG-PSO) algorithm to improve the performance. DG-PSO uses a double-group based evolution framework. The individuals are divided into two groups: an advantaged group and a disadvantaged group. The advantaged group works according to the original PSO, while two new strategies are developed for the disadvantaged group. The proposed algorithm is firstly evaluated by comparing it with the other five popular PSO variants and two state-of-the-art meta-heuristics on various benchmark functions. The results demonstrate that DG-PSO shows a remarkable performance in terms of accuracy and stability. Then, we apply DG-PSO to multilevel thresholding for remote sensing image segmentation. The results show that the proposed algorithm outperforms five other popular algorithms in meta-heuristic-based multilevel thresholding, which verifies the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Landslide geological disasters, occurring globally, often result in significant loss of life and extensive economic damage. In recent years, the severity of these disasters has increased, likely due to the frequent occurrence of extreme rainstorms associated with global warming. This escalating trend emphasizes the urgent need for a simple and efficient method to identify hidden dangers related to landslide geological disasters. Areas experiencing seasonal heavy rainfall are particularly susceptible to such disasters, posing a serious threat to the lives and property of local residents. In response to the challenging characteristics of landslide geological hazards, such as their strong concealment and the high vegetation coverage in the Liupan Mountain area of the Loess Plateau, this study focuses on the integrated remote sensing identification and research of hidden landslide dangers in Longde County. The methodology combines differential interferometric synthetic aperture radar technology (D-InSAR) and high-resolution optical remote sensing. Surface deformation information of Longde County was obtained by analyzing 85 Sentinel-1A data from 2019 to mid-2020 using Stacking-InSAR, in conjunction with high-resolution optical remote sensing image data from GF-2 in 2019. Furthermore, the study conducted integrated remote sensing identification and field verification of landslide hazards throughout the entire county. This involved interpreting the shape and deformation marks of landslide hazards, identifying the disaster-bearing bodies, and expertly interpreting the environmental factors contributing to the hazards. As a result, 47 suspected landslide hazards and 21 field investigation points were identified, with 16 hazards verified with an accuracy of 76.19%. This outcome directly confirms the applicability and accuracy of the integrated remote sensing identification technology in the study area. The research results presented in this paper provide an effective scientific and theoretical basis for the monitoring and treatment of landslide geological disasters in the future stages. They also play a pivotal role in the prevention of such disasters. [ABSTRACT FROM AUTHOR]

Rapid global economic development, population growth, and increased motorization have resulted in significant issues in urban traffic safety. This study explores the intrinsic connections between road environments and driving safety by integrating multiple visual landscape elements. High-resolution remote sensing and street-view images were used as primary data sources to obtain the visual landscape features of an urban expressway. Deep learning semantic segmentation was employed to calculate visual landscape features, and a trend surface fitting model of road landscape features and driver fatigue was established based on experimental data from 30 drivers who completed driving tasks in random order. There were significant spatial variations in the visual landscape of the expressway from the city center to the urban periphery. Heart rate values fluctuated within a range of 0.2% with every 10% change in driving speed and landscape complexity. Specifically, as landscape complexity changed between 5.28 and 8.30, the heart rate fluctuated between 91 and 96. This suggests that a higher degree of landscape richness effectively mitigates increases in driver fatigue and exerts a positive impact on traffic safety. This study provides a reference for quantitative assessment research that combines urban road landscape features and traffic safety using multiple data sources. It may guide the implementation of traffic safety measures during road planning and construction. [ABSTRACT FROM AUTHOR]

With the global issues of extreme climate and urbanization, the ecological security patterns (ESPs) in the Qinling Mountains are facing prominent challenges. As a crucial ecological barrier in China, understanding the characteristics of ESPs in the Qinling Mountains is vital for achieving sustainable development. This study focuses on Yangxian and employs methods such as machine learning (ML), remote sensing (RS), geographic information systems (GISs), analytic hierarchy process and principal component analysis (AHP–PCA), and the minimum cumulative resistance (MCR) model to construct an ecological security network based on multi-factor ecological sensitivity (ES) and conduct quantitative spatial analysis. The results demonstrate that the AHP–PCA method based on ML overcomes the limitations of the single-weighting method. The ESPs of Yangxian were established, consisting of 21 main and secondary ecological sources with an area of 592.81 km2 (18.55%), 41 main and secondary ecological corridors with a length of 738.85 km, and 33 ecological nodes. A coupling relationship among three dimensions was observed: comprehensive ecological sensitivity, ESPs, and administrative districts (ADs). Huangjinxia Town (1.43 in C5) and Huayang Town (7.28 in C4) likely have significant areas of ecological vulnerability, while Machang Town and Maoping Town are important in the ESPs. ADs focus on protection and management. The second corridor indicated high-quality construction, necessitating the implementation of strict protection policies in the study area. The innovation lies in the utilization of quantitative analysis methods, such as ML and RS technologies, to construct an ecological spatial pattern planning model and propose a new perspective for the quantitative analysis of ecological space. This study provides a quantitative foundation for urban and rural ecological spatial planning in Yangxian and will help facilitate the sustainable development of ecological planning in the Qinling region. [ABSTRACT FROM AUTHOR]

In remote sensing images, small objects have too few discriminative features, are easily confused with background information, and are difficult to locate, leading to a degradation in detection accuracy when using general object detection networks for aerial images. To solve the above problems, we propose a remote sensing small object detection network based on the attention mechanism and multi-scale feature fusion, and name it AMMFN. Firstly, a detection head enhancement module (DHEM) was designed to strengthen the characterization of small object features through a combination of multi-scale feature fusion and attention mechanisms. Secondly, an attention mechanism based channel cascade (AMCC) module was designed to reduce the redundant information in the feature layer and protect small objects from information loss during feature fusion. Then, the Normalized Wasserstein Distance (NWD) was introduced and combined with Generalized Intersection over Union (GIoU) as the location regression loss function to improve the optimization weight of the model for small objects and the accuracy of the regression boxes. Finally, an object detection layer was added to improve the object feature extraction ability at different scales. Experimental results from the Unmanned Aerial Vehicles (UAV) dataset VisDrone2021 and the homemade dataset show that the AMMFN improves the A P s values by 2.4% and 3.2%, respectively, compared with YOLOv5s, which represents an effective improvement in the detection accuracy of small objects. [ABSTRACT FROM AUTHOR]

Taiyuan City in the eastern Loess Plateau has experienced severe ecological problems caused by urban expansion. For cities undergoing rapid urbanization, building an ecological security pattern (ESP) is an effective means to improve urban resilience. Here, geographic information systems (GIS) were used to analyze, manipulate, and visualize urban ecological multi-source information and remote sensing (RS) for the history of land use/land-cover (LULC) changes and the structure of the urban ecological system. Four important ecosystem service functions were estimated: soil conservation, habitat quality, water yield, and carbon storage. The minimum cumulative resistance (MCR) model was combined with the circuit theory method to determine the ecological corridors, pinch points, and barrier points. Our results showed that: (1) from 1980 to 2020, Taiyuan's built-up area showed increased construction land and enhanced landscape fragmentation. The decline in cultivated land was mainly attributed to construction land. During the period from 2000 to 2010, a greater amount of land was changed in Taiyuan than in other periods; (2) The ecosystem service evaluation based on the LULC in 2020 revealed that the central urban area was lower than the other areas; (3) 38 ecological source sites were identified, accounting for 16% of the total study area. An area of 106 km2 was allocated to construct 79 ecological corridors. We identified 31 ecological pinch points and 6 ecological barrier points; (4) an ESP optimization governance model of "two rings, four zones, and nine corridors" was proposed. Our study provides theoretical guidance for sustainable development and ecological design in Taiyuan City and other regions. [ABSTRACT FROM AUTHOR]

Reconstruction of historical land uses helps to understand patterns, drivers, and impacts of land-use change, and is essential for finding solutions to land-use sustainability. In order to analyze the relationship between land-use change and urban flooding, this study used the Classification and Regression Tree (CART) method to extract modern (2017) land-use data based on remote sensing images. Then, the Paleo-Land-Use Reconstruction (PLUR) program was used to reconstruct the land-use maps of Xi'an during the Ming (1582) and Qing (1766) dynasties by consulting and collecting records of land-use change in historical documents. Finally, the Flo-2D model was used to simulate urban flooding under different land-use scenarios. Over the past 435 years (1582–2017), the urban construction land area showed a trend of increasing, while the unused land area and water bodies were continuously decreasing. The increase in urban green space and buildings was 20.49% and 19.85% respectively, and the unused land area changed from 0.32 km2 to 0. Urban flooding in the modern land-use scenario is the most serious. In addition to the increase in impervious areas, the increase in building density and the decrease in water areas are also important factors that aggravate urban flooding. This study can provide a reference for future land-use planning and urban flooding control policy formulation and revision in the study area. [ABSTRACT FROM AUTHOR]

As a result of urbanization and climate change, urban areas are increasingly vulnerable to flooding, which can have devastating effects on the loss of life and property. Remote sensing technology can provide practical help for urban flood disaster management. This research presents a review of urban flood-related remote sensing to identify research trends and gaps, and reveal new research opportunities. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the systematic literature search resulted in 347 documents classified as geography, disaster management application, and remote sensing data utilization. The main results include 1. most of the studies are located in high-income countries and territories and inland areas; 2. remote sensing for observing the environment was more popular than observing the building; 3. the most often applied disaster management activities were vulnerability assessment and risk modeling (mitigation) and rapid damage assessment (response); 4. DEM is often applied to simulate urban floods as software inputs. We suggest that future research directions include 1. coastal urban study areas in non-high-income countries/territories to help vulnerable populations; 2. understudied disaster management activities, which often need to observe the buildings in more urban areas; 3. data standardization will facilitate integration with international standard methods for assessing urban floods. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, climate problems affecting human health and safe operation of cities have intensified, such as heat island effect, haze, and acid rain. Using high-resolution remote sensing mapping image data to design scientific and efficient algorithms to excavate and plan urban ventilation corridors and improve urban ventilation environment is an effective way to solve these problems. In this paper, we use unmanned aerial vehicle (UAV) tilt photography technology to obtain high-precision remote sensing image digital elevation model (DEM) and digital surface model (DSM) data, count the city's dominant wind direction in each season using long-term meteorological data, and use building height to calculate the dominant wind direction. The projection algorithm calculates the windward area density of this dominant direction. Under the guidance of K-means, the binarized windward area density map is used to determine each area and boundary of potential ventilation corridors within the threshold range, and the length and angle of each area's fitted elliptical long axis are calculated to extract the ventilation corridors that meet the criteria. On the basis of high-precision stereo remote sensing data from UAV, the paper uses image classification, segmentation, fitting, and fusion algorithms to intelligently mine potential urban ventilation corridors, and the effectiveness of the proposed method is demonstrated through a case study in Zhuji City, Zhejiang Province. [ABSTRACT FROM AUTHOR]