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A detailed study of the ancient caravan routes connecting Mongolia and Tibet has yet to be established. This paper describes the results of initial investigations on the identification of caravan traces through Mongolia from historical sources, fieldwork, and remote sensing reconnaissance. Recreating the Roerich Central Asian Expedition of 1927, we identify several artifacts and locations that support our belief that these routes contributed to the movement and exchange of people, ideas, and commerce across the desert landscapes of Inner Asia and helped shape cultural and religious identities that still exist to this day. Moreover, we argue the Mongolia to Tibet caravan routes were part of the greater network of ancient Silk Roads and should be considered as such: an important, intangible cultural heritage worthy of further exploration, preservation, and scholarly study. • Mongolia-Tibet caravan routes were pivotal in shaping Inner Asian identities but are often overlooked in Silk Roads research. • Evidence of these routes include tracks, cairns, temples, and forts, signifying their role as well-traveled trade paths. • These routes were part of the broader Silk Roads network and merit attention for their intangible cultural heritage value. • Inclusive Silk Roads heritage acknowledges diverse cultural contributions and fosters understanding and conservation. [ABSTRACT FROM AUTHOR]

Fully-autonomous vehicles, both aerial and ground, could provide great benefits in the Agriculture 4.0 framework when operating within cooperative architectures, thanks to their ability to tackle difficult tasks, particularly within complex irregular and unstructured scenarios such as vineyards on sloped terrains. A decentralised multi-phase approach has been proposed as an alternative to more common cooperative schemes. When perennial crops are considered, it is advantageous to build a simplified geometrical (and georeferenced) crops model, which can be identified by using 3D point clouds acquired during a-priori explorative missions by unmanned aerial vehicles. This model can be used to plan the tasks to be performed within the crops by the in-field aerial and ground drones. In this companion paper, the proposed strategy is applied to a specific case study involving a vineyard on a sloped terrain, located in the Barolo region in Piedmont, Italy. Ad-hoc technologies and guidance, navigation and control algorithms were designed and implemented. The main objectives were to improve the autonomous driving capabilities of the drones involved and to automate the process of retrieving low-complexity maps from the data collected with preliminary remote sensing missions to make them available for the autonomous navigation by a quadrotor and an unmanned 4-wheel steering ground vehicle within the vine rows. Preliminary results highlight the benefits achievable by exploiting the tailored technologies selected and applied to improve each of the analysed mission phases. • Autonomous ground and aerial vehicles can lead to improve agriculture management. • A collaborative scheme based on a multi-phase approach is proposed. • A specific case study involving a vineyard located in the Barolo is presented. • Design of advanced GNC schemes are proposed for autonomous navigation of drones. [ABSTRACT FROM AUTHOR]

Wilderness areas are natural landscape elements that are relatively undisrupted by human activity and play a critical role in maintaining ecological equilibrium, preserving naturalness, and ensuring ecosystem resilience. Since 2000, monitoring of global wilderness areas has increased owing to the availability of spatial map data and remote sensing imagery related to human activity and/or human footprint. Progress has been made in the remote sensing of wilderness areas by relying on available historical literature (e.g., published papers, books, and reports). However, to our knowledge, a synthesis of wilderness area research from a remote sensing perspective has not yet been performed. In this preliminary review, we discuss the concept of wilderness in different historical eras and systematically summarize dynamic wilderness monitoring at local, national, and global scales, available remotely sensed indicators, disparities and commonalities in identification methods, and mapping uncertainties. Finally, since this field remains in its initial stage owing to a lack of unified standards and vertical/horizontal comparisons, we present insights into future research directions, particularly with regard to remote sensing. The findings of this review may help to improve the overall understanding of current wilderness patterns (i.e., increases/decreases) and the mechanisms by which they change, as well as provide guidance for global nature conservation programs. • Wilderness is a flexible concept that evolves to fit the changing times. • Reviewed progresses in mapping wilderness at different scales. • Wilderness identification at different scales is characterized by relatively consistent methods and attributes. • Integrating geo-big data with participatory methods is a useful direction for wilderness mapping. [ABSTRACT FROM AUTHOR]

Situated in the north of the Qinghai-Tibet Plateau, the Qaidam Basin experiences limited precipitation and significant evaporation. Despite these conditions, it stands out as one of the most densely distributed lakes in China. The formation of these lakes is controversial: whether the lake water primarily originates from local precipitation or external water sources. To address this issue, this paper explores the recharge sources of lakes in the Qaidam Basin and the circulation patterns of groundwater from a remote sensing perspective. Based on deep learning networks, we optimized the soft object regions of the Object-Contextual Representations Network (OCRNet) and proposed the Remote·Sensing Adaptive-Improved OCRNet (RSA-IOCRNet). Compared with seven other networks, RSA-IOCRNet obtained better experimental results and was used to construct an area sequence of 16 major lakes in the Qaidam Basin. Combined with multi-source data, the comprehensive analysis indicates no significant correlation between climatic factors and lake changes, while an obvious correlation between lakes and groundwater changes in the eastern Qaidam, consisting with the results of the field survey. Deep-circulating groundwater recharges numerous Qaidam lakes through upwelling from fault zones, such as Gasikule Lake and Xiaochaidan Lake. Groundwater in the Qaidam Basin is more depleted in hydrogen-oxygen isotope characteristics than surface water in the basin, but similar to some river water in the endorheic Tibetan Plateau. This indicates that Tibetan seepage water, estimated at approximately 540 billion m3/a, is transported through the Qaidam Basin via deep circulation. Moreover, it rises to recharge the groundwater and lakes within this basin through fracture zones, extending to various arid and semi-arid regions such as Taitema Lake. This work provides a new perspective on the impact of deep groundwater on lakes and water circulation in these areas. [Display omitted] • RSA-OCRNet constructs area changes of 16 major lakes in the Qaidam Basin. • Changes in the Qaidam lakes are strongly linked to groundwater recharge. • Groundwater in Qaidam may come from seepage in Qiangtang by deep circulation. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification has made great progress with deep learning. Due to complex backgrounds and the large number of objects with inhomogeneous sizes, remote sensing image scene classification still remains challenging. In fact, only a few parts of regions are expected to be representative of the scene. In this paper, we propose a dual-stream framework for remote sensing classification with the help of category-related regions. Swin Transformer-based global feature extractor is used to produce coarse classification results. A category-related key region localization module is developed to extract the representative region from the whole image. The ResNet50 is employed as a local feature extractor to mine critical features from prominent regions. Finally, the classification results is obtained with the shared weighted classify head on global-local features. Extensive experiments show that our method provides a promising approach for key region locations and performs best on three public remote sensing datasets. [ABSTRACT FROM AUTHOR]

Curbing the continuous abandonment of large areas of farmland is important for meeting the global food demand and promoting agricultural and rural development. Accurate identification is the key to the effective management and utilization of abandoned farmland. The identification of abandoned land based on a long time series of remote sensing data has become rapid and effective. Therefore, a set of training and test datasets generated from invariant samples and reference sample sets is established in this paper. On this basis, the Google Earth Engine (GEE) is used to classify Landsat and Sentinel high-precision long-term remote sensing images from 2000 to 2022. In addition, a change detector based on the sliding window algorithm is proposed to extract abandoned farmland in the Huangshui Basin from 2002 to 2020, and the intensity, trend, frequency, reclamation rate and utilization efficiency are analyzed. The results revealed that the OA of land use classification in the Huangshui Basin from 2000 to 2022 was between 0.852 and 0.91, and the kappa coefficient was between 0.822 and 0.89, indicating a good classification effect. From 2002 to 2020, the accumulated abandoned farmland area in the Huangshui Basin continued to increase, showing a fluctuating upward trend, and the phenomenon of farmland abandonment and reclamation occurs repeatedly in some areas. From the overall distribution, the abandoned area gradually increased from the central region to the southeast. With the passage of time, the amount of abandoned farmland in the valley increased gradually, and the abandoned area was transferred from the high mountains to the valley area. The average annual abandonment rate of supplementary farmland was 50.45%, which was much greater than that of basic farmland. Most of the supplementary farmland could not be effectively and judiciously used, and the utilization efficiency was low. The research results provide data support for the reuse of abandoned farmland in ecologically fragile plateau areas, the formulation of targeted strategies, the implementation of timely adjustments, and the establishment of new ideas and methods for the accurate identification of abandoned farmland. • Developed a set of training and test data sets generated by invariant and reference samples. • Proposed a sliding window algorithm based on the change detector to plot long time series abandoned farmland. • Spatiotemporal distribution, trend, and recultivation of abandoned were analyzed. • Abandonment rate of supplementary farmland is higher than original farmland. [ABSTRACT FROM AUTHOR]

The site selection for Low Impact Development (LID) practices is a significant process. It affects the effectiveness of LID in controlling stormwater surface runoff, volume, flow rate, and infiltration. This research paper presents a comprehensive review of various methods used for LID site selection. It starts by introducing different methods and tools. Three main methods: index-based methods, GIS-based multi-criteria decision analysis (MCDA), and multi-criteria models and tools, are discussed in detail. A comparative analysis of these methods is then conducted based on ten different criteria. These criteria include the number of variables, data properties, the scale of analysis, benefits maximization approach, multi-attribute decision analysis, user-friendliness, community and stakeholder participation, and the validation methods. This comparison reveals limitations in each method. These include inadequate data availability and quality, lack of evaluation methods, comprehensive assessment criteria and spatial explicitness. These challenges underscore the need for future research to prioritize spatial clarity, broaden criteria, improve data quality through standardization, incorporate field visits and remote sensing for robust results, integrate big data, and develop web-based, open-source tools for enhanced accessibility. These key strategies provide valuable insights for advancing LID site selection methods. • Reviews key LID site selection methods. • Comparative analysis based on multiple factors. • Highlights method limitations, prompting further research in LID site selection. • Recommends improvements and new methods to address research gaps. [ABSTRACT FROM AUTHOR]

With the continuous development of urbanization, urban population, economy and other factors have a close impact on the geometry and distribution of urban buildings. Obtaining information of urban buildings from aerial images or satellite images quickly and accurately is not only conducive to updating geospatial data, but also of great significance for effective monitoring of new thematic information such as new buildings. Moreover, in recent years, the research and improvement of building recognition and contour extraction algorithms based on satellite images or aerial images are helpful to the recognition and classification of urban buildings. It is of great significance to the acquisition of GIS data, the understanding of images, large-scale mapping and many other applications of remote sensing data. With the development of artificial intelligence and computer technology, the image processing of intelligent building environment based on pattern recognition technology has become an important research direction in the field of intelligent building image recognition. Based on the concept, principle and technology analysis of pattern recognition technology, this paper studies the application of pattern recognition technology in the image processing of intelligent building environment. In this paper, based on image processing of intelligent building as the basic theoretical platform, with the pattern recognition technology as the basic research means, three problems of image processing, image extraction and image recognition in image processing of building intelligent environment are studied respectively, and corresponding reasonable solutions are put forward. [ABSTRACT FROM AUTHOR]

Abstract The assessment of burn severity is highly important in order to describe and measure the effects of fire on vegetation, wildlife habitat and soils. The estimation of burn severity based on remote sensing is a powerful tool that, to be useful, needs to be related and validated with field data. The present paper explores the relationships between field accessible variables and Relative Differenced Normalized Burn Ratio (RdNBR) index by using linear mixed-effects models and boosted regression trees, based on data from 28 large fires and 668 field measurements across three countries in southern Europe. The RdNBR clearly reflected the mean height of charred stem and loss of ligneous, living shrub and tree cover during the fire. The paper confirms that remote sensing indices provide an acceptable assessment of fire induced impact on forest vegetation but also highlights there are important between-fire variations due to specific contexts that modify these relationships. These variations can be effectively assessed and should be taken into account in future predictive efforts. Highlights • Significant relationships were found between RdNBR and field measured variables. • The correct design of field inventories is key to assess burn severity. • RdNBR provides acceptable assessments of fire induced impact in the Mediterranean. • Between-fire variations need to be considered in burn severity assessments. [ABSTRACT FROM AUTHOR]

• Comprehensive remote sensing survey of the monumental funerary landscapes of SE Mauritania. • Characterisation of three newly documented prehistoric tumulus fields. • A size-based classification system for simple stone tumuli in the Sahara is presented. • First application of Gini coefficients to the study of prehistoric funerary landscapes. • Discussion of socio-economic dynamics in the pre-Islamic Sahara-Sahel ecotone. The archaeological record of southeastern Mauritania has considerable potential to contribute to longstanding anthropological debates in world prehistory, such as early cereal domestication or the emergence and organization of complex societies, although research remains limited. The archaeological study of funerary rites offers invaluable insights into cultural attitudes towards the dead and the socio-economic dynamics of the living. The materiality of remembrance is a political statement, rooted on the ability of different social agents to mobilize labor pools and networks of obligations within a given cultural framework. As such, understanding the spatial distribution and size variability in burial monuments within funerary landscapes provides insights into the social energetics of ancestor memorialization. To that end, this paper first presents a size-based classification system for pre-Islamic conical tumulus cairns in the highlands of southeastern Mauritania, West Africa, that has much wider applicability within prehistoric Saharan archaeology. Through the first application of Gini coefficients to the study of funerary landscapes, the paper then analyses the spatial distribution and degree of monument size variability within large, spatially bounded clusters ("tumulus fields") in Dhar Tichitt in order to contextualize the socio-economic dynamics of labor mobilization at three previously undocumented tumulus fields in the western Tagant Plateau. [ABSTRACT FROM AUTHOR]

Dexing City is an important mining city in China, abounding in copper ore, lead ore, zinc ore, and other metal resources, and there are two large open-pit mines in its territory, Dexing Copper Mine and Yinshan Mine. The two open-pit mines have been expanding their mining production scale since 2005, with frequent mining activities; and the expansion of the pits and the discharge of solid waste will undoubtedly increase the land use and cause the destruction of vegetation. Therefore, we plan to visualize the change in vegetation cover in Dexing City from 2005 to 2020 and the expansion of the two open-pit mines by calculating changes of the Fractional Vegetation Cover (FVC) in the mining area using remote sensing technology. In this study, we calculated the FVC of Dexing City in 2005, 2010, 2015 and 2020 using data from NASA Landsat Database via ENVI image analysis software, plotted the FVC reclassified maps via ArcGIS, and conducted field investigations in the mining areas of Dexing City. In this way, we can visualize the spatial and temporal changes of vegetation cover in Dexing City from 2005 to 2020, and appreciate the situation of mining expansion and its solid waste discharge in Dexing City. The results of this study showed that the vegetation cover of Dexing City remained stable from 2005 to 2020, as the expansion of mining scale and mine pits was accompanied by active environmental management and land reclamation, setting a positive example for other mining cities. • The change in FVC from 2005 to 2020 was used to visualize the impact of mining expansion in Dexing City, China. • The paper presented Dexing City as an example of a mining city that has achieved "healthy" development. • This paper described the environmental hazards associated with open-pit mining. [ABSTRACT FROM AUTHOR]

Resource-based cities generally face the dual pressures of ecological damage and high carbon emissions. Taking the National Sustainable Development Plan for Resource-Based Cities (NSDP) issued in China as a quasi-natural experiment, this paper for the first time, uses the difference-in-difference method to test the coordinated effect of green expansion and carbon reduction brought by NSDP at the county level using rich remote sensing data. The results show that NSDP has significantly promoted the ecological quality improvement and CO 2 emission mitigation in resource-based cities, and the above coordinated benefits mainly occur in energy, mature, declining, northeastern, and municipal resource-based cities. Through the mechanism analysis, we find that NSDP mainly works by promoting the transformation of industry sector to agriculture, animal husbandry, accommodation and catering, finance, and environment management sectors, which are reflected in output, employment, product yield, land type and land transaction area. Further discussion on the tertiary industry suggests that developing tourism, easing financing constraint and strengthening environment regulation, are the key channels to borne fruit in green expansion and carbon reduction. • Analysis of placed-based policy on resource at the county-level. • Coordinated effects of green expansion and carbon reduction are tested. • Key areas of policy effects are identified. • Industrial structure transformation and its related mechanisms are discussed. [ABSTRACT FROM AUTHOR]

Information about a company's environmental, social and governance (ESG) performance has become increasingly important in the decision-making process of financial institutions. The financial implications of environmental challenges (e.g. water stress), negative social impacts (e.g. health impacts in local communities) or poor corporate governance (e.g. breaching legislation) all continue to increase. Accordingly, there is a need for financial institutions to incorporate information on ESG risks, opportunities and impacts in decisions that relate to risk management, investments, credit, strategy, and reporting. ESG information is typically disseminated through ESG ratings, which combine the three constituents into a single rating, or ascribe them separate scores. The compilation of ESG ratings and the identification of appropriate data sources is an inherently complex process; as such, there is no single standard for data collection or reporting. This has led to a divergence in the underlying data sources used by different rating providers, as well as in the determination of factors that are deemed worthy of measurement in the first place. For example, when assessing a company's environmental impact, one rating provider may rely on company-provided data, while another may incorporate independent third-party assessments. Unfortunately, there is currently no clear mechanism for effectively resolving such disagreements to establish a standardised approach to ESG rating assessments. However, geospatial data and analyses offer several key advantages for ESG assessments, including consistency, the potential for enhanced accuracy, and the ability to identify and assess environmental impacts at a detailed physical asset level, in addition to evaluating the broader spatial context. By incorporating geospatial information (obtained through manually processing remotely sensed data, or by using existing products) rating methodologies can be improved, and disparities can be addressed more effectively. This would enable a more comprehensive understanding of the environmental considerations of ESG assessments, promoting a more informed and precise decision-making process. Within this context, a few institutions (e.g. the University of Oxford, the WWF, and a few others) are pioneering thought leadership around spatial finance, including the assessment of ESG issues utilising geospatial intelligence, but there are no consistent frameworks for incorporating geospatial data into ESG ratings and analysis. This paper explores the opportunity for such a geospatial environmental scoring framework, defining a variety of methods in which open data with broad geographic coverage could be incorporated into ESG analysis, generalisable to a range of assets and sectors. The proposed framework is organised into two categories: localised effects, which directly impact the immediate vicinity of an asset, and delocalised effects, which contribute to global climate change and atmospheric pollution. Sub-scores are defined within these categories, which capture both the localised effects on land use, biodiversity, soils, and hydrology, and the global impacts resulting from atmospheric emissions. The approaches for handling geospatial data to generate both these sub-scores and the final E-score are presented, including a test case, and the complete methodology is made available in open repositories. • First geospatial framework for corporates and investors to assess environmental impacts of a study area. • Transparent and accessible framework, making use of open access data and tools. • Practical description of the framework with an industrial test case. [ABSTRACT FROM AUTHOR]

There is growing global consensus that assessments of land degradation be conducted at regional or smaller scales. Working at this scale allows for locally relevant environmental and land use conditions to be incorporated into the assessment methodology. In this paper, a recently developed regional approach to assessing land degradation in the Hardeveld bioregion of the Succulent Karoo is applied to the Little Karoo region of this biome. The methodology uses fuzzy classification statistical techniques to combine field data with multiple Sentinel-2A and Landsat vegetation indices, as well as regionally modelled soil variables. The resultant habitat condition archetype map values show strong correlation with field observations of perennial plant and bare soil cover in 96 ground-truthed plots. The archetype map indicates that heavily degraded hotspots of high bare ground cover occur throughout the project region, although there is an overall lower average habitat condition in the western half of the Little Karoo. The mean habitat condition archetype value for the entire project area is 0.54 (standard deviation = 0.13), on a continuous scale where 0 and 1 represent the most degraded and pristine extremes, respectively. Random forest regression analysis of various environmental covariates of degradation indicates a strong relationship between habitat condition and topographic as well as rainfall variables, although the limited accuracy of modelled livestock data may obscure the negative impacts of overgrazing. The 30 m resolution habitat condition archetype map builds upon previous degradation research in the Little Karoo and has the potential to inform future conservation, restoration, and rangeland management decisions. The methodology was successfully transferred to a new region and provides an opportunity to improve reporting on the extent of land degradation across South Africa. • Effective transfer of methodology for regional scale assessment of degradation. • Western Little Karoo has lower habitat condition score than the eastern part. • Topography, rainfall, and land use history influence habitat condition score. • 30 × 30 m habitat condition map can inform sustainable land management decisions. [ABSTRACT FROM AUTHOR]

Water clarity is a critical parameter of water, it is typically measured using the setter disc depth (SDD). The accurate estimation of SDD for optically varying waters using remote sensing remains challenging. In this study, a water classification algorithm based on the Landsat 5 TM/Landsat 8 OLI satellite was used to distinguish different water types, in which the waters were divided into two types by using the a d (443)/a p (443) ratio. Water type 1 refers to waters dominated by phytoplankton, while water type 2 refers to waters dominated by non-algal particles. For the different water types, a specific algorithm was developed based on 994 in situ water samples collected from Chinese inland lakes during 42 cruises. First, the R rs (443)/R rs (655) ratio was used for water type 1 SDD estimation, and the band combination of (R rs (443)/R rs (655) - R rs (443)/R rs (560)) was proposed for water type 2. The accuracy assessment based on an independent validation dataset proved that the proposed algorithm performed well, with an R2 of 0.85, mean absolute percentage error (MAPE) of 25.98%, and root mean square error (RMSE) of 0.23 m. To demonstrate the applicability of the algorithm, it was extensively evaluated using data collected from Lake Erie and Lake Huron, and the estimation accuracy remained satisfactory (R2 = 0.87, MAPE = 28.04%, RMSE = 0.76 m). Furthermore, compared with existing empirical and semi-analytical SDD estimation algorithms, the algorithm proposed in this paper showed the best performance, and could be applied to other satellite sensors with similar band settings. Finally, this algorithm was successfully applied to map SDD levels of 107 lakes and reservoirs located in the Middle-Lower Yangtze Plain (MLYP) from 1984 to 2020 at a 30 m spatial resolution, and it was found that 53.27% of the lakes and reservoirs in the MLYP generally show an upward trend in SDD. This research provides a new technological approach for water environment monitoring in regional and even global lakes, and offers a scientific reference for water environment management of lakes in the MLYP. • An optical classification method for estimating water clarity in inland lakes is proposed. • The proposed algorithm had better performance than the other empirical and semi-analytical algorithms. • The proposed algorithm has the ability of mapping inland waters clarity with large-scale. • The 36-years of water clarity levels in the Middle-Lower Yangtze Plain were mapped using Landsat images. [ABSTRACT FROM AUTHOR]

Trees in agroforestry parklands influence crops both through competitive and facilitative mechanism, but the effects are challenging to disentangle due to the complexity of the system with high variability in tree cover structure and species diversity and crop combinations. Focusing on a landscape in central Burkina Faso dominated by Vitellaria paradoxa and Parkia biglobosa , this paper examines how tree cover influences crop yield at landscape scale using satellite data and spatial statistics. Our analysis is based on data from 2017 to 2018 with differences in rainfall to assess the stability in identified relationships. Our findings showed that tree canopy cover and tree density inside the fields tended to decrease crop yield because of competition, but also that these variables when considering the surrounding landscape exerted an opposite effect because of their buffering effects. The explanatory variables representing soil properties did have limited effects on crop yield in this study. These patterns were consistent during the two years of monitoring. Overall, our results suggest that farmers in this area might manage the tree cover in a way that optimizes sustainable yields as canopy cover and tree density in most parklands is below the limits identified here where competition outweight the facilitative effects. • We studied causes of crop yield variability in parklands at landscape scale. • Satellite remote sensing and field data was used in spatial statistics. • Trees in agricultural fields tend to reduce crop yield through resource competition. • Surrounding trees had positive influence on crop yield via buffering effects. • Fewer large trees have more positive influence on crop yield than many small ones. [ABSTRACT FROM AUTHOR]

Impact craters are the predominant geological features on the lunar surface. Data about the craters are crucial for inferring information about surface age, the generation processes of the geological units, and the sequences of geological events. The higher-resolution remote sensing datasets collected by recent lunar missions enable the investigation of impact craters of smaller size and provide more accurate information. This paper presents an investigation of the distribution and population characteristics of impact craters in and around the Orientale Basin based on high-resolution datasets. First, an update to the crater catalogue for the Orientale Basin and its surrounding area is provided, including craters as small as 1 km in diameter. Based on the updated crater catalogue, the crater densities and depth-to-diameter ratios in and around the Orientale Basin are investigated. The inclusion of small craters enables a crater density map with higher resolution, revealing a significantly higher crater density of the study area. Also, the surface age of Orientale Basin is estimated from the size–frequency distribution (SFD) using the new crater catalogue, showing an age of 3.75 Ga using the production function of Neukum et al. (2001), which is in good agreement with previous studies. Finally, distribution patterns of secondary craters around the Orientale Basin are investigated, indicating the Orientale Basin may be caused by an oblique impact with a downrange direction of about 235°–260° and an offset strength towards the direction of about 305°–350°. • An updated crater catalogue (craters ≥ 1 km in diameter) for the Orientale Basin is provided. • CFD analysis shows an age of 3.75 Ga for the Orientale Basin using the production function of Neukum et al. (2001). • Analysis on crater density and depth-to-diameter ratios in and around the Orientale Basin • Investigation on secondary craters around the Orientale Basin provides insights into the impact trajectory of the basin. [ABSTRACT FROM AUTHOR]

Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyper-spectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment. [ABSTRACT FROM AUTHOR]

Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyper-spectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment. [ABSTRACT FROM AUTHOR]

The production of municipal solid waste has increased noticeably during the recent past due to the mounting global population and fast urbanization. Furthermore, its poor management and inappropriate disposal are a global challenge since these have created environmental problems in urban ecosystems. Waste management problems are significantly stalled because of a lack of quality data. This is particularly the case in developing countries where observation infrastructure is weak. Regional waste management strategies involve distributed data, while typical small-scale studies present just point information and in the lack of vital widespread information, the regional studies can't provide dependable results. With the materialization of new techniques such as remote sensing and GIS, regional waste management studies have become easier during the last few decades. Use of these techniques in solid waste management supports in capturing, handling, and transmitting the required information in a prompt and proper manner. These techniques are also useful in acquiring information directly from the remote site at a fairly low cost. This paper provides an overview of remote sensing and GIS techniques used for managing the environmental problems of waste disposal. An indication of the waste disposal problems and its management alongside the ramifications of the analysis is discussed. The background and rationale of the waste disposal problems are detailed. The applications of remote sensing and GIS in waste management modeling are presented and applications of these techniques in diverse case studies worldwide are described. The study revealed that the efficiency of waste management system can be maximized by the proper use of remote sensing and GIS techniques. The study also revealed that these techniques were most commonly used for siting the landfill and waste bin for waste disposal and evaluation of environmental impact of buried waste. Image 1 • An overview of the environmental problems of urban areas is provided. • Remote sensing and GIS applications in waste management modeling are detailed. • The uses of GIS and remote sensing in various cases across the globe are presented. • The analysis shows the efficacy of approach in acquiring data from the remote site. • The techniques are used in capturing, handling, and transmitting the required data. [ABSTRACT FROM AUTHOR]

• Smart bolts with embedded sensor to evaluate the tension with instant status information. • The information is assessed in built-in micro-controller with decision and wireless alarms. • Power needed only for microcontroller to process information and communicate in a network. • Power harvested from radio frequencies to charge batteries and avoid batteries replacement. Bolted joint technology constitutes a critical element in structures safety due to possible bolt tension looseness. The existing axial monitoring methods are mostly visual requiring human intervention. The present paper introduces a new paradigm with a new configuration of a smart bolt capable of monitoring, processing and reporting remotely on the status of its axial tension variation. The information is assessed in a built-in electronic board with decision-making followed by a wireless alarm sent instantly to a receiver to map the health of the bolts within the network. The communication is allowed both ways within the designed network of bolts in a plant. Moreover, the proposed system can be powered by wireless energy transfer, or by battery for power supply maintenance free suitable for these standalone mechatronic devices. This device can be used as a node in a network of bolts remotely monitored in a plant. [ABSTRACT FROM AUTHOR]

Gully erosion leads to the formation of deep and wide channels that increase the risk of soil loss, flooding, and water pollution. In addition, this process reduces the productivity and viability of agricultural land and natural ecosystems. Preventing gully erosion is critical for maintaining ecological balance and preserving natural resources in certain areas. This paper presents a methodology integrating remote sensing and nuclear techniques to study gully erosion. The morphometric characterization of gullies using 360-degree camera photogrammetry was introduced as a new method in erosion research. This approach aims to investigate the suitability of unmanned aerial vehicle and terrestrial photogrammetry for modeling gullies, to study the variability of erosion processes in gullies at a small scale, and to compare the differences in erosion intensity between nearby gullies. The study's objectives include identifying the effective and economical method for gullies monitoring and providing a starting point for controlling and safeguarding gullies. Mainly erosion process was detected in the studied gullies, while deposition was identified at only 2 out of 39 sampling locations. The results showed an average soil redistribution rate of 16.2 t ha−1 yr−1 and coefficients of variation of 32%, 59%, and 91% for three investigated gullies. It was determined that aerial photogrammetry methods were not practical under the conditions prevailing in the study area. Highly detailed 3D models of the gullies were created using 360-degree photogrammetry. It was confirmed that the micro-relief obtained by photogrammetric modeling is an essential contribution to erosion research. The 360-degree camera photogrammetry serves as a reliable tool for analyzing the morphology of gullies and, in perspective, tracking changes in gully systems over time or monitoring the effectiveness of the applied protection measures. • High variability of erosion rates within gullies was identified at a small scale. • The erosion rates were higher on the sidewalls than at the bottom of the gullies. • The slope is a more important erosion factor within gullies than the slope length. • Detailed 3D photogrammetry model allows accurate gullies morphometry and mapping. • Microrelief obtained by a 360-degree camera is a vital input to erosion research. [ABSTRACT FROM AUTHOR]

Topographic correction for lunar optical and thermal infrared remote sensing images is a challenging task. Although geometrical, photometrical or albedo-dependent correction methods have been used in an attempt to eliminate topographic effects in the initial lunar reflectance and emissivity images, these methods did not perform well on lunar steep slopes, large crater interiors or walls. In this paper, a novel neural network topographic correction (NNTC) model that was trained based on the relationships between flat (< 0.1° slope angle) and rugged pixels with similar Kaguya FeO abundance and optical maturity parameters (OMATs) (relative ratio < 0.1%) was applied to the Lunar Reconnaissance Orbiter (LRO) Diviner standard Christiansen feature (CF) image. The topographic correction accuracy is 0.03 μm, and no outliers are produced. Visual comparisons with the previous topographic corrected reflectance and emissivity images indicate that the topographic effects on various slope and aspect pixels were effectively corrected. The excavated anorthosite materials in the highland crater (such as the Jackson, Giordano Bruno and Tycho) interiors, surroundings, ejecta, and ray deposits have very similar NNTC CF values. The crater structures in maria or cryptomaria are identified more clearly. In addition, the NNTC method has the potential to be a universal topographic correction method for lunar optical and thermal infrared images, and it provides reliable data sources and a new method for space weathering correction. [ABSTRACT FROM AUTHOR]

Although aircraft-acquired lidar has proven to be a transformative technology for archaeology in forested regions around the world, drone-acquired lidar systems that could potentially offer higher-resolution imagery at much lower cost remain difficult to deploy in field settings, while the data they produce are prone to large errors and are challenging to process. This paper presents results of a study that employs a new ultra-compact drone-based lidar system, alongside aerial thermal and visible light imaging, to document ancient agricultural landscapes at Picuris Pueblo, New Mexico. We discuss the advantages of this new instrumentation, various approaches to field data collection, and as well as our novel data processing and filtering methods, which collectively offer key methodological advances for archaeological investigations using drone-based lidar. Results reveal extensive remains of a vast system of terraces and stone-built field systems, preserved below the modern piñon-juniper-ponderosa forests of the Picuris Reservation and surrounding areas. These findings offer new perspectives on the scale and intensity of past agricultural activities in this highland region of the American Southwest, while demonstrating the power of combining multiple drone-based remote sensing datasets with detailed surface surveys to aid in the discovery, mapping, and interpretation of archaeological landscapes in forested regions. • Drone-based lidar and thermal survey at Picuris Pueblo reveals vast archaeological landscape. • We detail novel approaches to drone lidar data collection, processing, and visualization. • We offer new perspectives on the extent of agricultural landscape features. • Results raise questions about past subsistence and environment in the Southwest. • Results demonstrate the power of multi-sensor drone surveys in archaeology. [ABSTRACT FROM AUTHOR]

The Tabarit-East tumulus field is a large cluster of funerary monuments in the western Tagant (south ern Mauritania), where fieldwork has long been restricted by security and logistical concerns. In this paper, we use photogrammetry derived from very high-resolution satellite stereo imagery to measure the volumetric geometry of these funerary monuments, in order to quantify labour energetics and calorific intake associated with construction efforts. This is the first time such a study has been attempted on archaeological structures of this size (>3.5m in diameter). Our results indicate that while the smaller tumuli could have been constructed by nuclear family members within a very short time span, the largest monuments would have demanded greater coordination, as we estimate them to have required the mobilisation of more than 200 person/days and over a million kilocalories. We contextualised how past communities may have engaged with these monuments through spatial analyses, such as least-cost pathways and viewsheds. This combined approach demonstrates the potential of remote sensing techniques to facilitate a deeper understanding of cultural heritage in dryland ecosystems. Furthermore, we aim to integrate these desk-based approaches with collaborative, community-centred fieldwork as soon as this becomes a viable option for this region of Mauritania. • First study to use very high resolution satellite imagery to model volumes of small funerary monuments (>3.5m in diameter). • Volumetric data were used to calculate labour energetics and comparative metrics of inequality. • This approach is useful for studying past cultural landscapes where fieldwork is not possible. [ABSTRACT FROM AUTHOR]

Remote sensing provides an effective way for the estimation of regional evapotranspiration, therefore, a reliable scaling-up method from instantaneous evapotranspiration ( ET ) to daily ET is of great importance. In this paper, five commonly used scaling-up methods estimating daily ET from hourly latent heat measurements were compared under different energy and crop growing conditions. Field data collected during the whole growing season at 1.0 h intervals by eddy covariance system in North China were used. Evaporative fraction ( EF ), reference evaporative fraction ( K c ), crop coefficient (modified -K c ) and surface resistance ( r c ) varied slightly during the daytime, which gave a solid theoretical basis for the scaling-up methods based on the constant value of these parameters. In most cases, the EF and K c methods performed better than the others, the relative bias from 9:00–16:00 for the two methods ranged from −11.2% to 5.9% and −6.6% to 4.8%, respectively. The EF method was applicable when surface energy was limited, when R n was less than 50 W m −2 ; the average modified coefficient of efficiency of EF method during the daytime from 9 am to 5 pm was 0.68, which is around twice that of the second best method. The K c and the modified- K c methods were applicable when surface energy was relatively large. The performance of the five methods didn't seem sensitive to variation of leaf area. Overall, the K c and EF methods performed better, especially between 11:00 and 15:00. The results of this paper could be useful for optimisation of ET models. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates estimation of root zone soil moisture using two passive microwave remote sensing datasets, Advanced Microwave Scattering Radiometer – 2 and Soil Moisture Active Passive satellites sensors. The study is focused on two crops, namely rice and wheat for the Indo-Gangetic basin, India, having a dynamic crop and soil type and land use land cover. A total of 21 rice crop and 23 wheat crop locations are chosen from the states of Uttar Pradesh, Madhya Pradesh and Bihar falling in the basin. The root zone soil moisture information is derived by estimating soil wetness index from surface soil moisture at 10 and 40 cm depths using a recursive exponential filter. The soil wetness index based algorithm is implementable even in the absence of ground information for a basin level study. The reference soil moisture dataset is obtained from the Global Land Database Assimilation System – NOAH at 10 and 40 cm depth. The research has also demonstrated significant potential of GLDAS-NOAH soil moisture data in the absence of ground (in-situ) soil moisture data. Of the various factors affecting surface and root zone soil moisture, this work evaluates the control of soil constituents on root zone soil moisture. The Spearman rank correlation coefficient is estimated for characteristic time delay with sand, silt and clay percentage at different locations. Coupling between and trends of surface and root zone soil moisture for rice and wheat crop locations are studied. The accuracy of estimated soil wetness index at 10 and 40 cm from two different satellite sensors at two different acquisition times (ascending and descending passes) is investigated by calculating the coefficient of determination, mean absolute error and mean biased error. This work highlights the significant difference in surface soil moisture estimation by two satellite sensors to derive root zone soil moisture for rice and wheat crops. Coefficient of determination is more (∼0.9) for SMAP derived soil wetness index whereas it is lower (∼0.65) for AMSR-2 derived soil wetness index for both crops. Characteristic time delay variation is observed at two different times and at both the depths, with characteristic time delay increasing with depth. Also, at the descending pass characteristic time delay is lower as compared to the ascending pass. A strong relationship between root zone soil moisture and soil texture is observed. For rice crop, a positive correlation with sand and clay is observed for Uttar Pradesh, Madhya Pradesh and Bihar locations having loam and sandy loam as the major soil class. And, for wheat locations, a positive correlation is observed for silt and clay for Uttar Pradesh locations and sand for Madhya Pradesh locations having loam and clay (light) soil texture. This work delivers essential information in understanding sustainable irrigation scheduling and increasing irrigation potential for rice and wheat crop locations. Having the knowledge of all the factors influencing crop cultivation and the derived root zone soil moisture, crop production can be optimized. Graphical abstract Image 10347690 Highlights • Estimating root zone soil moisture from AMSR-2 and SMAP surface soil moisture data. • Correlation between derived root zone soil moisture and soil texture for rice and wheat crops. • Error of estimated soil moisture is higher at morning times than afternoon. • Precise root zone soil moisture estimation (higher R2) from SMAP than AMSR-2 sensor. • Understanding the potential of GLDAS-NOAH as the reference data in absence of in-situ data. [ABSTRACT FROM AUTHOR]

Highlights • We computed spectral parameters starting from VIR level 1B data cubes. • We map-projected the data cubes taking into consideration the geometry of observation at Ceres. • We produced the mineralogic quadrangle maps following a 15-quadrangle scheme. • The basic statistics and qualitative view on the global maps give an overview on the mineralogic diversity of Ceres. Abstract This article presents the spectral parameter maps used in this Surface Composition of Ceres Special Issue. The definition and use of spectral parameters has always played a fundamental role in understanding the properties and composition of a planetary surface. Mapping proper spectral parameters shows the global mineralogical diversity across Ceres. In this work, we discuss the production process of Ceres spectral parameter maps derived by the data of the Visible and Infrared mapping spectrometer (VIR) onboard NASA's Dawn mission. We describe the data processing of the VIR spectra and the procedure to retrieve the geometries (latitude, longitude and illumination angles) of the acquired data. Spectra and geometries are used to project and mosaic this data to produce Geographic Information System-compatible spectral parameters maps of Ceres. An overview of the variability of the data across the quadrangles is given, addressing the specific analysis to each quadrangle mapping paper included in this special issue. [ABSTRACT FROM AUTHOR]

Coastal wetlands are some of the most valuable ecosystems on Earth because they provide many ecological services for coastal security. However, these wetlands are seriously threatened by accelerated climate change and intensive anthropogenic activities. To understand the impacts of land reclamation on landscape change of coastal wetlands and the long-term effects of disturbances of coastal wetlands on their sustainable management, we used time-series Landsat imagery with an object-oriented classification and Digital Shoreline Analysis System to map wetland changes within a reclaimed area in the Pudong District (PD), in Shanghai, China. Our analysis indicated that from 1989 to 2013, 19,793.4 ha of coastal wetlands have been changed to inland wetlands enclosed by a seawall and dike since 1989, thereby cutting off the exchange of sediment and water flux between the wetlands and the coastal ocean. Subsequently, under the increasing threats of anthropogenic activities, the wetland ecosystem collapsed sharply, in a transformation chain of inland wetland (fresh swamp), artificial wetland (agriculture and aquaculture wetland), and non-wetland (urban land). Under this explosive utilization following coastal reclamation, only 8.9% of natural wetlands remain in the reclaimed area, which has experienced an average annual wetland loss rate of 3.8% over the past 24 years. More than 80% of the wetlands have been developed for agricultural, industrial, and urban land uses, leading to an enormous loss of associated ecological services—benefits arising from the ecological functions provided by wetland ecosystems, thereby undermining the coastal protection these wetlands provided. Nevertheless, considerable regeneration of wetlands occurred because of their inherent resilience. This paper addresses the importance of maintaining a balance between economic growth and coastal ecological protection for sustainable management. It proposes a strategy for how ecosystem-based land planning and ecological engineering should be applied to ensure the effective and sustainable management of living shorelines so that the benefits of healthy ecological functions accrue to coastal ecosystems. [ABSTRACT FROM AUTHOR]

Abstract This paper calls attention to the presence of countless ancient paths on the basalt-covered surfaces characteristic of harra landscapes in north-eastern Jordan. These paths have developed over the course of at least the last two millennia, and potentially prior to that, by trafficking of nomadic peoples and animals. These paths facilitated movements through terrains that were otherwise difficult to traverse. Paths can be recognised on high-resolution satellite imagery, which allows for systematic documentation and the reconstruction of potential routes through the landscape. The identification and mapping of these paths is important for better understanding mobility patterns of nomadic peoples who inhabited these desert landscapes in antiquity. Highlights • Ancient paths were identified on harra surfaces in northeastern Jordan. • Paths can be systematically mapped using high resolution satellite imagery. • Paths developed as a result of trafficking of people and animals. • Paths were used to traverse the landscape at least for the last 2000 years. • Routes relate to nomadic exploitation of the environment on a local scale. [ABSTRACT FROM AUTHOR]

This paper introduces VEDAI: Vehicle Detection in Aerial Imagery a new database of aerial images provided as a tool to benchmark automatic target recognition algorithms in unconstrained environments. The vehicles contained in the database, in addition of being small, exhibit different variabilities such as multiple orientations, lighting/shadowing changes, specularities or occlusions. Furthermore, each image is available in several spectral bands and resolutions. A precise experimental protocol is also given, ensuring that the experimental results obtained by different people can be properly reproduce and compared. Finally, the paper also gives the performance of baseline algorithms on this dataset, for different settings of these algorithms, to illustrate the difficulties of the task and provide baseline comparisons. [ABSTRACT FROM AUTHOR]

Scarcity of in situ vegetation data inhibits research and natural resource management in the Sudano-Sahelian zone (SSZ). Satellite and aerial remote sensing (RS) constitute key technologies for improving the availability of vegetation data, and consequently the preconditions for scientific analysis and monitoring. The aim of this paper was to investigate how the hands-on application of RS for vegetation analysis has developed in the SSZ by reviewing the scientific literature published between 1975 and 2014. The paper assesses the usages and the users of RS by focusing on four aspects of the material (268 peer-reviewed articles), including publication details (time of publication, scientific discipline of journals and author nationality), geographic information (location of study areas and spatial scale of research), data usage (application of RS systems and procedures for accuracy assessments), and research topic (scientific objective of the research). Three key results were obtained: i) the application of RS to analyze vegetation in the SSZ has increased consistently since 1977 and it seems to become adopted by a growing number of scientific disciplines; ii) the contribution of African authors is low, potentially signalling a need for an increased transfer of knowledge and technology from developed countries; iii) RS has primarily been used to analyze changes in vegetation productivity and broad vegetation types, whereas its use for studying interactions between vegetation and environmental factors has been relatively low. This calls for stronger collaborative RS research that enables the mapping of additional vegetation variables of high relevance for the environmental problems facing the SSZ. Remotely sensed vegetation data are needed at spatial scales that suits the requirements of both research and natural resource management in order to further enhance the usefulness of this technology. [ABSTRACT FROM AUTHOR]

This paper presents a respiratory frequency system composed by a polymer optical fiber sensor using a smartphone as the interrogator and a remote access to information through a Web server. The system developed a locally-based signal processing method for a dedicated designed application while ensuring remote access using the Thingspeak Web server and application. To check the sensor accuracy, tests with known respiratory rates of 30, 35 and 40 Breaths per Minute (BPM), respectively, are performed using a metronome. The sensor is manually stretched on the metronome beats frequency and the results achieved by the system were of 30.3599, 35.88 and 41.4 BPM. Furthermore, tests are performed in volunteers to check system performance in a real environment, where the volunteers are asked to breathe normally so their respiratory rate could be counted manually and compared to the values delivered by the experimental system in a given period. The error rate verified in the validation experiments are less than 2%. To verify the remote access to information, it is checked whether the respiratory rate data measured by the acquisition system is accordingly sent and visualized by Thingview: the application used in this prototype. The frequency read on the app is the same as measured by the sensor, which shows the suitability of the proposed approach for remote sensing applications with cloud integration. • Local data acquisition using a smartphone and dedicated platform. • Smartphone-integrated optical fiber sensor for real-time respiration monitoring. • Remote Access to information using free web server and application. [ABSTRACT FROM AUTHOR]

Optical sensors based on absorption spectroscopy play a central role in the detection and monitoring of atmospheric trace gases. We here present for the first time the experimental demonstration of OSAS-Lidar on the remote sensing of CH 4 in the atmosphere. This new methodology, the OSAS-Lidar, couples the Optical Similitude Absorption Spectroscopy (OSAS) methodology with a light detection and ranging device. It is based on the differential absorption of spectrally integrated signals following Beer Lambert-Bouguer law, which are range-resolved. Its novelty originates from the use of broadband laser spectroscopy and from the mathematical approach used to retrieve the trace gas concentration. We previously applied the OSAS methodology in laboratory on the 2 ν 3 methane absorption band, centered at the 1665 nm wavelength and demonstrated that the OSAS-methodology is almost independent from atmospheric temperature and pressure. In this paper, we achieve an OSAS-Lidar device capable of observing large concentrations of CH 4 released from a methane source directly into the atmosphere. Comparison with a standard in-situ measurement device shows that the path-integrated concentrations retrieved from OSAS-Lidar methodology exhibit sufficient sensitivity (2 000 ppm m) and observational time resolution (1 s) to remotely sense methane leaks in the atmosphere. The coupling of OSAS-lidar with a wind measurement device opens the way to monitor time-resolved methane flux emissions, which is important in regards to future climate mitigation involving regional reduction of CH 4 flux emissions. [ABSTRACT FROM AUTHOR]

This paper presents results of coordinated ultraviolet and visible wavelength reflectance measurements, X-ray diffraction analyses of mineral components, and micro X-ray fluorescence analyses of Ti concentrations of 13 lunar soil samples (<210 µm) that span a range of maturity and TiO 2 contents. Results of these analyses are used to determine the effects of soil maturity, TiO 2 concentration, and specific mineralogical makeup, especially ilmenite content, on the ultraviolet/visible (UV/VIS) ratio for application to remote sensing observations of the Moon and other airless bodies. We find that measured ilmenite weight percent correlates highly with measured TiO 2 concentrations. Thus, the ilmenite content is a good predictor of TiO 2 concentration. Ilmenite is the main contributor of TiO 2 for soils with greater than about 2 wt.% TiO 2 , so we take the effects of TiO 2 on reflectance spectra to be essentially those of ilmenite. Constraining the data set to eight mature Apollo soils, we find that among the UV/VIS ratios from laboratory-measured spectra, the 321/415 nm ratio shows the best correlation with TiO 2 and ilmenite. Moreover, for soils with similar maturity in the submature to mature range, those with higher TiO 2 have higher 321/415 UV/VIS ratios. Finally, the correlation between TiO 2 content and 321/415 ratio in samples measured in the lab appears weaker than for the same relationship using the Lunar Reconnaissance Orbiter (LRO) Wide Angle Camera (WAC) spectral data for the 321/415 ratio of Apollo ground-truth sites. The correlation between lab-derived 321/415 ratios and TiO 2 content for measured samples improves when low-maturity samples are excluded from the dataset, implying that the LROC WAC spectra at 400 m/pix spatial resolution senses mostly mature soil. [ABSTRACT FROM AUTHOR]

In this paper, detection of the power lines on images acquired by Unmanned Aerial Vehicle (UAV) based remote sensing is carried out using spectral-spatial methods. Spectral clustering was performed using Kmeans and Expectation Maximization (EM) algorithm to classify the pixels into the power lines and non-power lines. The spectral clustering methods used in this study are parametric in nature, to automate the number of clusters Davies-Bouldin index (DBI) is used. The UAV remote sensed image is clustered into the number of clusters determined by DBI. The k clustered image is merged into 2 clusters (power lines and non-power lines). Further, spatial segmentation was performed using morphological and geometric operations, to eliminate the non-power line regions. In this study, UAV images acquired at different altitudes and angles were analyzed to validate the robustness of the proposed method. It was observed that the EM with spatial segmentation (EM-Seg) performed better than the Kmeans with spatial segmentation (Kmeans-Seg) on most of the UAV images. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) plays an essential role for detecting plant water status, estimating crop water needs and optimising irrigation management. Accurate estimates of ET at field scale are therefore critical. The present paper investigates a remote sensing and modelling coupled approach for monitoring actual ET of irrigated wheat crops in the semi-arid region of Tensift Al Haouz (Morocco). The ET modelling is based on a modified Penman–Monteith equation obtained by introducing a simple empirical relationship between surface resistance (r c ) and a stress index (SI). SI is estimated from Landsat-derived land surface temperature (LST) combined with the LST endmembers (in wet and dry conditions) simulated by a surface energy balance model driven by meteorological forcing and Landsat-derived fractional vegetation cover. The proposed model is first calibrated using eddy covariance measurements of ET during one growing season (2015–2016) over an experimental flood-irrigated wheat field located within the irrigated perimeter named R3. It is then validated during the same growing season over another drip-irrigated wheat field located in the same perimeter. Next, the proposed ET model is implemented over a 10 × 10 km 2 area in R3 using a time series of Landsat-7/8 reflectance and LST data. The comparison between modelled and measured ET fluxes indicates that the model works well. The Root Mean Square Error (RMSE) values over drip and flood sites were 13 and 12 W m −2 , respectively. The proposed approach has a great potential for detecting crop water stress and estimating crop water requirements over large areas along the agricultural season. [ABSTRACT FROM AUTHOR]

This contribution presents the boundary integral formulation for numerical computation of time-harmonic radar cross section for 3D targets. Method relies on hybrid edge element BEM/FEM to compute near field edge element coefficients that are associated with near electric and magnetic fields at the boundary of the computational domain. Special boundary integral formulation is presented that computes radar cross section directly from these edge element coefficients. Consequently, there is no need for near-to-far field transformation (NTFFT) which is common step in RCS computations. By the end of the paper it is demonstrated that the formulation yields accurate results for canonical models such as spheres, cubes, cones and pyramids. Method has demonstrated accuracy even in the case of dielectrically coated PEC sphere at interior resonance frequency which is common problem for computational electromagnetic codes. [ABSTRACT FROM AUTHOR]

Groundwater markets improve the agricultural economy by transferring water entitlements from low-efficient users to high-efficient ones to maximize productivity. Aiming at developing an efficient groundwater market, the environmental effects of the market mechanism should be assessed, and a reliable method for monitoring water consumption needs to be employed. Toward this end, this paper proposes three annual smart groundwater market mechanisms to maximize water net benefits, minimize groundwater withdrawal, and precisely measure water consumption in agricultural fields. To guarantee the aquifer's safe yield in each mechanism, a groundwater simulation model (i.e., Groundwater Modeling System (GMS)) is used to control groundwater table drawdown at the end of the planning horizon. In addition, the fields' evapotranspiration (ET) is estimated using Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapo Transpiration at high Resolution with Internalized Calibration (METRIC) algorithm to measure the net groundwater consumption during the market. In this regard, we evaluated the algorithms' performances using observed data from a local lysimeter. They are applied to the Nough plain in Iran to assess the effectiveness of the proposed market framework. The findings illustrate their efficiency in recovering approximately 80% (23.33 million cubic meters (MCM)) of groundwater loss due to overexploitation in the study area and increasing the users' annual benefits by 10.6% compared to the non-market condition. In addition, results imply that the METRIC model approximates daily crop ET with a higher accuracy level than the SEBAL model with RMSE, MAE, and Percentage Error of 0.37 mm/day, 0.32 mm/day, and 14.92%, respectively. This research revealed that the proposed market framework is a powerful tool for reallocating water entitlements and increasing water productivity in arid and semi-arid regions. • A groundwater market is proposed to promote economic and environmental situation. • The uncertainty of available water is incorporated in the proposed methodolgoy • Designed market mechanisms recover nearly 85% of groundwater loss in the study area. • The market framework raises the economic benefit by 128.7 billion rials annually. • METRIC algorithm approximates daily crop evapotranspiration with RMSE of 0.37 mm. [ABSTRACT FROM AUTHOR]

To effectively solve the ill-posed image compressive sensing (CS) reconstruction problem, it is essential to properly exploit image prior knowledge. In this paper, we propose an efficient hybrid regularization approach for image CS reconstruction, which can simultaneously exploit both internal and external image priors in a unified framework. Specifically, a novel centralized group sparse representation (CGSR) model is designed to more effectively exploit internal image sparsity prior by suppressing the group sparse coding noise (GSCN), i.e., the difference between the group sparse coding coefficients of the observed image and those of the original image. Meanwhile, by taking advantage of the plug-and-play (PnP) image restoration framework, a state-of-the-art deep image denoiser is plugged into the optimization model of image CS reconstruction to implicitly exploit external deep denoiser prior. To make our hybrid internal and external image priors regularized image CS method (named as CGSR-D-CS) tractable and robust, an efficient algorithm based on the split Bregman iteration is developed to solve the optimization problem of CGSR-D-CS. Experimental results demonstrate that our CGSR-D-CS method outperforms some state-of-the-art image CS reconstruction methods (either model-based or deep learning-based methods) in terms of both objective quality and visual perception. • A novel image prior model (CGSR) is proposed to exploit image structural sparsity • A hybrid regularization combining CGSR and deep denoiser is proposed for image CS • A SBI algorithm is designed to solve the hybrid regularization image CS problem [ABSTRACT FROM AUTHOR]

The effect of storms on water quality in the Chesapeake Bay has been studied in a patchwork fashion at various sites for short periods of time. In this paper, we use a relationship between MODIS-Terra red light reflectance and total suspended solid (TSS) concentrations in order to develop a fourteen-year time period of TSS estimates during which effects of storms and land cover can be studied over a large portion of the Bay. Exponential fits were most appropriate, resulting in viable reflectance-TSS relationships for the five major Western Shore rivers and the Mainstem of the Bay. Other tributaries were less well-disposed for such relationships due to lack of a large range of TSS concentrations, shallow river beds, or low number of data points. Treating the entire Chesapeake as a single entity and modeling a single reflectance-TSS relationship for the entire estuary produced poorer models with less significance compared to treating each channel separately. Over 2800 rain events were studied in the Lower Western Shore between 2000 and 2014. We found some evidence that higher rainfall amounts correspond to a lower distribution of TSS concentrations 1 day following the event in forested watersheds. At rainfall events of <50 mm, maximum TSS within one day of the storm was highly variable, suggesting that rainfall amounts alone cannot explain variation in TSS levels. Finally, we found value in the use of prediction intervals around TSS estimates, a statistical procedure uncommonly used heretofore with satellite-based estimates but which can help to determine if results are significant or not. [ABSTRACT FROM AUTHOR]

High spatial resolution images as well as image processing and object detection algorithms are recent technologies that aid the study of biodiversity and commercial plantations of forest species. This paper seeks to contribute knowledge regarding the use of these technologies by studying randomly dispersed native palm tree. Here, we analyze the automatic detection of large circular crown (LCC) palm tree using a high spatial resolution panchromatic GeoEye image (0.50 m) taken on the area of a community of small agricultural farms in the Brazilian Amazon. We also propose auxiliary methods to estimate the density of the LCC palm tree Attalea speciosa (babassu) based on the detection results. We used the “Compt-palm” algorithm based on the detection of palm tree shadows in open areas via mathematical morphology techniques and the spatial information was validated using field methods (i.e. structural census and georeferencing). The algorithm recognized individuals in life stages 5 and 6, and the extraction percentage, branching factor and quality percentage factors were used to evaluate its performance. A principal components analysis showed that the structure of the studied species differs from other species. Approximately 96% of the babassu individuals in stage 6 were detected. These individuals had significantly smaller stipes than the undetected ones. In turn, 60% of the stage 5 babassu individuals were detected, showing significantly a different total height and a different number of leaves from the undetected ones. Our calculations regarding resource availability indicate that 6870 ha contained 25,015 adult babassu palm tree, with an annual potential productivity of 27.4 t of almond oil. The detection of LCC palm tree and the implementation of auxiliary field methods to estimate babassu density is an important first step to monitor this industry resource that is extremely important to the Brazilian economy and thousands of families over a large scale. [ABSTRACT FROM AUTHOR]

Chang'E-3 (CE-3) landed in the northwest Mare Imbrium, a region that has not been explored before. Yutu rover that released by CE-3 lander carried the first lunar surface penetrating radar (LPR) for exploring lunar regolith thickness and subsurface shallow geological structures. In this paper, based on the LPR data and the Panoramic Camera (PC) data, we first calculate the lunar surface regolith parameters in CE-3 landing area including its permittivity, density, conductivity and FeO + TiO 2 content. LPR data provides a higher spatial resolution and more accuracy for the lunar regolith parameters comparing to other remote sensing techniques, such as orbit radar sounder and microwave sensing or earth-based powerful radar. We also derived the regolith thickness and its weathered rate with much better accuracy in the landing area. The results indicate that the regolith growth rate is much faster than previous estimation, the regolith parameters are not uniform even in such a small study area and the thickness and growth rate of lunar regolith here are different from other areas in Mare Imbrium. We infer that the main reason should be geological deformation that caused by multiple impacts of meteorites in different sizes. [ABSTRACT FROM AUTHOR]

This paper presents a novel experimental approach to in-situ study of atmospheric phenomena such as nucleation scavenging by biological seeds, bio-droplet dehydration, and bioaerosol's particle scavenging by raindrops. Our methodology is based on the analysis of the dynamical changes of fluorescence signal. We use a remote sensing system based on a homebuilt hyperspectral laser induced fluorescence (LIF) Lidar to measure the transient back-fluorescence and backscattering signals. The spectral line shape of the transient fluorescence associated with an aerosolized tryptophan solution was first analyzed in the laboratory. It then used to study bioaerosol phase transitions between wet and dry conditions. The experiments were first conducted in a dynamic aerosol cell where we repetitively create and monitor the droplets containing bioaerosol cloud starting from its early formation till its total evaporation. The LIF-Lidar was used to simultaneously measure back-fluorescence, scattering and transmission. These measurements were synchronized with the generation of droplets containing bioaerosol and with the monitoring of aerosol's size distribution and ambient conditions. A novel optical receiver design was used to simultaneously detect both back-fluorescence polarization components. Results showed that along with droplet's evaporation process, bioaerosol's fluorescence spectrum exhibit a blue shift, known as the dynamic Stokes-shifts, of ∼2000 cm−1 and an increase in its fluorescence anisotropy. To the best of our knowledge, this is the first report of fluorescence Stokes-shifts and anisotropy within microdroplets containing a biological solution due to wet-dry phase transitions. This method was also used to quantify scavenging of biological particle by raindrops from 100 m. It shows that valuable information can be derived from analyzing the fluorescence spectrum of bioaerosol within a cloud and demonstrate the potential of a LIF-LIDAR remote system to perform in-situ studies of scavenging processes. • Laser induced fluorescence (LIF) LIDAR enables detection of cloud phase changes in-situ. • Bioaerosol dehydration and nucleation-scavenging are monitored by Stokes-shifts. • Real-time analysis of atmospheric bioaerosol scavenging by raindrops: a novel method. • Mie-theory calculations of wet tryptophan cross sections during dynamic evaporation. [ABSTRACT FROM AUTHOR]

With the development of remote sensing technology, significant progress has been made in the evaluation of the eco-environment. The remote sensing ecological index (RSEI) is one of the most widely used indices for the comprehensive evaluation of eco-environmental quality. This index is entirely based on remote sensing data and can monitor eco-environmental aspects quickly for a large area. However, the use of RSEI has some limitations. For example, its application is generally not uniform, the obtained results are stochastic in nature, and its calculation process cannot consider all ecological elements (especially the water element). In spite of the widespread application of the RSEI, improvements to its limitations are scarce. In this paper, we propose a new index named the remote sensing ecological index considering full elements (RSEIFE). The proposed RSEIFE is compared with commonly used evaluation models such as RSEI and RSEI LA (Remote Sensing Ecological Index with Local Adaptability) in several types of study areas to assess the stability and accuracy of our model. The results show that the calculation process of RSEIFE is more stable than those of RSEI and RSEI LA , and the results of RSEIFE are consistent with the real eco-environment surface and reveal more details about its features. Meanwhile, compared with RSEI and RSEI LA , the results of RSEIFE effectively reveal the ecological benefits of both water bodies themselves and their surrounding environments, which lead to more accurate and comprehensive basis for the implementation of environmental protection policies. [Display omitted] • We propose a new ecological index for land surface eco-environment simulation. • The instability and incompleteness of the RSEI, RSEI LA calculation was solved. • RSEIFE has stability and versatility in cities with different ecological contexts. • RSEIFE can monitor eco-environmental of cities with abundant water bodies. [ABSTRACT FROM AUTHOR]

Atmospheric Aerosol Optical Depth (AOD), derived from polar-orbiting satellites, has shown potential in PM 2.5 predictions. However, this important source of data suffers from low temporal resolution. Recently, geostationary satellites provide AOD data in high temporal and spatial resolution. However, the feasibility of these data in PM 2.5 prediction needs further study. In this paper, we analyzed the impact of AOD derived from Himawari-8 in PM 2.5 predictions. Moreover, by combining wavelet, machine learning techniques, and minimum redundancy maximum relevance (mRMR), a novel hybrid model was proposed. The results showed that AOD missing rate over Yangtze River Delta region is the highest in Nanjing, Hefei, and Maanshan. In addition, missing rates are the lowest in winter and summer (∼80%). Moreover, we found that considering AOD, as an auxiliary variable in the model, could not improve the accuracy of PM 2.5 predictions, and in some cases decreased it slightly. In comparison with other models, our proposed hybrid model showed higher prediction accuracy, R2 is improved by 11.64% on average, and root mean square error, mean absolute error, and mean absolute percentage error is reduced by 26.82%, 27.24%, and 29.88% respectively. This research provides a general overview of the availability of Himawari-8 AOD data and its feasibility in PM 2.5 predictions. In addition, it evaluates different machine learning approaches in PM 2.5 predictions. Our proposed framework can be used in other regions to predict different air pollutants concentrations and can be used as an aid for air pollution controlling programs. [ABSTRACT FROM AUTHOR]

Multisensory platforms for remote sensing measurements offer the possibility to monitor in real-time the crop health status without affecting the crop and environmental conditions. The concept of the speaking plant approach, and plant response based sensing in general, could be valuable providing a better understanding of the interactions between the microclimate and the physical conditions of the plants. Early detection of plant stress is critical, especially in intensive production systems, in order to minimise both acute and chronic loss of productivity. Non-contact and non-destructive sensing techniques can continuously monitor plants and enable automated sensing and control capabilities. This paper reviews past research and recent advances regarding the sensors and approaches used for crop reflectance measurements and the indices used for crop water and nutrient status detection. The most practical and effective indices are those based on ground reflectance sensors data which are evaluated in terms of their efficiency in detecting plant water status under greenhouse conditions. Some possible applications of this approach are summarised. Although crop reflectance measurements have been widely used under open field conditions, there are several factors that limit the application of reflectance measurements under greenhouse conditions. The most promising type of sensors and indices for early stress detection in greenhouse crops are presented and discussed. Future research should focus on real time data analysis and detection of plant water stress using advanced data analysis techniques and to the development of indices that may not be affected by plant microclimate. [ABSTRACT FROM AUTHOR]

Road information plays an important role in many civilian and military applications. Road centerlines extraction from high-resolution remote sensing images can be used to update a transportation database. However, it is difficult to extract a complete road network from high-resolution images, especially when the color of road is close to that of background. This paper proposes an improved method for road centerlines extraction, which is based on shear transform, directional segmentation, shape features filtering, directional morphological filtering, tensor voting, multivariate adaptive regression splines (MARS) and enhanced broken lines connection. The proposed method consists of five steps. Firstly, directional segmentation based on spectral information and shear transform is used to segment the images for obtaining the initial road map. Shear transform is introduced to overcome the disadvantage of the loss of the road segment information. Secondly, we perform hole filling to remove the holes due to noise in some road regions. Thirdly, reliable road segments are extracted by road shape features and directional morphological filtering. Directional morphological filtering can separate road from the neighboring non-road objects to ensure the independence of each road target candidate. Fourthly, tensor voting and MARS are exploited to extract smooth road centerlines, which overcome the shortcoming that the road centerlines extracted by the thinning algorithm have many spurs. Finally, we propose an enhanced broken lines connection algorithm to generate a complete road network, in which a new measure function is constructed and spectral similarity is introduced. We evaluate the performance on the high-resolution aerial and QuickBird satellite images. The results demonstrate that the proposed method is promising. [ABSTRACT FROM AUTHOR]