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Vegetation health assessment by using airborne multispectral images throughout crop production cycles, among other precision agriculture technologies, is an important tool for modern agriculture practices. However, to really take advantage of crop fields imagery, specialized analysis techniques are needed. In this paper we present a geographic object-based image analysis (GEOBIA) approach to examine a set of very high resolution (VHR) multispectral images obtained by the use of small unmanned aerial vehicles (UAVs), to evaluate plant health states and to generate cropland maps for Capsicum annuum L. The scheme described here integrates machine learning methods with semi-automated training and validation, which allowed us to develop an algorithmic sequence for the evaluation of plant health conditions at individual sowing point clusters over an entire parcel. The features selected at the classification stages are based on phenotypic traits of plants with different health levels. Determination of areas without data dependencies for the algorithms employed allowed us to execute some of the calculations as parallel processes. Comparison with the standard normalized difference vegetation index (NDVI) and biological analyses were also performed. The classification obtained showed a precision level of about 95 % in discerning between vegetation and non-vegetation objects, and clustering efficiency ranging from 79 % to 89 % for the evaluation of different vegetation health categories, which makes our approach suitable for being incorporated at C. annuum crop's production systems, as well as to other similar crops. This methodology can be reproduced and adjusted as an on-the-go solution to get a georeferenced plant health estimation.

Forests are expected to expand into alpine areas because of climate warming, causing land-cover change and fragmentation of alpine habitats. However, this expansion will only occur if the present upper treeline is limited by low-growing season temperatures that reduce plant growth. This temperature limitation has not been quantified at a landscape scale. Here, we show that temperature alone cannot realistically explain high-elevation tree cover over a >100-km(2) area in the Canadian Rockies and that geologic/geomorphic processes are fundamental to understanding the heterogeneous landscape distribution of trees. Furthermore, upslope tree advance in a warmer scenario will be severely limited by availability of sites with adequate geomorphic/topographic characteristics. Our results imply that landscape-to-regional scale projections of warming-induced, high-elevation forest advance into alpine areas should not be based solely on temperature-sensitive, site-specific upper-treeline studies but also on geomorphic processes that control tree occurrence at long (centuries/millennia) timescales.

Landslides are a major natural hazard, threatening communities and infrastructure worldwide. The mitigation of these hazards relies on the understanding of their causes and triggering processes, which depends directly on soil properties, land use, and their changes over time. In this study, we propose a novel framework to estimate the probability of failure in highly developed urban areas. The framework combines remote sensing and geophysical data to estimate soil properties and land covers. Such estimate properties are then integrated into a hydro-geomechanical model to provide a robust estimate of the probability of failure. To assess the importance and sensitivity of the input parameters to the probability of failure assessment, a sensitivity analysis was performed on the seven main parameters (density, friction angle, cohesion, soil thickness, slope, water recharge and saturated hydraulic conductivity) of the hydro-geomechanical model. Slope angle, soil thickness and cohesion are shown to be the most important parameters. While the slope angle can be derived from high-resolution digital elevation models, soil thickness and cohesion cannot be assessed. To incorporate the variability of these two parameters into the model, seismic noise measurements were performed to estimate soil thickness. Supervised classification of remote sensing data was used to map vegetation type and related root cohesion, which can impact the cohesion significantly. The results show that slopes with relatively thick soil layers (above 2 m) have up to four times higher probability of failure. Slopes with tall vegetation cover, and hence comparably high root cohesion, reduce the probability of failure, particularly when the soil layer is relatively thin (< 3 m). The developed approach makes use of rapid to acquire geophysical and easily to obtain remote sensing data, and hence is transferable to other study sites. This approach may be of particular importance to areas of active vegetation management that may cause considerable changes in landslide hazard maps.

Remote sensing plays an important role for modern geography and environmental science. At the same time, it often stands on a weak epistemological foundation. Remote sensing results are mostly treated as strictly objective, context-independent artifacts. This vastly ignores the human practices that led to these results. Thus, remote sensing data are uncritically incorporated into (environmental) policy decision-making processes without understanding exactly how they were generated. Recent research has been critical of this. In a previous study, I showed that the accuracy of land use results can be increased by class aggregation, while the geographic or environmental meaning of the results suffers. I called this provocatively the "more accurate, less meaningful (MALM)" effect and showed that it exists regardless of the technical level of classification. In this study, I discuss the extent to which MALM can be remedied by choosing an appropriate quality indicator. I show that, to the largest extent conceivable, the quality indicator does not and cannot unveil the effects of socio-technical practices, which are materially inscribed into land use maps. Hence, quality indicators are unable to objectivize the effects of practices and values by the researchers. Consequently, they do not solve the MALM problem. On the contrary, I show that the explicit inclusion of geographic knowledge in quality addresses the MALM effect to the largest extent possible. This reinforces my claim that more attention needs to be paid to considering the values and practices behind remote sensing information. I discuss the results in a broad context and argue that and why critical remote sensing based on critical (physical) geography and science-and-technology studies is vital to better incorporate such results into policymaking. [ABSTRACT FROM AUTHOR]

Oxbow lakes in the Indian subcontinent are niches rich in biodiversity, and contain insights into the palaeoclimate and past settlement patterns of the region. This study examines an erstwhile oxbow lake in Sonipat, Haryana, India, which possibly arose from the former course of River Yamuna. The study first confirms the presence of this lake using satellite imagery to identify its remnants. It also employs various pathways, including elevation data, Corona imagery and Survey of India maps, to discern the path of the lake in today's geography of Sersa. The reason for its disappearance has also been accounted for in this study. Apart from using satellite evidence and GIS, an on-site ground truth survey was also conducted, the results of which are discussed. Finally, further suggestions for research are provided to understand the palaeoclimate of the study site. It is also significant that this would be an understudied avenue of research on such a feature in Sonipat. [ABSTRACT FROM AUTHOR]

Substrate facies monitoring is critical for the understanding of fluvial geomorphologic and ecohydraulic patterns and processes. However, direct substrate measurement is time-consuming and subjected to data sparsity because of small sample, size, and limited data collections within an area of interest, which make it difficult to capture facies patterns. Most new experimental studies focus on mapping substrate based on median grain size of a specific grain size class using automatic or semiautomatic photosieving techniques. This study aimed to develop and apply a method to accurately predict size-mixture facies patterns on exposed riverbeds with minimal ground truth plots (100) using airborne lidar and machine learning. The selected testbed river was a 37.5-km stretch of the regulated lower Yuba River in California, USA, mapped at sub-meter resolution in 2017. First, we designed a grid-by-point grain size sampling method and binned grain sizes into representative mixtures, such as fine or large gravel, to assign subaerial facies labels. Second, we classified facies based on a multivariate cluster analysis. Third, we generated 15 lidar-derived topographic and spectral predictors. Six distinct size-mixture facies were identified from field data and a seventh, pure sand facies, from UAV data. A random forest predictive model with an 86% 10-fold cross-validation accuracy was applied to produce a facies map at the 1.54 m pixel scale. The detrended elevation was identified as the most important variable for predicting facies spatial patterning, followed by baseflow, wetted area proximity, and green lidar intensity. We conclude that machine learning combined with intensity lidar data is highly effective for distinguishing mixed classes of substrates. Ultimately, the new substrate mixture-binning approach also provides novel insights into the arrangement of river sediment facies patterns.

Advances in visualisation techniques provide new ways for us to explore how we introduce complex topics like remote sensing to non-specialist audiences. Taking inspiration from the popularity of augmented reality (AR) apps, a free, mobile digital AR app titled SatelliteSkill5, has been developed for both Androids and iPhones in Unity AR. SatelliteSkill5 helps users conceptualise remote sensing (RS) theory and technology by showcasing the potential of datasets such as multispectral images, SAR backscatter, drone orthophotography, and bathymetric LIDAR for tackling real-world challenges, with examples tackling many of the United Nations' Sustainable Development Goals (SDGs) as the focus. Leveraging tried and tested pedagogic practices such as active learning, game-based learning, and targeting cross-curricular topics, SatelliteSkill5 introduces users to many of the fundamental geospatial data themes identified by the UN as essential for meeting the SDGs, imparting users with a familiarity of concepts such as land cover, elevation, land parcels, bathymetry, and soil. The SatelliteSkill5 app was piloted in 12 Irish schools during 2021 and 2022 and with 861 students ranging from 12 to 18 years old. This research shows that both students and teachers value learning in an easy-to-use AR environment and that SDGs help users to better understand complex remote sensing theory. [ABSTRACT FROM AUTHOR]

With the continuous improvement in the volume and spatial resolution of remote sensing images, the self-supervised contrastive learning paradigm driven by a large amount of unlabeled data is expected to be a promising solution for large-scale land cover classification with limited labeled data. However, due to the richness and scale diversity of ground objects contained in remote sensing images, self-supervised contrastive learning encounters two challenges when performing large-scale land cover classification: (1) Self-supervised contrastive learning models treat random spatial–spectral transformations of different images as negative samples, even though they may contain the same ground objects, which leads to serious class confusion in land cover classification. (2) The existing self-supervised contrastive learning models simply use the single-scale features extracted by the feature extractor for land cover classification, which limits the ability of the model to capture different scales of ground objects in remote sensing images. In this study, we propose a contrastive learning network with Geography Feature space joint negative sample Correction (GFCNet) for land cover classification. To address class confusion, we propose a Geography Feature space joint negative sample Correction Strategy (GFCS), which integrates the geography space and feature space relationships of different images to construct negative samples, reducing the risk of negative samples containing the same ground object. In order to improve the ability of the model to capture the features of different scale ground objects, we adopt a Multi-scale Feature joint Fine-tuning Strategy (MFFS) to integrate different scale features obtained by the self-supervised contrastive learning network for land cover classification tasks. We evaluate the proposed GFCNet on three public land cover classification datasets and achieve the best results compared to seven baselines of self-supervised contrastive learning methods. Specifically, on the LoveDA Rural dataset, the proposed GFCNet improves 3.87% in Kappa and 1.54% in mIoU compared with the best baseline. [ABSTRACT FROM AUTHOR]

Until recently, the development of a global geography of floods was challenged by the fragmentation and heterogeneity of in situ data and the high costs of processing large amounts of remote sensing data. Such geography would facilitate the exploration of large‐scale drivers of flood extent and timing including wide latitudinal, climate, and topographic effects. Here we used a monthly data set spanning 30 years (Global Surface Water) to develop a worldwide geographical characterization of slow floods (1‐degree grid), weighting the relative contribution of seasonal, interannual, and long‐term fluctuations on overall variability, and quantified precipitation‐flooding delays where seasonality dominated. We explored the dominance of different flooding timings across five main Köppen‐Geiger climates and seven topography classes derived from modeled water table depths (i.e., hydro‐topography) to contribute top‐down insight about the most salient, cross‐regional flooding patterns and their likely large‐scale drivers. Our results showed that, globally, the mean extent of floods averaged 0.48% of the global land area, predominantly associated with hydro‐topography (>2× more extensive in flatter landscapes). Climate drove flood timings, with predictable, seasonally dominated fluctuations in cold regions, interannual, and mixed patterns in temperate climates, and more irregular (higher variability) and unpredictable (less seasonal) patterns in arid regions. Net gains of flooded area dominated temporal variability in 9% of the cells including boreal clusters likely affected by warming trends. We propose that this new geographical perspective of floods can aid different avenues of hydrological research in the upscaling and extrapolation of field studies and the parsimonious representation of floods in hydroclimatic models. Key Points: Topography is more important than climate in explaining the global distribution of floodsFloods are more seasonal in equatorial and boreal climates and less seasonal in arid lowlands and the subtropical and temperate beltOver the last 30 years, near a tenth of the Earth's land experienced long‐term flooding increases, while less than 2% saw net decreases [ABSTRACT FROM AUTHOR]

This research is focused on a review of geospatial technologies (such as Remote Sensing and Geographic Information Systems technologies) in monitoring vegetation land use land cover (LULC) conservation. This study is considered very apt and timely because of the ecological challenges and intricacies associated with the paradoxical phenomenon chracterized by conflicting land use practices of urban development as well as the conservation of vegetation LULC in many parts of the world today. It is also imperative to note that the literature reviewed for this research is rich, diversified and developed based on relevant themes in the specific field of study. Relevant literature on the applications of geo-information technologies, and the conservation of vegetation were specifically sourced from internationally recognized academic publications, electronic data sources and other educational websites. This study makes a clarion call towards the conservation of degraded vegetation environments and proffers solutions towards the actualization of environmental sustainability of these fragile ecosystems.

This study aims to present and analyze the time series of the snow parameters focusing on representative geographical areas of the Eastern Mediterranean (i.e., Greece and Italy) and to examine their seasonal variability, in terms of region and geography. The satellite retrievals were firstly validated against in-situ retrievals for 67 common days, with a mean bias equal to −0.018 cm, with a near-Gaussian distribution, showing the good performance of the satellite snow detection. The satellite-based analysis resulted in increasing trends of snow water equivalent, attributed to the enhanced values between 2000 and 2009; however, decreasing trends are found starting from 2010 until now of −1.79 × 10−17 and −2.31 × 10−18 over the two representative areas of Greece (e.g., Thessaloniki and Kozani). A similar pattern is found for the snow water equivalent in the Italian study area, with a decreasing trend of −4.45 × 10−18. The presented results contribute to a better understanding of the spatial snow distribution and the snow coverage seasonality that could be crucial for the long-term groundwater management, by combining snow data trends from in-situ data and satellite statistics. [ABSTRACT FROM AUTHOR]

Landscape archaeology (archaeogeography) is a multidisciplinary study used by prehistorical, classic, and historical archaeologists. Archaeogeography deals with the study of how people have shaped and used their environment throughout history. It focuses on the relationship between the material culture of a given community and the changes it introduces in its spatial environment. Traditionally search for archaeological sites is mainly carried out using surface research, i.e., observation of ploughed fields in spring and autumn. However, the percentage ratio of the area covered by forests to the total area of the countries can reach up to even several dozen per cent. This is where archaeogeography aided with modern remote sensing data and processing techniques can be most useful. In this article, the authors describe the problems occurring while searching for anthropological objects in forest areas. In this first part of the two-part series, the authors present a non-invasive method based on widely available remote sensing and historical data that can be used for remote prospection or archaeological sides. The authors also present methods of field verification and critically describe the limitations and advantages of this method. [ABSTRACT FROM AUTHOR]

Advances in visualisation techniques provide new ways for us to explore how we introduce complex topics like remote sensing to non-specialist audiences. Taking inspiration from the popularity of augmented reality (AR) apps, a free, mobile digital AR app titled SatelliteSkill5, has been developed for both Androids and iPhones in Unity AR. SatelliteSkill5 helps users conceptualise remote sensing (RS) theory and technology by showcasing the potential of datasets such as multispectral images, SAR backscatter, drone orthophotography, and bathymetric LIDAR for tackling real-world challenges, with examples tackling many of the United Nations’ Sustainable Development Goals (SDGs) as the focus. Leveraging tried and tested pedagogic practices such as active learning, game-based learning, and targeting cross-curricular topics, SatelliteSkill5 introduces users to many of the fundamental geospatial data themes identified by the UN as essential for meeting the SDGs, imparting users with a familiarity of concepts such as land cover, elevation, land parcels, bathymetry, and soil. The SatelliteSkill5 app was piloted in 12 Irish schools during 2021 and 2022 and with 861 students ranging from 12 to 18 years old. This research shows that both students and teachers value learning in an easy-to-use AR environment and that SDGs help users to better understand complex remote sensing theory.

Shi et al. [[9]] emphasized the significance of remote sensing reflectance as a crucial parameter in the remote sensing inversion of plateau inland water colors. Remote sensing has played a pivotal role in advancing the fields of archaeology, geography, and earth sciences, offering new perspectives and unparalleled opportunities for research, analysis, and interpretation within these disciplines. [Extracted from the article]

Mainland China has a poor distribution of meteorological stations. Existing models' estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature, and low for relative humidity and wind speed (few studies reported). This study compared the typical generalized additive model (GAM) and autoencoder-based residual neural network (hereafter, residual network for short) in terms of predicting three meteorological parameters, namely air temperature, relative humidity, and wind speed, using data from 824 monitoring stations across China's mainland in 2015. The performance of the two models was assessed using a 10-fold cross-validation procedure. The air temperature models employ basic variables such as latitude, longitude, elevation, and the day of the year. The relative humidity models employ air temperature and ozone concentration as covariates, while the wind speed models use wind speed coarse-resolution reanalysis data as covariates, in addition to the fundamental variables. Spatial coordinates represent spatial variation, while the time index of the day captures time variation in our spatiotemporal models. In comparison to GAM, the residual network considerably improved prediction accuracy: on average, the coefficient of variation (CV) R² of the three meteorological parameters rose by 0.21, CV root-mean square (RMSE) fell by 37%, and the relative humidity model improved the most. The accuracy of relative humidity models was considerably improved once the monthly index was included, demonstrating that varied amounts of temporal variables are crucial for relative humidity models. We also spoke about the benefits and drawbacks of using coarse resolution reanalysis data and closest neighbor values as variables. In comparison to classic GAMs, this study indicates that the residual network model may considerably increase the accuracy of national high spatial (1 km) and temporal (daily) resolution meteorological data. Our findings have implications for high-resolution and high-accuracy meteorological parameter mapping in China. [ABSTRACT FROM AUTHOR]

The present research paper aims to study and shed light on the spatial change of vegetation coverage in the Algerian steppe region. The investigation is based on remote sensing and geographic information systems through the analysis of satellite visuals in calculating the vegetation coverage index (Ndvi) for three different periods 1985 and 2022, with consideration to remote sensing and information systems. Geography is among the most important digital means that work to collect, analyze and study the geographical field in all its dimensions, and it also works to follow up on the changes taking place in it. The latter shows the most important changes in the vegetation cover in the study area. [ABSTRACT FROM AUTHOR]

The universal remote sensing image processing software focuses on the preprocessing of remote sensing data, and it is used to implement the monitoring and evaluation functions of the ecological environment such as drought and flood. The operation is complex. In order to simplify the work process of natural geographical environment monitoring and assessment, GIS technology and data mining technology were combined in this paper. With monitoring and evaluation as the core, a decision tree model was constructed. Taking the Dongting Lake as an example, the scale and characteristics of its texture were studied, and combined with other ancillary data, the Dongting land information was explored and the land types were classified. Finally, a conclusion was reached: The algorithm designed in this paper can be applied to the land types of the natural geographical environment. The classification model has a positive guiding role in the judgment of environmental monitoring and assessment. [ABSTRACT FROM AUTHOR]

Objectives: Intelligent interpretation based on high-resolution remote sensing is an important way to realize the fine generation and rapid update of geographic information. Based on the background of fine-accurate geographical application using remote sensing, this paper analyzes the limitations of remote sensing information products in production and application. We explain the necessity of remote sensing serving geographical research and the key of coupling the fine shape of the map and the accurate content of the spectrum. Methods: Based on the basic understanding that geography guides the research of intelligent remote sensing, we first put forward the development direction and technical ideas of high-resolution remote sensing geoscience analysis based on fine geographical scenes. Then, we propose an intelligent computing mode via the space-time/satellite-ground collaboration. Furthermore, we take the evaluation of rocky desertification cultivated land in Guanling County, Guizhou Province, China as an application case. Results: Through this case study, three basic models, namely zoning-stratified perception, spatiotemporal synergistically inversion and multi-granular decision-making, are used to show how to carry out fine-accurate application by high-resolution remote sensing collaborative computing in complex mountain areas from a comprehensive perspective of refinement, quantification and topicalization. Conclusions: Combined with the previous work experience and practical cognition, several key scientific problems are discussed, such as spatial expression using irregular grids, multimodal reconstruction of temporal features, multi-source un‐ certainty analysis and iterative optimization guided by uncertainty. We give some potential study directions and research ideas in order to establish a more complete and feasible theoretical system and explore the development paths for the research on intelligent remote sensing under the guidance of geography and the fine-accurate geographical application with remote sensing data. [ABSTRACT FROM AUTHOR]

Drones have equipped geographers with the capacity to collect high-quality geospatial data at multiple spatial, spectral, and temporal resolutions. Although the adoption of drones is increasing across geography, knowledge of those using this technology and their practices is limited. The purpose of this article is to understand who is using drones in geography, how they are using them, and what future opportunities exist. We collected data from eighty-eight survey respondents, predominantly based in the United States but a handful from Australia, Canada, the European Union, and the United Kingdom. The findings from our Web-based survey show that about 85 percent of geographers using drones are White. Female respondents made up only about 30 percent of respondents, although they represented about 75 percent of the eighteen to twenty-four age group. Although the word drone has a negative connotation, most users (∼38 percent) prefer it, followed by unmanned aerial vehicle (∼21 percent) and unmanned aerial systems (∼19 percent). Only 22 percent of geographers have more than six years of drone experience, suggesting the rapid growth in use and popularity among geographers. Off-the-shelf drones are the most desirable, perhaps due to their low cost and ease of use. Overall, drones in geography are considered positive and have introduced a new era of small extent geospatial analyses. [ABSTRACT FROM AUTHOR]

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Human outbreaks with avian influenza have been, so far, constrained by poor viral adaptation to non-avian hosts. This could be overcome via co-infection, whereby two strains share genetic material, allowing new hybrid strains to emerge. Identifying areas where co-infection is most likely can help target spaces for increased surveillance. Ecological niche modeling using remotely-sensed data can be used for this purpose. H5N1 and H9N2 influenza subtypes are endemic in Egyptian poultry. From 2006 to 2015, over 20,000 poultry and wild birds were tested at farms and live bird markets. Using ecological niche modeling we identified environmental, behavioral, and population characteristics of H5N1 and H9N2 niches within Egypt. Niches differed markedly by subtype. The subtype niches were combined to model co-infection potential with known occurrences used for validation. The distance to live bird markets was a strong predictor of co-infection. Using only single-subtype influenza outbreaks and publicly available ecological data, we identified areas of co-infection potential with high accuracy (area under the receiver operating characteristic (ROC) curve (AUC) 0.991).

The information revolution has been one of the driving forces to the innovation in geography. However, environmental remote sensing, geographic information science and technology, and geocomputing, which once resided within the family of geography, are gradually moving close to information science but are alienated from geography. Therefore, it is necessary to reexamine the interactive convergence of geography and information science, and advance the disciplinary system of geographic science to accommodate the researches with information as subjects and methods. In this paper, we propose to reformulate the relationship between geographic science and information science with a new discipline, i.e., information geography, which not only refers to the geography of information but also a methodological system for studying geography using information science. This paper summarizes the background of information geography's emergence, its definition, and the difference and similarities with other disciplinary concepts. The impact of information geography on geographic paradigm shift is also investigated from the ontological, epistemological, and methodological perspectives. [ABSTRACT FROM AUTHOR]

Soil moisture is vital to understanding many natural systems such as hydrology, climate and weather, erosion, and biology. Current remote sensing provides soil moisture data with a resolution on the scale of tens of kilometers, due to the current constraints of microwave antennae technology. In this study, we present a machine-learning technique based on rule transference that allows us to use a low-resolution but high-accuracy product, obtained through multiple proxies, to produce a high-resolution model of Earth's soil moisture. The low-resolution, high-accuracy microwave product is utilized as a dependent variable in rule-building only. This algorithm is simple, utilizes public data, and overcomes many local issues inherent in other techniques, such as topographic, biographic, temporal, and climatic variations. The final result demonstrates close parity with high-resolution airborne L-band radiometric data. Keywords: Downscaling, soil moisture, random forests, Introduction Soil moisture is a measure of the hydrological component within a finite amount of soil. The variability of soil moisture is highly dependent on the soil properties (Cosby et [...]

Copyright of Turkish Studies - Social Sciences is the property of Electronic Turkish Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conservation of biodiversity and natural resources in a changing climate requires understanding what controls ecosystem resilience to disturbance. This understanding is especially important in the fire-prone Mediterranean systems of the world. The fire frequency in these systems is sensitive to climate, and recent climate change has resulted in more frequent fires over the last few decades. However, the sensitivity of postfire recovery and biomass/fuel load accumulation to climate is less well understood than fire frequency despite its importance in driving the fire regime. In this study, we develop a hierarchical statistical framework to model postfire ecosystem recovery using satellite-derived observations of vegetation as a function of stand age, topography, and climate. In the Cape Floristic Region (CFR) of South Africa, a fire-prone biodiversity hotspot, we found strong postfire recovery gradients associated with climate resulting in faster recovery in regions with higher soil fertility, minimum July (winter) temperature, and mean January (summer) precipitation. Projections using an ensemble of 11 downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) suggest that warmer winter temperatures in 2080-2100 will encourage faster postfire recovery across the region, which could further increase fire frequency due to faster fuel accumulation. However, some models project decreasing precipitation in the western CFR, which would slow recovery rates there, likely reducing fire frequency through lack of fuel and potentially driving local biome shifts from fynbos shrubland to nonburning semidesert vegetation. This simple yet powerful approach to making inferences from large, remotely sensed datasets has potential for wide application to modeling ecosystem resilience in disturbance-prone ecosystems globally.

Difficulties in identifying actual uses of land space from remote sensing-based land cover products often result in lost opportunities to enhance the capacity of applied research on human settlements. In an attempt to address these difficulties, this study investigates how land cover and land use are interrelated with each other and what determines the relationship patterns by analyzing detailed land use and land cover data for two large US metropolitan areas - the five-county Los Angeles and six-county Chicago regions - where a broad spectrum of human settlements, ranging from urban cores to less-urbanized edges, coexist. The analysis shows that the land cover-land use relationship substantially varies not only across regions but across neighborhoods within each region. Through multivariate regression, it is also found that the intraregional variation is highly associated with the neighborhood's stage of urbanization, median housing age, and other development characteristics, suggesting that the relationship pattern can largely be shaped by the history and evolution of urban design/development.

Difficulties in identifying actual uses of land space from remote sensing-based land cover products often result in lost opportunities to enhance the capacity of applied research on human settlements. In an attempt to address these difficulties, this study investigates how land cover and land use are interrelated with each other and what determines the relationship patterns by analyzing detailed land use and land cover data for two large US metropolitan areas - the five-county Los Angeles and six-county Chicago regions - where a broad spectrum of human settlements, ranging from urban cores to less-urbanized edges, coexist. The analysis shows that the land cover-land use relationship substantially varies not only across regions but across neighborhoods within each region. Through multivariate regression, it is also found that the intraregional variation is highly associated with the neighborhood's stage of urbanization, median housing age, and other development characteristics, suggesting that the relationship pattern can largely be shaped by the history and evolution of urban design/development.

In this paper a sensor data fusion approach for characteristics field monitoring, based on time variance control model, is proposed. Distributed sensing and remote processing are the basic features of the employed architecture. In fact, in order to obtain meaningful information about the temporal and spatial variations, which characterize the field levels of some characteristics (electromagnetic, air pollution, seismic, etc), a distributed network of wireless and mobile smart-sensors has been designed.Starting from the partitioned configuration of a monitored geographic areas, this model allows to take into account the different levels of degradation over time in the sensors' performances associated with the different geographic partitions, progressively increasing the severity of the control. To this end, through the introduction of a reliability curve, a revised traditional control chart for variables is proposed.The proposed approach, further constituting an element of the scientific debate, aims to be a useful operational tool for professionals and managers employed in the environment control. [ABSTRACT FROM AUTHOR]

Surface mining has been subject to increased criticism and pressure due to its potential to cause environmental damage. China accounts for more than half of world coal consumption, and this continued demand for coal, combined with low costs, has accelerated surface coal mining worldwide. However, it isn't easy to understand the impact of surface mining in China. Therefore, combined with POI, the Exposed Coal Frequency Index (ECFI) was constructed without any mining information in China. A LandTrendr algorithm was used to identify past mining disturbances and reclamation events (1986–2021). While examining these events, surface destruction due to coal mining in China was revealed. Based on the results, (1) the detection accuracy of statistical surface mining sites by province reaches 91.8%, while the detection accuracy of disturbance and reclamation events exceeds 72.59 %. Also, by comparing the mine data, our results provide more accurate information regarding both time and spatial consistency. (2) 253.61 km2 of surface mining sites were identified in 14 provinces or autonomous regions during 1986–2021. Of these, Inner Mongolia, Ningxia, Shanxi and Xinjiang ranked in the top four occupying 89.0% of the total mining site. (3) China has cumulatively disturbed 545.84 km2 and reclaimed 169.41 km2 including more comprehensive human activities. In the last 12 years, large-scale mining and reclamation have accounted for 51.63% and 75.17% of the cumulative area, respectively. (4) Generally, reclamation in China can be completed within four years. In contrast, reclamation time intervals and the proportion of reclamation varied widely between mining areas. Clearly, this study provides the most accurate data on surface coal mining disturbances in China, which will be beneficial for future studies in spatial geography related to mining. It also includes more policies and management creation for mines. [Display omitted] [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have achieved great success when characterizing remote sensing (RS) images. However, the lack of sufficient annotated data (together with the high complexity of the RS image domain) often makes supervised and transfer learning schemes limited from an operational perspective. Despite the fact that unsupervised methods can potentially relieve these limitations, they are frequently unable to effectively exploit relevant prior knowledge about the RS domain, which may eventually constrain their final performance. In order to address these challenges, this article presents a new unsupervised deep metric learning model, called spatially augmented momentum contrast (SauMoCo), which has been specially designed to characterize unlabeled RS scenes. Based on the first law of geography, the proposed approach defines spatial augmentation criteria to uncover semantic relationships among land cover tiles. Then, a queue of deep embeddings is constructed to enhance the semantic variety of RS tiles within the considered contrastive learning process, where an auxiliary CNN model serves as an updating mechanism. Our experimental comparison, including different state-of-the-art techniques and benchmark RS image archives, reveals that the proposed approach obtains remarkable performance gains when characterizing unlabeled scenes since it is able to substantially enhance the discrimination ability among complex land cover categories. The source codes of this article will be made available to the RS community for reproducible research. [ABSTRACT FROM AUTHOR]

A huge irrigation project is being conducted in the Iranian western border and satellite investigation of the area was initially performed in order to identify archaeological settlement sites before they were threatened by the so-called Garmsiri water project. Because of the diverse geography and the inherited critical war conditions such as mined lands, the investigation of ancient sites in this region is not easy; therefore, satellite-based methods can play an important role in the detection and documentation of archaeological sites. The main hypothesis of this study is that all settlements have internal and external characteristics allowing to detect the presence of archaeological sites. The identification of a series of these characteristics in a spatial area will lead to the discovery of archaeological sites. Three general methods which this study utilizes to identify the location of the site include: 1) Identification using satellite images; 2) Identification using the predictive model based on GIS; 3) Integration of satellite images data applying the prediction model. Thereby, those points having a series of internal and external characteristics related to settlement sites were introduced as potential ancient sites. In the field survey, 57 points were confirmed as settlement sites. The perspective of this study helps archaeologists to explore the surface and subsurface remnants of ancient sites without conventional field-walking survey. [ABSTRACT FROM AUTHOR]