InfraRed Thermography (IRT), a non-invasive, non-contact, and non-destructive testing (NDT) technique, has become an established tool in the assessment of a building’s behavior and energy performance. However, the inherent low spatial resolution of thermal infrared (TIR) cameras has led recent work to fuse thermographic and geometric data to generate accurate 3D representations of buildings encapsulating temperature information. Whilst existing data fusion methods have relied on sensors in fixed relative orientation (RO), the co-registration of independent TIR and RGB blocks using ground control points (GCPs), or the reprojection of TIR images onto additional geometric or parametric models, approaches that directly match multi-modal images remain limited. In principle, if multi-modal tie points were available, it would be possible to directly align the RGB block with the TIR block; however, such matching is extremely challenging due to the substantial differences in radiometric properties. The main contribution of this paper is to demonstrate the applicability of off-the-shelf deep learning-based image matching algorithms, originally trained on mono-modal datasets, to multi-modal matching tasks for InfraRed Thermography 3D-Data Fusion (IRT-3DDF). We conduct a comparative evaluation of the principal algorithms developed in recent years, with particular emphasis on 3D accuracy and computational efficiency, under the hypothesis that, owing to the inherently local nature of the problem they address, these algorithms can generalize from a mono-modal training domain to a multi-modal application domain. The results are benchmarked against existing hand-crafted open-source multi-modal reference methods. Importantly, the proposed method is fully-automatic, obviating the need for sensor pre-calibration, manual co-registration, or associated positioning information. Results demonstrate that DL-based image matching, using pre-trained neural networks outside of their expected training domain, provides a viable approach for IRT-3DDF capable of co-registering blocks of multi-modal images across varying scales, settings, sensors, and subjects. Our results indicate accuracy in 3D is up to seven times better than multi-modal hand-crafted algorithms, while hand-crafted mono-modal methods fail to co-register images in their entirety.

We present the first inverse analysis of 2019 annual mean methane (CH4) emissions in South Africa, focusing on a rectilinear region between the latitudes of 24°- 28° South and the longitudes of 26° - 30° East, containing large commercial agricultural fields, the biggest industries and urbanised environments. Using TROPOMI satellite observations and the cloud-based Integrated Methane Inversion v2 tool, we estimate CH4 emissions at a spatial resolution of 25 × 25 km2. Our analysis employs the EDGAR v8 greenhouse gas inventory as the prior and produces a posterior estimate of 0.65 Tg CH4 yr-1 - 62% lower than the original bottom-up inventory estimate. This substantial discrepancy is discussed in the context of potential uncertainties in emission factors and activity data, particularly for livestock-dominated regions, where relative prediction errors in cattle energy intake calculations can exceed 20%. The exclusion of biogenic sources in bottom-up inventories may also contribute to these differences, though such sources would likely increase, not decrease, anthropogenic emissions. These findings underscore the limitations of bottom-up approaches for accurate CH4 emissions quantification. Our study contributes to global CH4 research by providing the first satellite-based inverse analysis of CH4 observations for South Africa and the first observational evaluation of the country's 2019 gridded CH4 inventory from the Department of Forestry, Fisheries, and the Environment.

Remote sensing technology presents unique possibilities for monitoring agricultural systems, providing accurate information like crop type distribution, crop planting area, crop rotation, etc. Extracted from remote sensing imagery, previous efforts generally produce crop information based on pixel-based classification strategy without considering spatial context of objects. Further incorporation of object-based image analysis in crop type mapping could improve mapping accuracy and reduce disturbance caused by uncertainties caused by pixel-based methods. Here we aim to combine the advantages of pixel-based and object-based approaches for further improving crop type maps over Northeast China based on Sentinel-2 imagery, simple non-iterative clustering (SNIC), random forest classifier and Google Earth Engine platform. The results showed in the majority of cropland, object-based mapping results had higher accuracies and reduced obvious errors at parcel level. Overall accuracies improved by 0.5% and the Kappa coefficient improved by 9% in Sanjiang Plain. However, soybean and maize intercropping with small parcels could be ignored in object-based methods when clustering objects. Therefore, an integration of pixel and object-based approaches was adopted considering different landscapes and patch areas to generate an unprecedentedly accurate crop type map in Northeast China.

Abstract This study investigates land use, land cover (LULC) changes, vegetation health, and drought severity in Rajasthan, India, from 1985 to 2020 using remote sensing techniques. By analyzing satellite imagery with the normalized difference vegetation index (NDVI), temperature condition index (TCI), vegetation condition index (VCI), and NDVI deviation (Dev_NDVI), we assess the spatial and temporal dynamics of the region's landscape and drought conditions. Our findings indicate significant LULC changes, including a decrease in water bodies from 6412.87 to 2248.51 km2 and dense forests by 61.37%, while built‐up areas expanded by 890.50%, reflecting substantial human impact and environmental change. Drought analysis revealed that nearly 49% of the study area experienced moderate to severe drought conditions, with VCI levels below 40%, indicating widespread drought impact across different regions and time periods. The study employs weighted sum analysis of Dev_NDVI, VCI, and TCI to create a detailed drought severity map, revealing areas of severe and extreme drought that necessitate immediate action for sustainable management. The novelty of this approach lies in its integrated multi‐index method for assessing drought over a 35 year period, providing a robust framework for analyzing environmental dynamics and the resilience of ecosystems to climatic stresses. This research emphasizes the value of remote sensing for continuous environmental monitoring and highlights future implications for integrating advanced satellite technologies to enhance drought management strategies, ultimately informing policy decisions for sustainable land and water resource management in Rajasthan and similar semi‐arid regions globally.

The irreducible water saturation of reservoirs seriously restricts the efficient drainage of unconventional energy sources. NMR logging can be used to determine parameters such as total porosity, effective porosity, irreducible water saturation, and permeability, which play an important role in oil and gas identification. T2 cut off value identification using the NMR T2 spectrum is the key to clarifying the irreducible water saturation of unconventional reservoirs. In this paper, saturation and centrifugal T2 spectra of sandstone and coal samples are used to study and calculate the T2 cut off value, with methods including single fractal dimension, multi-fractal dimension, and spectrum morphological discrimination; in addition, the applicability of these three methods in characterizing T2 cut off is discussed. According to the morphological difference of the saturated T2 spectrum, relationships between morphological parameters and the T2 cut off of four types of sample are described. The parameters related to T2 cut off can be divided into two types: (1) the first type includes morphological parameters main peak position (TM) and smaller-pore volume percentage (SPVP); with an increase of T2 cut off, TM increases linearly and SPVP decreases exponentially, and the correlation between SPVP and T2 cut off is stronger than that of TM. (2) The other type includes fractal parameters D2 (fractal dimension of larger pore), D−10 – D10, and D−10/D10; with the increase of T2 cut off, single and multi-fractal dimensions all increase linearly, and the correlation between D2 and T2 cut off is stronger than that of the multi-fractal dimension. When calculating the T2 cut off of samples with macro-pores developed, spectrum morphological methods should be used preferentially, while the fractal dimension discrimination methods need be used for the T2 cut off of samples with developed micro-pores. Then, the T2 cut off value prediction and evaluation system are described. The overall results of this work can provide a theoretical basis for the inversion of bound water content in the original formation.

The determination of precise and reliable interior (IO) and relative (RO) orientation parameters for thermal infrared (TIR) cameras is critical for their subsequent use in photogrammetric processes. Although 2D calibration boards have become the predominant approach for TIR geometric calibration, these targets are susceptible to projective coupling and often introduce error through manual construction methods, necessitating the development of 3D targets tailored to TIR geometric calibration. Therefore, this paper evaluates TIR geometric calibration results obtained from 2D board and 3D field calibration approaches, documenting the construction, observation, and calculation of IO and RO parameters. This includes a comparative analysis of values derived from three popular commercial software packages commonly used for geometric calibration: MathWorks’ MATLAB, Agisoft Metashape, and Photometrix’s Australis. Furthermore, to assess the validity of derived parameters, two InfraRed Thermography 3D-Data Fusion (IRT-3DDF) methods are developed to model historic building façades and medieval frescoes. The results demonstrate the success of the proposed 3D field calibration targets for the calculation of both IO and RO parameters tailored to photogrammetric data fusion. Additionally, a novel combined TIR-RGB bundle block adjustment approach demonstrates the success of applying ‘out-of-the-box’ deep-learning neural networks for multi-modal image matching and thermal modelling. Considerations for the development of TIR geometric calibration approaches and the evolution of proposed IRT-3DDF methods are provided for future work.

Geo-computation is a crucial process in geographic information science that selects geo-computational models and matches geographic data based on a geo-computational task for detecting, predicting, and simulating geographic entities, events, and phenomena. However, current geo-computations require expertise from users to effectively configure the models, data, and procedures for specialized tasks, which particularly poses challenges for users, especially novice users, as their attention is often drawn to technical details rather than computational analysis of the task. Therefore, we propose a systematic descriptive and procedural method driven by knowledge graphs to capture, organize, and process the essential features of components, relationships, and dynamic computational procedures in geo-computations, aiming to reduce manual involvement and assist in automating model selection and data matching. Then, an application prototype system is developed to implement automated geo-computations that are driven by knowledge graphs. Two application cases, namely, soil erosion and soil potential productivity, are computed to illustrate the accessibility of automated geo-computations supported by our proposed method. As demonstrated by the cases studied, the proposed knowledge graph-driven method improves the efficiency of model selection and configuration, enhances the value of open data, and advances integration of data and models for automated geo-computations.

Urban Underground Space (UUS) development plays a crucial role in urban structure optimisation and resilience enhancement. As UUS is inherently sensitive and irreversible, scientific geological evaluations are essential to inform UUS planning and sustainable development. However, challenges such as the absence of unified evaluation criteria and insufficient integration of geological conditions into urban planning exist. To address these issues within Beijing’s urban context, we developed the standard for geological evaluation on UUS resources in the Beijing Plain. This standard defines a systematic evaluation procedure, an index system that incorporates both engineering geological and hydrogeological indicators, index grading and quantification, classification of overall evaluation levels, and evaluation scales that align with the varying requirements of UUS planning. As the first standard of its kind in Beijing, it enhances the consistency and practical applicability of geological evaluations for UUS, offering significant implications for sustainable urban planning and serving as a model for other urban areas.

Introduction & Background Methane (CH4) is a powerful greenhouse gas, leaving both a physical and digital footprint from natural (40%) and human (60%) sources. Its atmospheric concentration has increased from 722 ppb before the industrial age to ~1,922 ppb in recent times. Because of its global warming potential, measuring and monitoring CH4 is crucial to mitigating the impacts of climate change. However, large uncertainties exist in “bottom-up” inventories (a product of activity data based on counts of components, equipment or throughput, and estimates of gas-loss rates per unit of activity for different land uses) reported to the United Nations Framework Convention on Climate Change, making it difficult for policymakers to set emission reduction targets. To address this, we employ causality-constrained machine learning (ML) to combine different gas observations from satellite sensors onboard the TROPOspheric Monitoring Instrument (which measure a digital footprint of human methane-generating behaviour) with outputs from chemical modelling. These are linked with datasets from the national statistics office, meteorology office and a comprehensive survey on quality of life in the emission field, to improve bottom-up estimates of CH4 emissions at the Earth’s surface. Objectives & Approach The research uses mixed methods for collecting and analysing both qualitative and quantitative data for multidisciplinary processing strategies for monitoring CH4 emissions locally and regionally. It also assesses whether additional “digital footprint” variables besides the well-known chemical sources and sinks can be studied to improve our understanding of the CH4 budget. We have conducted an “analytical inversion” of satellite observations of CH4 to obtain emission fluxes. These represent the dependent variable for our ML model, in combination with 22 independent variables (co-occurring trace gases, meteorological fields, land use, land cover, population, livestock, and data from a survey of quality of life from the Gauteng City-Region Observatory, covering a broad range of socio-economic, personal and political issues) with near-real-time Earth observation data, to aid the development of a causality-constrained ML model for the prediction of CH4 fluxes. Relevance to Digital Footprints We make use of not only satellite imagery, but socio-economic, demographic, and environmental data, and repurpose it for environmental sustainability in the context of mitigating climate change. We are creating unique resources in documenting rapid changes in emissions. Conclusions & Implications This research will make important contributions to developing countries with limited resources, enabling them to contribute to the global stocktake towards net-zero by helping policymakers identify geographic regions that are major emitters, enabling them to put measures into place to mitigate emissions.

State and transition models (STMs) are widely used for organizing, understanding, and communicating complex information regarding ecological change. One foundational component of STMs is the representation of the current state of ecological sites (ecosites) delineated by topoedaphic features. Field inventory and assessment techniques used to characterize ecosites are labor-intensive and based on limited sampling in time and space. Remote sensing and Geographic Information System technologies increasingly offer opportunities to generate synoptic, high-resolution characterizations of ecosites in heterogeneous and remote rangelands. Here, we show how advanced remotely-sensed hyperspectral data acquired by the National Ecological Observatory Network can be combined with uncrewed aerial vehicle data within a GIS framework to quantify land cover at scales that inform STMs in Sonoran Desert landscapes in southern Arizona. Using 1 m airborne hyperspectral reflectance data, spectral vegetation and moisture indices (derived from hyperspectral bands and rendered together with the hyperspectral stack), and aerial imagery for ground-truthing, we were able to 1) produce a classification product quantifying some, but not all, plant and soil categories used in STMs and 2) delineate the spatial pattern and areal extent of ecological states on several ecological sites. Our remote sensing-based assessments were then compared to vegetation state maps based on traditional field surveys. We found that with the exception of native vs. nonnative grass ground cover, remote sensing picked up contributions of key ecostate classification variables. Remote sensing products thus have value for planning and prioritizing field surveys and pinpointing areas of concern or novelty. Furthermore, remote sensing approaches more thoroughly encompass greater spatial extents and are ostensibly more cost-effective than traditional field surveys when viewed through the lens of the time-series analyses needed to document whether the ecological states in STMs are stable or in the process or transitioning.

Coastal sediment grain size is an important factor in determining coastal morphodynamics. In this study, we explore a novel approach for retrieving the median sediment grain size (D50) of gravel-dominated beaches using Synthetic Aperture Radar (SAR) spaceborne imagery. We assessed this by using thirty-six Sentinel-1 (C-band SAR) satellite images acquired in May and June 2022 and 2023, and three NovaSAR (S-band SAR) satellite images acquired in May and June 2022, for three different training sites and one test site across England (the UK). The results from the Sentinel-1 C-band data show strong positive correlations (R2≥0.75) between the D50 and the backscatter coefficients for 15/18 of the resultant models. The models were subsequently used to derive predictions of D50 for the test site, with the models which exhibited the strongest correlations resulting in Mean Absolute Errors (MAEs) in the range 2.26–5.47 mm. No correlation (R2 = 0.04) was found between the backscatter coefficients from the S-band NovaSAR data and D50. These results highlight the potential to derive near-real time estimates of coastal sediment grain size for gravel beaches to better inform coastal erosion and monitoring programs.

As the impacts of climate change continue to increase, more and more populations face risks from natural hazards such as droughts, fires and flooding, the monitoring of which all rely on accurate and timely soil moisture data. Many of these areas of increasing risk are located in the global south, where funding for risk management and resilient infrastructure is often low. Therefore, an open source approach to risk management is especially effective - allowing effective action from governments and NGOs with tight budgets. To maximise the impact of this work, a fully open source algorithm and data sources are used. This paper describes a progress update and future direction on the development of an open-source soil moisture estimation product, developed via the data fusion of spaceborne GNSS Reflectometry (GNSS-R) and Synthetic Aperture Radar (SAR) data. Existing GNSS R soil moisture products provide very good temporal resolution but coarse spatial resolution, limiting their usefulness for hydrological modelling and disaster risk management. Therefore, data fusion of the high temporal but coarse spatial resolution GNSS R with lower temporal but very high spatial resolution SAR data. GNSS-R data used in this study is the CYGNSS UCAR/CU Soil Moisture product, alongside SAR backscatter data from Sentinel 1. The validation data product is the SMAP Enhanced L3 Global Daily Soil Moisture product, a well-established radiometry dataset. The GNSS-R and SAR data both have similar responses to the validation data when co-located, indicating that minimal scaling will be required when combining the two soil moisture measurements. However, errors are currently found to be large with an average of 40.8% difference between SAR and the validation data. This is to be expected at this early stage, and a plan for future improvements is laid out.

Synthetic aperture radar (SAR) is traditionally used in the identification, mapping and analysis of petroleum slicks, regardless of their origin. On SAR images, oil slicks appear as dark patches that contrast with the brightness of the surrounding sea surface. This distinction allows for automated detection algorithms to be designed using computer vision methods for objective oil slick identification. Nevertheless, efficient interpretation of the SAR imagery by statistical analysis can be diminished due to the speckle effect present on SAR images, a granular artefact associated with the coherent nature of SAR that visually degrades the image quality. In this study, a quantitative and qualitative assessment of common SAR image despeckling methods is presented, analysing their performance when applied to images containing natural oil slicks. The assessment is performed on Copernicus Sentinel-1 images acquired with various temporal and environmental conditions. The assessment covers a diverse array of filters that employ Bayesian and non-linear statistics in the spatial, transform and wavelet domains, focussing on their demonstrated performance and capabilities for edge and texture retention. In summary, the results reveal that filters using local statistics in the spatial domain produce consistent desired effects. The novel SAR-BM3D algorithm can be used effectively, albeit with a higher computational demand. Supplementary material: Implementations of the speckle filters used in this paper are made available at https://github.com/cavrinceanu/specklefilters under an MIT license. Image statistics data are available in the supplementary table at https://doi.org/10.6084/m9.figshare.13010405 Thematic collection: This article is part of the Remote sensing for site investigations on Earth and other planets collection available at: https://www.lyellcollection.org/cc/remote-sensing-for-site-investigations-on-earth-and-other-planets

Provided is an excel spreadsheet which contains data used to estimate maximum potential shrub cover across a semi-arid grassland in Southern Arizona. Data was obtained using a classified shrub cover (mesquite) map of Las Cienegas National Conservation Area in Southeastern Arizona which was derived using 2017 NAIP imagery which is free available on EarthExplorer. Classified shrub cover map was created using an unsupervised ISO classification technique within ArcGIS. This shrub cover map was upscaled to 100m and a number of topoedaphic spatial layers were overlaid onto this shrub cover layer and their layers extracted per pixel. This data was then analyized within R using a segmented quantile regression approach to identify maximum shrub cover by topoedaphic characteristics at the 95th percent quantile. For sample of quantile code please contact the corresponding author. Topoedaphic variables analyzed in this data set are: Shrub Cover (%) Elevation (m) Slope Inclination (°) Slope Aspect (Cardinal Direction) Value 2 = North Value 3 = East Value 4 = South Value 5 = West Percent Clay between 0 to 5cm (%) Depth to bedrock (cm) Topographic Wetness index (TWI) (unitless with higher values representing more run-on/wetter conditions) Shrub cover was analyzed at the study site level and at the ecological site level.

Comprehensive documentation is the foundation of effective conservation, repair and maintenance (CRM) practices for architectural heritage. In order to diagnose historic buildings and inform decision making, a combination of multi-disciplinary surveys is fundamental to understanding a building’s heritage and performance. Infrared thermography (IRT), a non-contact, non-invasive and non-destructive imaging technique, allows both qualitative and quantitative assessments of temperature to be undertaken. However, the inherent low spatial resolution of thermal imaging has led recent work to fuse thermographic and geometric data for the accurate 3D documentation of architectural heritage. This paper maps the scope of this emerging field to understand the application of IRT and 3D-data fusion (IRT-3DDF) for architectural heritage. A scoping review is undertaken to systematically map the current literature and determine research gaps and future trends. Results indicate that the increasing availability of thermal cameras and advances in photogrammetric software are enabling thermal models to be generated successfully for the diagnosis and holistic management of architectural heritage. In addition, it is evident that IRT-3DDF provides several opportunities for additional data integration, historic building information modelling (H-BIM) and temporal analysis of historic buildings. Future developments are needed to transform IRT-3DDF findings into actionable insights and to apply IRT-3DDF to pressing climate-related challenges, such as energy efficiency, retrofitting and thermal comfort assessments.

<p>Glaciers in the Tibetan Plateau are melting at an unprecedented rate in the context of global warming. Hailuogou (HLG) Glacier, a rapidly receding temperate land-terminating glacier in the southeastern Tibetan Plateau, has been observed to lose mass partly through ice frontal mechanical ablation (i.e., ice collapse).</p> <p>In this study, we present analysis from Uncrewed Aerial Vehicles (UAV) surveys conducted over nine field campaigns to the HLG Glacier, providing evidence of glacier change and frontal ice collapse between 2017 and 2021. Structure from Motion with Multi-View Stereo was applied to produce multi-temporal Digital Surface Models (DEMs) and orthophoto mosaics, from which geomorphological maps and DEMs of Difference were derived to quantify the changes of the glacier snout and the ice loss from frontal ice collapse. Based on that, a linear correlation of Area-Volume for frontal ice collapse was subsequently built. Planet images were used to identify additional ice collapse events (i.e., 2017 to 2021) and to extract time-sequenced glacier extents. ASTER-derived DEMs generated by NASA Ames Stereo Pipeline (ASP) were then differenced to calculate the ice volume changes in the period. Combined with frontal ice collapse events identified from Planet, the contribution of that to the glacier mass balance can be estimated from the established Area-Volume correlation.</p> <p>These analyses reveal that at the margins of the glacier terminus retreated 132.1 m over the period of analysis, and that in the area specifically affected by collapsing (i.e., the glacier collapsed terminus), it retreated 236.4 m. Overall the volume lost in the terminal area was of the order of 184.61 ± 10.32 x 10<sup>4</sup> m<sup>3</sup>, within which the volume change due to observed collapsing events comprises approximately 28%. We show that ice volume changes at the terminus due to a single ice collapse event may exceed the interannual level of volume change, and the daily volume of ice loss due to ice calving exceeds the seasonal and interannual level by a factor of ~ 2.5 and 4. The contribution to the mass balance change of the entire glacier that is attributed to frontal ice collapse is limited (i.e., ranges from 0.48% to 1.12% from 2017 to 2021). However, the mechanical ablation (e.g., frontal ice collapse and subglacial/englacial conduit’s roof collapse) has probably changed the way of losing ice mass to some extent.</p> <p>Our results suggest that the evolution of the HLG Glacier terminus is dominantly controlled by the frontal ice collapse. The projection of the recession rate of the HLG Glacier may well be underestimated if based on surface mass balance alone, as the frontal ice collapsing might be more frequent and larger under the context of warming. If the future evolution of glaciers such as HLG Glacier is to be robustly predicted, the contribution of mechanical ablation should be accounted for by numerical models.</p>

The capacity of aquifers to store water and the stability of infrastructure can each be adversely influenced by variations in groundwater levels and subsequent land subsidence. Along the south bank of the River Thames, the Battersea neighbourhood of London is renovating a vast 42-acre (over 8 million sq ft) former industrial brownfield site to become host to a community of homes, shops, bars, restaurants, cafes, offices, and over 19 acres of public space. For this renovation, between 2016 and 2020, a significant number of bearing piles and secant wall piles, with diameters ranging from 450 mm to 2000 mm and depths of up to 60 m, were erected inside the Battersea Power Station. Additionally, there was considerable groundwater removal that caused the water level to drop by 2.55 ± 0.4 m/year between 2016 and 2020, as shown by Environment Agency data. The study reported here used Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology to analyse the associated land movement for Battersea, London, during this period. The average land subsidence was found to occur at the rate of −6.8 ± 1.6 mm/year, which was attributed to large groundwater withdrawals and underground pile construction for the renovation work. Thus, this study underscores the critical interdependence between civil engineering construction, groundwater management, and land subsidence. It emphasises the need for holistic planning and sustainable development practices to mitigate the adverse effects of construction on groundwater resources and land stability. By considering the Sustainable Development Goals (SDGs) outlined by the United Nations, particularly Goal 11 (Sustainable Cities and Communities) and Goal 6 (Clean Water and Sanitation), city planners and stakeholders can proactively address these interrelated challenges.

Glaciers in the Tibetan Plateau are melting at an unprecedented recently rate in the context of global warming. Time-sequenced landform mapping for the Hailuogou Glacier, a partly debris-covered glacier in the southeastern Tibetan Plateau, shows the detailed evolution of glacier changes as the ice recedes. This study presents four maps of the Hailuogou Glacier tongue, a partly debris-covered glacier in the southeastern Tibetan Plateau, documenting the spatial evolution of glaciological, hydrological, and geomorphological features from 2018 to 2021. Structure from Motion with Multiview Stereo software was applied to images captured by from uncrewed aerial vehicles were used to produce digital surface models and orthophoto mosaics. These datasets were used, and then to identify and map the features based on pre-defined mapping criteria. From 2018 to 2021, the glacier underwent continuous recession, with the terminus retreating, intense crevassing in the lower part of the ablation zone, and continuous expansion of the terminal disintegration area. The recent evolution of the glacier implies that the gradual disintegration of the lower glacier tongue is likely to be exacerbated over the next decades by the continuous climate warming.

Hailuogou (HLG) Glacier, a rapidly receding temperate glacier in the southeastern Tibetan Plateau, has been observed to lose mass partly through ice frontal mechanical ablation (i.e., ice collapse). These events are difficult to monitor and quantify due to their small scale and frequent nature. However, recent developments in Uncrewed Aerial Vehicles (UAV) have provided a possible approach to track their spatiotemporal variation and their impact on the geomorphological evolution of the glacier terminus area. Here, we present analysis from UAV surveys conducted over eight field campaigns to the HLG Glacier, providing evidence of glacier change between October 2017 and November 2020. Structure from Motion with Multi-View Stereo (SfM-MVS) was applied to produce multi-temporal Digital Surface Models (DSMs) and orthophoto mosaics, from which geomorphological maps and DEMs of Difference (DoDs) were derived to quantify glacier changes. These analyses reveal that at the margins of the glacier terminus retreated 132.1 m over the period of analysis, and that in the area specifically affected by collapsing (i.e., the glacier collapsed terminus), it retreated 236.4 m. Overall the volume lost in the terminal area was of the order of 184.61 +/- 10.32 x 10(4) m(3), within which the volume change due to observed collapsing events comprises approximately 28%. We show that ice volume changes at the terminus due to a single ice collapse event may exceed the interannual level of volume change, and the daily volume of ice loss due to ice calving exceeds the seasonal and interannual level by a factor of ~2.5 and 4. Our results suggest that the evolution of the HLG Glacier terminus is dominantly controlled by the frontal ice-water interactions. If the future evolution of glaciers such as HLG Glacier is to be robustly predicted, the contribution of mechanical ablation should be accounted for by numerical models.

Land-use planning identifies the best land-use options by considering environmental, economic, and social factors. Different theoretical land-use plan models can be found in the literature; however, few studies focus on its practical application and particular challenges in different contexts, especially in the Global South. We use expert surveys to explore the feasibility and relevance of integrated land-use planning and data acquisition in developing countries using Paraguay as an example. We identify the challenges of developing land-use plans and strategies to navigate these barriers to speed up its implementation. The results show that it might be difficult to develop an integrated land-use plan in the context of developing countries, mainly due to data availability, lack of political will, lack of stakeholder engagement, and insufficient financial and human resources. We also highlight examples of creative ways in which previous land-use planning projects and studies navigated these challenges, including stakeholder consultations, use of simpler models that required less data, prioritization of data collection, and engagement of decision makers throughout the process. We provide crucial information to improve land-use planning processes in Paraguay and across the Global South in areas with similar contexts and challenges that aim to develop in a more sustainable way.