Land use is central to addressing sustainability issues, including biodiversity conservation, climate change, food security, poverty alleviation, and sustainable energy. In this paper, we synthesize knowledge accumulated in land system science, the integrated study of terrestrial social-ecological systems, into 10 hard truths that have strong, general, empirical support. These facts help to explain the challenges of achieving sustainability in land use and thus also point toward solutions. The 10 facts are as follows: 1) Meanings and values of land are socially constructed and contested; 2) land systems exhibit complex behaviors with abrupt, hard-to-predict changes; 3) irreversible changes and path dependence are common features of land systems; 4) some land uses have a small footprint but very large impacts; 5) drivers and impacts of land-use change are globally interconnected and spill over to distant locations; 6) humanity lives on a used planet where all land provides benefits to societies; 7) land-use change usually entails trade-offs between different benefits—"win–wins" are thus rare; 8) land tenure and land-use claims are often unclear, overlapping, and contested; 9) the benefits and burdens from land are unequally distributed; and 10) land users have multiple, sometimes conflicting, ideas of what social and environmental justice entails. The facts have implications for governance, but do not provide fixed answers. Instead they constitute a set of core principles which can guide scientists, policy makers, and practitioners toward meeting sustainability challenges in land use.
The spatial pattern of sea surface temperature (SST) plays a central role in shaping the climate system, yet the influence of land surface temperature (LST) remains poorly understood. Using a state-of-the-art coupled ocean--land--atmosphere model, we examine the model's response to regional LST perturbations imposed through LST nudging and idealized time-dependent ramp warming simulations. We find that LST warming over South America strengthens the tropical Pacific zonal SST gradient, yielding a more La Niña--like mean state. Enhanced LST increases the zonal contrast in diabatic heating and excites stationary Rossby wave responses, which reinforce alongshore winds and coastal upwelling in the eastern Pacific. This provides a dynamical pathway linking regional land warming to changes in the equatorial Pacific mean state. Similar responses are found for warming over North America, accompanied by North Pacific cooling, and for warming over Central Africa, coupled with tropical Atlantic cooling. In contrast, warming over the Maritime Continent or the Tibetan Plateau does not induce significant SST pattern changes. Historical simulations nudged toward observed LST exhibit cooling in the tropical southeast Pacific, with the tentative implication that uncertainty in LST may contribute to model-simulated SST biases during the historical period.
Ivan Felipe Benavides-Martinez, Justin Guthrie, Jhon Edwin Arias
et al.
Geospatial foundation models generate high-dimensional embeddings that achieve strong predictive performance, yet their internal organization remains obscure, limiting their scientific use. Recent interpretability studies relate Google AlphaEarth Foundations (GAEF) embeddings to continuous environmental variables, but it is still unclear whether the embedding space exhibits a functional or hierarchical organization, in which some dimensions act as specialized representations while others encode shared or broader geospatial structure. In this work, we propose a functional interpretability framework that reverse-engineers the role of embedding dimensions by characterizing their contribution to land cover structure from observed classification behavior. The approach combines large-scale experimentation with a structural analysis of embedding-class relationships based on feature importance patterns and progressive ablation. Our results show that embedding dimensions exhibit consistent and non-uniform functional behavior, allowing them to be categorized along a hierarchical functional spectrum: specialist dimensions associated with specific land cover classes, low- and mid-generalist dimensions capturing shared characteristics between classes, and highgeneralist dimensions reflecting broader environmental gradients. Critically, we find that accurate land cover classification (98% of baseline performance) can be achieved using as few as 2 to 12 of the 64 available dimensions, depending on the class. This demonstrates substantial redundancy in the embedding space and offers a pathway toward significant reductions in computational cost. Together, these findings reveal that AlphaEarth embeddings are not only physically informative, but also functionally organized into a hierarchical structure, providing practical guidance for dimension selection in operational classification tasks.
Rassam Noura, El Alami Younes, Houari Abdelghani
et al.
You should leave 8 mm of space above the abstract and 10 mm after the abstract. The heading Abstract should be typed in bold 9-point Arial. The body of the Faced with the growing frequency and intensity of urban flooding, particularly in Moroccan cities undergoing rapid and poorly planned urbanization, valley management is a strategic issue that is both environmental and territorial in nature. As a territory exposed to complex dynamics, the Fez metropolitan area provides an ideal case study for developing an integrated model for urban valley management, aimed at reducing flood risk and enhancing the ecological value of river environments. The approach adopted is based on an integrated analysis of hydrogeomorphological dynamics, land use patterns, and socio-environmental vulnerability indicators.
Modeling hydraulic hazards, analyzing hydrogeomorphological features, and assessing the ecological functions of valleys have helped identify sustainable development solutions such as riverbed restoration, the creation of buffer zones, and the integration of green infrastructure. The results highlight the need for collaborative territorial governance and prior environmental assessment to guide development proposals. The objective is to contribute to international debates on integrated water resource management and urban resilience by proposing operational recommendations for sustainable land-use planning based on the ecological and functional potential of valleys.
Large-scale land cover maps generated using deep learning play a critical role across a wide range of Earth science applications. Open in-situ datasets from principled land cover surveys offer a scalable alternative to manual annotation for training such models. However, their sparse spatial coverage often leads to fragmented and noisy predictions when used with existing deep learning-based land cover mapping approaches. A promising direction to address this issue is object-based classification, which assigns labels to semantically coherent image regions rather than individual pixels, thereby imposing a minimum mapping unit. Despite this potential, object-based methods remain underexplored in deep learning-based land cover mapping pipelines, especially in the context of medium-resolution imagery and sparse supervision. To address this gap, we propose LC-SLab, the first deep learning framework for systematically exploring object-based deep learning methods for large-scale land cover classification under sparse supervision. LC-SLab supports both input-level aggregation via graph neural networks, and output-level aggregation by postprocessing results from established semantic segmentation models. Additionally, we incorporate features from a large pre-trained network to improve performance on small datasets. We evaluate the framework on annual Sentinel-2 composites with sparse LUCAS labels, focusing on the tradeoff between accuracy and fragmentation, as well as sensitivity to dataset size. Our results show that object-based methods can match or exceed the accuracy of common pixel-wise models while producing substantially more coherent maps. Input-level aggregation proves more robust on smaller datasets, whereas output-level aggregation performs best with more data. Several configurations of LC-SLab also outperform existing land cover products, highlighting the framework's practical utility.
Bharat Sharma, Jitendra Kumar, Nathan Collier
et al.
Human-induced carbon dioxide (CO2) emissions, primarily from fossil fuel combustion and changes in land use and land cover (LULCC), are a key contributor to climate change. As the climate warms, extreme events such as heatwaves, droughts, and wildfires have become more frequent and are projected to intensify throughout the 21st century. These escalating extremes are likely to further disrupt vegetation productivity, known as gross primary production (GPP), and reduce the ecosystem's capacity to absorb carbon. In this study, we employ a global Earth system model to assess how (a) CO2 emissions alone and (b) CO2 combined with LULCC influence the severity, frequency, and duration of GPP extremes. Our results show that negative GPP extremes periods of unexpectedly low carbon uptake are increasing more rapidly than positive extremes, especially under LULCC scenarios. The primary climate factor driving these extremes is soil moisture variability, which is influenced by fluctuations in both precipitation and temperature. The delayed responses of GPP to different climate drivers depend on the specific driver and geographical region. Overall, the highest incidence of GPP extremes arises from the combined influence of water stress, temperature anomalies, and fire-related disturbances.
Saiful Anwar, Ega Rusanti, Dewi Rahmawati Maulidiyah
This study aims to examine whether the adoption of the Global Reporting Initiative (GRI) Standards enhances carbon emission disclosure among banks in Indonesia. Furthermore, it provides empirical evidence that the presence of women on boards moderates the relationship between GRI adoption and carbon emission disclosure. The study was conducted on 40 conventional and Islamic banks listed on the Indonesia Stock Exchange (IDX) during the period 2015–2021. The analysis employs Ordinary Least Squares (OLS) regression, with robustness tests conducted using alternative measurement variables to ensure the consistency of the results. The findings consistently demonstrate that the adoption of GRI Standards positively influences carbon emission disclosure in Indonesian banks. The presence of women on boards promotes banks’ engagement in global climate change agendas, aligning with the implementation of Sustainable Development Goals (SDGs) 5, 8, and 13. This study reinforces stakeholder theory and Critical Mass Theory, indicating that a minimum threshold of female board members is necessary to influence strategic decisions, particularly in encouraging voluntary disclosures such as carbon emission reporting. Notably, the study also finds that carbon emission disclosure is valued by banking stakeholders in Indonesia. Therefore, policymakers are encouraged to establish regulations that mandate GRI adoption and ensure a minimum representation of women in strategic decision-making positions within the banking sector.
Economics as a science, Regional economics. Space in economics
Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume and the total call volume. A semi-supervised fuzzy c-means clustering approach is then applied to infer the land-use types. The method is validated using mobile phone data collected in Singapore. Land use is determined with a detection rate of 58.03%. An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.
Deep learning (DL)-based sea\textendash land clutter classification for sky-wave over-the-horizon-radar (OTHR) has become a novel research topic. In engineering applications, real-time predictions of sea\textendash land clutter with existing distribution discrepancies are crucial. To solve this problem, this article proposes a novel Multisource Semisupervised Adversarial Domain Generalization Network (MSADGN) for cross-scene sea\textendash land clutter classification. MSADGN can extract domain-invariant and domain-specific features from one labeled source domain and multiple unlabeled source domains, and then generalize these features to an arbitrary unseen target domain for real-time prediction of sea\textendash land clutter. Specifically, MSADGN consists of three modules: domain-related pseudolabeling module, domain-invariant module, and domain-specific module. The first module introduces an improved pseudolabel method called domain-related pseudolabel, which is designed to generate reliable pseudolabels to fully exploit unlabeled source domains. The second module utilizes a generative adversarial network (GAN) with a multidiscriminator to extract domain-invariant features, to enhance the model's transferability in the target domain. The third module employs a parallel multiclassifier branch to extract domain-specific features, to enhance the model's discriminability in the target domain. The effectiveness of our method is validated in twelve domain generalizations (DG) scenarios. Meanwhile, we selected 10 state-of-the-art DG methods for comparison. The experimental results demonstrate the superiority of our method.
Traditional land vehicle gravity measurement heavily rely on high-precision satellite navigation positioning information. However, the operational range of satellite navigation is limited, and it cannot maintain the required level of accuracy in special environments. To address this issue, we propose a novel land vehicle gravity anomaly measurement method based on altimeter-assisted strapdown inertial navigation system (SINS)/dead reckoning (DR) integration. Gravimetric measurement trials demonstrate that after low-pass filtering, the new method achieves a fit accuracy of 2.005 mGal, comparable to that of the traditional SINS/global navigation satellite system (GNSS) integration method. Compared with the SINS/DR integration method, the proposed method improves accuracy by approximately 11%.
In any spacecraft landing mission, fuel-efficient precision soft landing while avoiding nearby hazardous terrain is of utmost importance. Very few existing literature have attempted addressing both the problems of precision soft landing and terrain avoidance simultaneously. To this end, an optimal terrain avoidance landing guidance (OTALG) was recently developed, which showed promising performance in avoiding the terrain while consuming near-minimum fuel. However, its performance significantly degrades in the face of external disturbances, indicating lack of robustness. To mitigate this problem, in this paper, a near fuel-optimal guidance law is developed to avoid terrain and achieve precision soft landing at the desired landing site. Expanding the OTALG formulation using sliding mode control with multiple sliding surfaces (MSS), the presented guidance law, named `MSS-OTALG', improves precision soft landing accuracy. Further, the sliding parameter is designed to allow the lander to avoid terrain by leaving the trajectory enforced by the sliding mode and eventually returning to it when the terrain avoidance phase is completed. And finally, the robustness of the MSS-OTALG is established by proving practical fixed-time stability. Extensive numerical simulations are also presented to showcase its performance in terms of terrain avoidance, low fuel consumption, and accuracy of precision soft landing under bounded atmospheric perturbations, thrust deviations, and constraints. Comparative studies against existing relevant literature validate a balanced trade-off of all these performance measures achieved by the developed MSS-OTALG.
Highlights • Global patterns of land use intensity are poorly understood, particularly in the developing world.• The multidimensionality of land use intensity should be considered by jointly using input, output, and system metrics.• A range of cropland intensity metrics exist, but existing data are often uncertain.• Large data gaps remain for grazing and forestry intensity.• Research priorities should include first, better integration of satellite-based and ground based data, second, validating and better documentation of datasets, and third, creation of consistent time series.