Hasil untuk "Land use"

D. Sulla‐Menashe, J. Gray, S. P. Abercrombie et al.

Abstract Land cover and land use maps provide an important basis for characterizing the ecological state and biophysical properties of Earth's land areas. The Collection 5 MODIS Global Land Cover Type product, initially released in 2010, was produced at annual time steps and has been widely used in the land science community. In this paper we describe refinements and improvements, in both the algorithm and the resulting map data sets, that have been implemented in the MODIS Collection 6 Global Land Cover Type product. Unlike the Collection 5 product, which was based on the 17-class International Geosphere-Biosphere Programme (IGBP) legend, the Collection 6 algorithm uses a hierarchical classification model where the classes included in each level of the hierarchy reflect structured distinctions between land cover properties. The resulting suite of nested classifications is combined to create eight distinct classification schemes including the five legacy schemes included in Collection 5, and three new legends based on the FAO-Land Cover Classification System (LCCS) that distinguish between land cover, land use, and surface hydrologic state. The Collection 6 algorithm also incorporates a state-space multitemporal modeling framework based on hidden Markov models that reduce spurious land cover changes introduced by classification uncertainty in individual years. Among other changes, relative to Collection 5, the Collection 6 product includes less area mapped as forests, open shrublands, and cropland/natural vegetation mosaics, and more area mapped as woodlands and grasslands. Accuracy assessment indicates that the Collection 6 product has an overall accuracy of 73.6% for the primary LCCS layer and that the amount of spurious land cover change has been substantially reduced in Collection 6 relative to Collection 5 (1.6% in C6 and 11.4% in C5).

P. Verburg, P. Schot, M. Dijst et al.

Susanna T. Y. Tong, Wenlin Chen

R. Houghton, J. Hackler, K. Lawrence

D. Allan, D. Erickson, J. Fay

J. Harding, E. F. Benfield, P. Bolstad et al.

D. Kleijn, F. Kohler, A. Báldi et al.

C. Dupraz, Hélène Marrou, G. Talbot et al.

Anika Arifin, Duncan DeProfio, Layla Lammers et al.

Environmental policy evaluation frequently requires thoughtful consideration of space and time in causal inference. We use novel statistical methods to analyze the causal effect of land concessions on deforestation rates in Cambodia. Standard approaches, such as difference-in-differences regression, effectively address spatiotemporally-correlated treatments under some conditions, but they are limited in their ability to account for unobserved confounders affecting both treatment and outcome. Double Spatial Regression (DSR) is an approach that uses double machine learning to address these scenarios. DSR resolves the confounding variables for both treatment and outcome, comparing the residuals to estimate treatment effectiveness. We improve upon DSR by considering time in our analysis of policy interventions with spatial effects. We conduct a large-scale simulation study using Bayesian Additive Regression Trees (BART) with spatial embeddings and find that, under certain conditions, our DSR model outperforms standard approaches for addressing unobserved spatial confounding. We then apply our method to evaluate the policy impacts of land concessions on deforestation in Cambodia.

Philipp Schitz, Paolo Mercorelli, Johann C. Dauer

We present a runtime efficient algorithm for autonomous helicopter landings on moving ship decks based on Shrinking-Horizon Model Predictive Control (SHMPC). First, a suitable planning model capturing the relevant aspects of the full nonlinear helicopter dynamics is derived. Next, we use the SHMPC together with a touchdown controller stage to ensure a pre-specified maneuver time and an associated landing time window despite the presence of disturbances. A high disturbance rejection performance is achieved by designing an ancillary controller with disturbance feedback. Thus, given a target position and time, a safe landing with suitable terminal conditions is be guaranteed if the initial optimization problem is feasible. The efficacy of our approach is shown in simulation where all maneuvers achieve a high landing precision in strong winds while satisfying timing and operational constraints with maximum computation times in the millisecond range.

Iris Dumeur, Jérémy Anger, Gabriele Facciolo

Multi-modal Satellite Image Time Series (SITS) analysis faces significant computational challenges for live land monitoring applications. While Transformer architectures excel at capturing temporal dependencies and fusing multi-modal data, their quadratic computational complexity and the need to reprocess entire sequences for each new acquisition limit their deployment for regular, large-area monitoring. This paper studies various dual-form attention mechanisms for efficient multi-modal SITS analysis, that enable parallel training while supporting recurrent inference for incremental processing. We compare linear attention and retention mechanisms within a multi-modal spectro-temporal encoder. To address SITS-specific challenges of temporal irregularity and unalignment, we develop temporal adaptations of dual-form mechanisms that compute token distances based on actual acquisition dates rather than sequence indices. Our approach is evaluated on two tasks using Sentinel-1 and Sentinel-2 data: multi-modal SITS forecasting as a proxy task, and real-world solar panel construction monitoring. Experimental results demonstrate that dual-form mechanisms achieve performance comparable to standard Transformers while enabling efficient recurrent inference. The multimodal framework consistently outperforms mono-modal approaches across both tasks, demonstrating the effectiveness of dual mechanisms for sensor fusion. The results presented in this work open new opportunities for operational land monitoring systems requiring regular updates over large geographic areas.

N. Blüthgen, C. Dormann, D. Prati et al.

Yinuo Zou, Yuefeng Lyu, Guan Li et al.

The relationship between ecosystem services (ESs) and human well-being (HWB) is a central issue of sustainable development. However, current research often relies on qualitative frameworks or indicator-based assessments, limiting a comprehensive understanding of the relationship between natural environment and human acquisition, which still needs to be strengthened. As an element transferred in the natural–society coupling system, carbon can assist in characterizing the dynamic interactions within coupled human–natural systems. Carbon, as a fundamental element transferred across ecological and social spheres, offers a powerful lens to characterize these linkages. This study develops and applies a novel analytical framework that integrates carbon flow as a unifying metric to quantitatively assess the spatiotemporal dynamics of the land use and land cover change (LUCC)–ESs–HWB nexus in Guizhou Province, China, from 2000 to 2020. The results show that: (1) Ecosystem services in Guizhou showed distinct trends from 2000 to 2020: supporting and regulating services declined and then recovered, and provisioning services steadily increased, while cultural services remained stable but varied across cities. (2) Human well-being generally improved over time, with health remaining stable and the HSI rising across most cities, although security levels fluctuated and remained low in some areas. (3) The contribution of ecosystem services to human well-being peaked in 2010–2015, followed by declines in central and northern regions, while southern and western areas maintained or improved their levels. (4) Supporting and regulating services were positively correlated with HWB security, while cultural services showed mixed effects, with strong synergies between culture and health in cities like Liupanshui and Qiandongnan. Overall, this study quantified the coupled dynamics between ecosystem services and human well-being through a carbon flow framework, which not only offers a unified metric for cross-dimensional analysis but also reduces subjective bias in evaluation. This integrated approach provides critical insights for crafting spatially explicit land management policies in Guizhou and offers a replicable methodology for exploring sustainable development pathways in other ecologically fragile karst regions worldwide. Compared with conventional ecosystem service frameworks, the carbon flow approach provides a process-based, dynamic mediator that quantifies biogeochemical linkages in LUCC–ESs–HWB systems, which is particularly important in fragile karst regions. However, we acknowledge that further empirical comparison with traditional ESs metrics could strengthen the framework’s generalizability.

Sonia Dupuis, Nando Metzger, Konrad Schindler et al.

Land surface temperature (LST) is an essential climate variable (ECV) crucial for understanding land-atmosphere energy exchange and monitoring climate change, especially in the rapidly warming Arctic. Long-term satellite-based LST records, such as those derived from the Advanced Very High Resolution Radiometer (AVHRR), are essential for detecting climate trends. However, the coarse spatial resolution of AVHRR's global area coverage (GAC) data limit their utility for analyzing fine-scale permafrost dynamics and other surface processes in the Arctic. This paper presents a new 42 years pan-Arctic LST dataset, downscaled from AVHRR GAC to 1 km with a super-resolution algorithm based on a deep anisotropic diffusion model. The model is trained on MODIS LST data, using coarsened inputs and native-resolution outputs, guided by high-resolution land cover, digital elevation, and vegetation height maps. The resulting dataset provides twice-daily, 1 km LST observations for the entire pan-Arctic region over four decades. This enhanced dataset enables improved modelling of permafrost, reconstruction of near-surface air temperature, and assessment of surface mass balance of the Greenland Ice Sheet. Additionally, it supports climate monitoring efforts in the pre-MODIS era and offers a framework adaptable to future satellite missions for thermal infrared observation and climate data record continuity.

Robert Ndugwa, Dennis Mwaniki

With more than 50 percent of the global population living in urban areas, Sustainable Development Goal 11 on Sustainable Cities and Communities provides a critical lever for us to realise all other SDG goals. This calls for tracking urban spatial development at various levels to facilitate a better understanding of the role, amongst others, of remote sensing data in the field of sustainable urban development and services of general interest to be provided by authorities. Urbanisation patterns may thus be retraced, but also modelled in order to provide evidence for decision makers. Without proper planning, the spatial impacts of urbanisation and subsequent spatial inequalities are more likely to affect disadvantaged groups most. In the last decade of the SDGs, the use of data to inform policies is very critical, and such evidence needs to be anchored in multi-level analysis and ensure vertical and horizontal applications at all governance levels.

Junhao Yu, Jiarun Wei

In this project, we attempt to optimize a landing trajectory of a rocket. The goal is to minimize the total fuel consumption during the landing process using different techniques. Once the optimal and feasible trajectory is generated using batch approach, we attempt to follow the path using a Model Predictive Control (MPC) based algorithm, called Trajectory Optimizing Path following Estimation from Demonstration (TOPED), in order to generalize to similar initial states and models, where we introduce a novel cost function for the MPC to solve. We further show that TOPED can follow a demonstration trajectory well in practice under model mismatch and different initial states.

Kadir Aden, Sadik Aden Dirir

Transportation has a profound effect on the environment, aggravating air pollution, climate change, and natural resource depletion. Additionally, the construction and maintenance of transportation infrastructure contribute to deforestation and habitat loss. Therefore, the aim of this research is to investigate the correlation between c02 emissions, natural resource depletion, trade, FDI inflow and transportation in a chosen number of eastern Asian countries, with a unique perspective of examining the influence of institutional qualities as a moderator among these factors. The analysis involves the utilization of CS-ARDL and dumitrescu-hurlin causality test to examine the data. The findings suggest that institutional qualities have a positive impact on the relationship between c02 emissions and transportation, reversing the negative association. Additionally, trade has a negative correlation with transportation, this can be explained by the fact that weak institutional quality can lead to corruption and a lack of transparency, which can discourage foreign investment and trade in the transportation sector. On the other hand, resource depletion and FDI inflows affect negatively the transportation services in East Asian countries. Therefore, the study highlights the significance of effective governance, regulation, and management of institutions in promoting better transportation planning and coordination, ultimately leading to sustainable transportation service.