Chlorophyll content measured by a Soil and Plant Analyzer Development (SPAD) meter is a key indicator of nitrogen status and photosynthetic capacity in greenhouse-grown tomatoes. However, hyperspectral data collected under greenhouse conditions are strongly affected by leaf posture, illumination variability, high-dimensional redundancy, and multicollinearity, which make small-sample modeling unstable To address these challenges, this study proposes an advanced and lightweight inversion framework integrating multiscale spectral enhancement, deep latent compression, ensemble modeling, and output calibration. A total of 240 leaf spectra (450–950 nm) were processed using Savitzky-Golay (SG) smoothing, fractional-order differentiation (FOD), and Morlet-L7 continuous wavelet transform (CWT) to enhance chlorophyll-sensitive structural features. A convolutional autoencoder (CAE) was used to extract 64-dimensional latent representations, which were fused with red-edge parameters, vegetation indices, and wavelet statistics to form a multi-source feature set. Support vector regression (SVR), gradient boosting regression tree (GBRT), kernel ridge regression (KRR), partial least squares regression (PLSR), and a lightweight Lightformer model were trained, and their out-of-fold (OOF) predictions were integrated through Ridge Stacking, followed by linear calibration. The proposed “Stacking + LinearCal” framework achieved R² = 0.782, RMSE = 1.451, and RPD = 2.156 on the independent test set (n = 72), outperforming all single models. SHAP analysis showed that CAE features, red-edge slope, red-edge inflection point (REIP), and near-infrared tail statistics within 940–950 nm contributed most to prediction. The framework demonstrates high accuracy, stability and interpretability, providing a practical basis for nutrient monitoring in greenhouse tomato production.
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.
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.
Rural Resilience represents the ability of maintaining their core functions when facing internal changes and recovering to original conditions through transformation. Withdrawal from rural homesteads (WRH) is considered as one of critical strategy for rural revitalization of China but its systemic impacts on rural resilience remain underexplored. This study develops a multidimensional resilience evaluation framework encompassing economic, social, cultural, environmental, and governance dimensions through a Delphi-structured expert consultation process with 16 specialists. Considering the complexity of rural socio-ecological systems and the interplay among various dimensions of rural resilience, this paper uses the Fuzzy Decision-Making Trial and Evaluation Laboratory methodology to analyze the causal relationships between 22 resilience indicators. Results reveal economic resilience and social resilience as dominant causal dimensions, with economic diversification promotion and collective land marketization emerging as key drivers. Cultural and environmental dimensions exhibit effect characteristics, demonstrating dependence on economic, social, governance interventions. Notably, villagers’ income improvement and cooperative mechanisms demonstrate high centrality, while indicators related to culture and environment rank as vulnerable nodes. These findings provide policymakers with a prioritized intervention framework, emphasizing the need for economic restructuring coupled with institutional safeguards to balance developmental and conservation objectives in rural spatial reorganization processes.
Bharat Sharma, Jitendra Kumar, Auroop R. Ganguly
et al.
Increasing surface temperature could lead to enhanced evaporation, reduced soil moisture availability, and more frequent droughts and heat waves. The spatiotemporal co-occurrence of such effects further drives extreme anomalies in vegetation productivity and net land carbon storage. However, the impacts of climate change on extremes in net biospheric production (NBP) over longer time periods are unknown. Using the percentile threshold on the probability distribution curve of NBP anomalies, we computed negative and positive extremes in NBP. Here we show that due to climate warming, about 88% of global regions will experience a larger magnitude of negative NBP extremes than positive NBP extremes toward the end of 2100, which accelerate the weakening of the land carbon sink. Our analysis indicates the frequency of negative extremes associated with declines in biospheric productivity was larger than positive extremes, especially in the tropics. While the overall impact of warming at high latitudes is expected to increase plant productivity and carbon uptake, high-temperature anomalies increasingly induce negative NBP extremes toward the end of the 21st century. Using regression analysis, we found soil moisture anomalies to be the most dominant individual driver of NBP extremes. The compound effect of hot, dry, and fire caused extremes at more than 50% of the total grid cells. The larger proportion of negative NBP extremes raises a concern about whether the Earth is capable of increasing vegetation production with growing human population and rising demand for plant material for food, fiber, fuel, and building materials. The increasing proportion of negative NBP extremes highlights the consequences of not only reduction in total carbon uptake capacity but also of conversion of land to a carbon source.
As artificial intelligence (AI) systems become increasingly embedded in critical societal functions, the need for robust red teaming methodologies continues to grow. In this forum piece, we examine emerging approaches to automating AI red teaming, with a particular focus on how the application of automated methods affects human-driven efforts. We discuss the role of labor in automated red teaming processes, the benefits and limitations of automation, and its broader implications for AI safety and labor practices. Drawing on existing frameworks and case studies, we argue for a balanced approach that combines human expertise with automated tools to strengthen AI risk assessment. Finally, we highlight key challenges in scaling automated red teaming, including considerations around worker proficiency, agency, and context-awareness.
Ima Amaliah, Qaisar Ali, Oktofa Yudah Sudrajad
et al.
The emergence of digital financial inclusion has initiated a debate about whether it is the next frontier of sustainable economic growth, especially for developing economies. This study aims to verify these contentions by examining the impact of digital financial inclusion on sustainable economic growth. Accordingly, we created automated teller machines and debit card holders (Debit) as the proxies of digital financial inclusion and examined their impact on sustainable economic growth through the proxies of gross domestic product (GDP) growth and carbon dioxide (CO2) emissions. The empirical data between 2011 and 2020 was retrieved from Indonesia and was analyzed using the generalized method of movements (GMM) technique. The findings confirm that digital financial inclusion’s proxies (automated teller machines and Debit) have a significant positive (strong) effect on GDP growth and a significant positive (moderate) effect on CO2 emissions. This study may motivate developing countries to accelerate their digital financial inclusion initiatives to achieve sustainable economic growth.
The spectacular advancement in information and communication technology (ICT) has shaped the way firms conduct their business, interact and communicate with customers and suppliers along their supply chains (SC). Yet, there exists limited knowledge of how investing in ICT can be leveraged to help firms streamline SC integration and achieve sustainable financial performance (FPF). Drawing on dynamic capability theory, this research delineates the dimensions of internal SC integration (ISI) and external SC integration (ESI) as potential mediators of the relationship between ICT capabilities and a firm’s FPF. A simple random sampling approach and cross-sectional questionnaire survey were used for data collection from 274 manufacturing SMEs in the Mbeya region, Tanzania. Subsequently, the collected data was analysed using Hayes PROCESS macro model 4 to test the study hypotheses. Empirical results indicate an insignificant direct effect of ICT capabilities on a firm’s FPF. However, ICT capabilities significantly and positively affect both ISI and ESI, which in turn play critical mediating roles, positively impacting the firm’s FPF. Essentially, the findings imply that the pathway from ICT capabilities to FPF predominantly transpires through enhanced ISI and ESI. As such, the study underscores the importance for SME managers to invest in ICT capabilities and harness such capabilities to strengthen ISI and ESI as the strategic intermediaries that translate ICT investments into tangible financial outcomes. The study contributes to the existing few empirical studies that establish the potential mediating effect of SC integration in the link between ICT capabilities and a firm’s FPF.
This paper proposes a novel trajectory generation method based on Model Predictive Control (MPC) for agile landing of an Unmanned Aerial Vehicle (UAV) onto an Unmanned Surface Vehicle (USV)'s deck in harsh conditions. The trajectory generation exploits the state predictions of the USV to create periodically updated trajectories for a multirotor UAV to precisely land on the deck of a moving USV even in cases where the deck's inclination is continuously changing. We use an MPC-based scheme to create trajectories that consider both the UAV dynamics and the predicted states of the USV up to the first derivative of position and orientation. Compared to existing approaches, our method dynamically modifies the penalization matrices to precisely follow the corresponding states with respect to the flight phase. Especially during the landing maneuver, the UAV synchronizes attitude with the USV's, allowing for fast landing on a tilted deck. Simulations show the method's reliability in various sea conditions up to Rough sea (wave height 4 m), outperforming state-of-the-art methods in landing speed and accuracy, with twice the precision on average. Finally, real-world experiments validate the simulation results, demonstrating robust landings on a moving USV, while all computations are performed in real-time onboard the UAV.
Destia Novasari, Christine Wulandari, S. P. Harianto
et al.
Through the selecting cropping patterns can be used as a strategy to regulate the success rate of land management it can contribute to deciding the level of the earth’s temperature through the selection of plant species. The high level of the earth’s temperature causes various impacts such as an increase in drought, which affects the agricultural industry, changes in weather that affect the success of planting, to the food crisis. Therefore, the selection of plant species must be done correctly and in accordance with the preferences of farmers. The choice of plant types that are by following the wishes of farmers can increase the motivation of farmers to maintain and caring for plants so that the success rate of planting will be higher. Therefore, it is essential to research farmers’ preferences in choosing crop types and agroforestry cropping patterns (simple agroforestry and complex agroforestry) because the amount of carbon stored by plants will depend on farmers’ preferences. The purpose of this study is to determine the preferences of farmers in choosing types of crops and agroforestry cropping patterns by using decision-making analysis methods. The study was conducted from December 2020 to February 2021 at the Batutegi Forest Management Unit, Tanggamus Regency, and Lampung Province. Research results show that the aspects considered by farmers are the aspects considered by farmers in choosing plant types and cropping patterns were production orientation (100%), time and labor (95.65%), biophysical conditions (80.43%), knowledge (80.43%) and the ability to invest in plants (36.96%). The reasons for farmers choosing crop types and cropping patterns are income (100%), productivity (88.89%), production speed (82.22%), and ease of harvesting (37.78%). As many as 73% of farmers who choose complex agroforestry cropping patterns have a more significant role in minimizing the impact of global warming. This happens because complex agroforestry cropping patterns are able to minimize the effects of global warming more optimally with larger stored carbon stocks of 765.61 tons/ha when compared to simple agroforestry planting patterns with carbon stocks of 356.21 tons/ha.
Abstract
Context and background:
The Sagara hills provide key ecosystem services to the communities in Kongwa and Mpwapwa districts in Dodoma region. In particular, the hills provide watershed services which is vital in a challenging semi-arid condition. However, the current situation suggests that the watershed services are at risk due to anthropogenic activities.
Goal and Objectives:
This study assesses the dynamics of land use and land cover changes in Sagara catchment and its implication to watershed services for the surrounding communities.
Methodology:
Remote sensing and Geographical Information System (GIS) techniques were used to analyze changes in land use and land cover in the catchment between 2013 and 2021. The study used two categories of data: Landsat 8 layers and reference data. Landsat 8 layers were used as input data for change detection and quantification of vegetation cover and other land uses at Sagara hills, while field data and higher resolution Google Earth Pro Historical images were used to create reference data for training the classifier and accuracy assessment.
Results:
Results show that the built area increased from 249.4 ha in 2013 to 504.2 ha in 2021 with a net gain of 254.8 ha. Farmland increased with a net gain of 3108.1 ha whereby the farmland area was 10900.7 ha in 2013, but increased to 14008 ha in 2021. It was further observed that there were significant changes in vegetation cover from 2013 to 2021. The woodland forest which was a dominant vegetation in 2013 with an area of 24187.5 ha has been reduced to 12439 ha. This means in 9years; 11,748 ha of forest have been lost due destructive human activities. Grassland area was also observed to decrease from 995.1 ha in 2013 to 751.9 ha in 2021 with a net loss of 243.2 ha. Closed bushes and thickets which increased significantly by 2021 has become the dominant vegetation. Bare land was also observed to have increased. This is attributed to poor farming methods which resulted into soil erosion and loss of land productivity in the catchment.
Hannah Kerner, Catherine Nakalembe, Adam Yang
et al.
Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa, where there is high food insecurity and sparse agricultural statistics. EO-based monitoring systems require accurate cropland maps to provide information about croplands, but there is a lack of data to determine which of the many available land cover maps most accurately identify cropland in African countries. This study provides a quantitative evaluation and intercomparison of 11 publicly available land cover maps to assess their suitability for cropland classification and EO-based agriculture monitoring in Africa using statistically rigorous reference datasets from 8 countries. We hope the results of this study will help users determine the most suitable map for their needs and encourage future work to focus on resolving inconsistencies between maps and improving accuracy in low-accuracy regions.
Faezeh Shirmohammadi, Deyan Draganov, Johno van IJsseldijk
et al.
The overburden structures often can distort the responses of the target region in seismic data, especially in land datasets. Ideally, all effects of the overburden and underburden structures should be removed, leaving only the responses of the target region. This can be achieved using the Marchenko method. The Marchenko method is capable of estimating Green's functions between the surface of the Earth and arbitrary locations in the subsurface. These Green's functions can then be used to redatum wavefields to a level in the subsurface. As a result, the Marchenko method enables the isolation of the response of a specific layer or package of layers, free from the influence of the overburden and underburden. In this study, we apply the Marchenko-based isolation technique to land S-wave seismic data acquired in the Groningen province, the Netherlands. We apply the technique for combined removal of the overburden and underburden, which leaves the isolated response of the target region which is selected between 30 m and 270 m depth. Our results indicate that this approach enhances the resolution of reflection data. These enhanced reflections can be utilised for imaging and monitoring applications.
Current research indicates that (sub)surface ocean worlds essentially devoid of subaerial landmasses (e.g., continents) are common in the Milky Way, and that these worlds could host habitable conditions, thence raising the possibility that life and technological intelligence (TI) may arise in such aquatic settings. It is known, however, that TI on Earth (i.e., humans) arose on land. Motivated by these considerations, we present a Bayesian framework to assess the prospects for the emergence of TIs in land- and ocean-based habitats (LBHs and OBHs). If all factors are equally conducive for TIs to arise in LBHs and OBHs, we demonstrate that the evolution of TIs in LBHs (which includes humans) might have very low odds of roughly $1$-in-$10^3$ to $1$-in-$10^4$, thus outwardly contradicting the Copernican Principle. Hence, we elucidate three avenues whereby the Copernican Principle can be preserved: (i) the emergence rate of TIs is much lower in OBHs, (ii) the habitability interval for TIs is much shorter in OBHs, and (iii) only a small fraction of worlds with OBHs comprise appropriate conditions for effectuating TIs. We also briefly discuss methods for empirically falsifying our predictions, and comment on the feasibility of supporting TIs in aerial environments.