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The importance to consider changes in surface albedo and go beyond simple carbon accounting when assessing climate change impacts of forestry and land use activities is increasingly recognized. However, representation of albedo changes in climate models is complex and highly parameterized, thereby limiting their applications in climate impact studies. The availability of simple yet reliable albedo models can enhance consideration of albedo changes in land use studies. We propose a set of simplified models for estimating surface albedo in a boreal forest. We process and harmonize datasets of remotely‐sensed albedo estimates, forest structure parameters, and meteorological records for different forest locations in Norway. By combining linear unmixing with nonlinear programming, we simultaneously produce albedo estimates at the same resolution of the land cover dataset (16 m, notably higher than satellite retrievals) and a variety of flexible models for albedo predictions. We test different combinations of functional forms, variables, and constraints, including variants specific for snow‐free conditions. We find that models capture the seasonal pattern of surface albedo and the interactive effect of forest structures and meteorological parameters, and many of them show good statistical scores. The cross‐validation exercise shows that the models derived from one area perform reasonably well when applied to other forested areas in Norway, regardless of the temporal and spatial scales. By incorporating changes in forest structure and climate conditions as explicit variables, these models are simple to be used in different applications aiming at estimating albedo changes from forest management and climate change. Plain Language Summary Surface albedo is the fraction of solar radiation reflected back into the atmosphere by a surface, and it determines how much energy is re‐distributed in the biosphere. It is one of the most important physical properties of land cover and a key mechanism for climate control. By combining satellite retrievals of Norwegian boreal forest with high resolution land cover data and meteorological records, our study produces a range of relatively simple models to estimate surface albedo using the forest structure parameters and climate data. The models require simple variables of forest structure information (age and/or volume) and temperature and/or snow water equivalents. These models are relatively easy and fast to be used for quantifying effects of forest management and climate change on surface albedo. Key Points Availability of models for simulating surface albedo changes in climate impact assessment studies of forest management is limited A set of flexible models based on forest and climate variables can predict albedo changes from forest management and climate change The models capture the seasonal pattern of surface albedo and the interactive effects of forest structure and meteorological parameters

Urban forests are an essential component of urban areas as they provide many environmental and social services that contribute to the quality of life in cities. Urban forests in most cities of Nigeria are gradually becoming bitty as a result of urbanization activities, thereby posing adverse effects. In this study, we assessed the changes in the urban forests cover under the influence of different human settlement (HS) extents across the urban area of Ibadan city using remotely sensed data. The pattern of change(s) in the urban forests cover over 20 years were examined by analysing and manipulating Landsat and Sentinel-2 datasets using Google Earth Engine, ArcGIS 10.1, and Erdas 2014 software. The extents of human settlement (for the year 2000, 2005, 2010, 2015, and 2020) were extracted (from Landsat datasets), analysed, and mapped to evaluate the status of the urban forests cover under different human settlement extents. The result reveals a substantial land cover changes within the urban area of Ibadan.  The urban forest cover decreased from 24.14% to 7.99%. Also, there is a significant decrease in the urban forests cover as a result of a substantial increase in human settlement extent (102,806 to 122,572 pixels). The study provides an opportunity to map the status of urban forest cover and extents of HS in a developing city using remotely sensed data and applications of GIS tools.

Automated individual tree crown detection and delineation (ITCD) using remotely sensed data plays an increasingly significant role in efficiently, accurately, and completely monitoring forests. This paper reviews trends in ITCD research from 1990–2015 from several perspectives—data/forest type, method applied, accuracy assessment and research objective—with a focus on studies using LiDAR data. This review shows that active sources are becoming more prominent in ITCD studies. Studies using active data—LiDAR in particular—accounted for 80% of the total increase over the entire time period, those using passive data or fusion of passive and active data comprised relatively small proportions of the total increase (8% and 12%, respectively). Additionally, ITCD research has moved from incremental adaptations of algorithms developed for passive data sources to innovative approaches that take advantage of the novel characteristics of active datasets like LiDAR. These improvements make it possible to explore more complex forest conditions (e.g., closed hardwood forests, suburban/urban forests) rather than a single forest type although most published ITCD studies still focused on closed softwood (41%) or mixed forest (22%). Approximately one-third of studies applied individual tree level (30%) assessment, with only a quarter reporting more comprehensive multi-level assessment (23%). Almost one-third of studies (32%) that concentrated on forest parameter estimation based on ITCD results had no ITCD-specific evaluation. Comparison of methods continues to be complicated by both choice of reference data and assessment metric; it is imperative to establish a standardized two-level assessment framework to evaluate and compare ITCD algorithms in order to provide specific recommendations about suitable applications of particular algorithms. However, the evolution of active remotely sensed data and novel platforms implies that automated ITCD will continue to be a promising technology and an attractive research topic for both the forestry and remote sensing communities.

Dynamic ocean management, or management that uses near real-time data to guide the spatial distribution of commercial activities, is an emerging approach to balance ocean resource use and conservation. Employing a wide range of data types, dynamic ocean management can be used to meet multiple objectives—for example, managing target quota, bycatch reduction, and reducing interactions with species of conservation concern. Here, we present several prominent examples of dynamic ocean management that highlight the utility, achievements, challenges, and potential of this approach. Regulatory frameworks and incentive structures, stakeholder participation, and technological applications that align with user capabilities are identified as key ingredients to support successful implementation. By addressing the variability inherent in ocean systems, dynamic ocean management represents a new approach to tackle the pressing challenges of managing a f luid and complex environment.

This study developed a rapid rice yield estimation workflow and customized yield prediction model by integrating remote sensing and meteorological data with machine learning (ML). Several issues need to be addressed while developing a crop yield estimation model, including data quality issues, data processing issues, selecting a suitable machine learning model that can learn from few available time-series data, and understanding the non-linear relationship between historical crop yield and remote sensing and meteorological factors. This study applied a series of data processing techniques and a customized ML model to improve the accuracy of crop yield estimation at the district level in Nepal. It was found that remote sensing-derived NDVI product alone was not sufficient for accurate estimation of crop yield. After incorporating other meteorological variables into the ML models, estimation accuracy improved dramatically. Along with NDVI, the meteorological variables of rainfall, soil moisture, and evapotranspiration also exhibited a strong association with rice yield. This study also found that stacking multiple tree-based regression models together could achieve better accuracy than benchmark linear regression or standalone ML models. Due to the unique and distinct physio-geographical setting of each district, a variation in estimation accuracy from district to district could be observed. Our data processing and ML model workflow achieved an average of 92% accuracy of yield estimation with RMSE 328.06 kg/ha and MAE 317.21 kg/ha. This methodological workflow can be replicated in other study areas and the results can help the local authorities and stakeholders understand the factors affecting crop yields as well as estimating crop yield before harvesting season to ensure food security and sustainability.

Forest biomass quantification is essential to the global carbon cycle and climate studies. Many studies have estimated forest biomass from a variety of data sources, and consequently generated some regional and global maps. However, these forest biomass maps are not well known and evaluated. In this paper, we reviewed an extensive list of currently available forest biomass maps. For each map, we briefly introduced the data sources, the algorithms used, and the associated uncertainties. Large-scale biomass datasets were compared across Europe, the conterminous United States, Southeast Asia, tropical Africa and South America. Results showed that these forest biomass datasets were almost entirely inconsistent, particularly in woody savannas and savannas across these regions. The uncertainties in biomass maps could be from a variety of sources including the chosen allometric equations used to calculate field data, the choice and quality of remotely sensed data, as well as the algorithms to map forest biomass or extrapolation techniques, but these uncertainties have not been fully quantified. We suggested the future directions for generating more accurate large-scale forest biomass maps should concentrate on the compilation of field biomass data, novel approaches of forest biomass mapping, and comprehensively addressing the accuracy of generated biomass maps.

Indicator 11.3. 1 of the 2030 sustainable development goals (SDG) 11, i.e., the ratio of the land use to the population growth rate, is currently classified by the United Nations as a Tier II indicator, as there is a globally-accepted methodology for its calculation, but the data are not available, nor are not regularly updated. Recently, the increased availability of remotely sensed data and products allows not only for the calculation of the SDG 11.3. 1, but also for its monitoring at different levels of detail. That is why this study aims to address the interrelationships between population development and land use changes in Poland and Lithuania, two neighboring countries in Central and Eastern Europe, using the publicly available remotely sensed products, CORINE land cover and GHS-POP. The paper introduces a map modelling process that starts with data transformation through GIS analyses and results in the geovisualisation of the LCRPGR (land use efficiency), the PGR (population growth rate), and the LCR (land use rate). We investigated the spatial patterns of the index values by utilizing hotspot analyses, autocorrelations, and outlier analyses. The results show how the indicators’ values were concentrated in both countries; the average value of SDG 11.3. 1, from 2000 to 2018 in Poland amounted to 0.115 and, in Lithuania, to −0.054. The average population growth ratio (PGR) in Poland equaled 0.0132, and in Lithuania, it was −0.0067, while the average land consumption ratios (LCRs) were 0.0462 and 0.0067, respectively. Areas with an increase in built-up areas were concentrated mainly on the outskirts of large cities, whereas outliers of the LCRPGR index were mainly caused by the uncertainty of the source data and the way the indicator is interpreted.

Forest attributes, such as standing stock, diameter at breast height (DBH), tree height, and basal area, are critical for effective forest management; yet, traditional estimation methods remain labor-intensive and often lack the spatial detail required for contemporary decision-making. This study addresses these challenges by integrating machine learning algorithms with high-resolution remotely sensed data and rigorously collected ground truth measurements to produce accurate, national-scale maps of forest attributes in Romania. To ensure the reliability of the model predictions, extensive field campaigns were conducted across representative Romanian forests. During these campaigns, detailed measurements were recorded for every tree within selected plots. For each tree, DBH was measured directly, and tree heights were obtained either by direct measurement—using hypsometers or clinometers—or, when direct measurements were not feasible, by applying well-established DBH—height allometric relationships that have been calibrated for the local forest types. This comprehensive approach to ground data collection, supplemented by an independent dataset from Brasov County collected using the same protocols, allowed for robust training and validation of the machine learning models. This study evaluates the performance of three machine learning algorithms—Random Forest (RF), Classification and Regression Trees (CART), and the Gradient Boosting Tree Algorithm (GBTA)—in predicting the forest attributes from Sentinel-2 satellite imagery. While Random Forest consistently delivered high R2 values and low root mean square errors (RMSE) across all attributes, GBTA showed particular strength in predicting standing stock, and CART excelled in basal area estimation but was less reliable for other attributes. A sensitivity analysis across multiple spatial resolutions revealed that the performance of all algorithms varied significantly with changes in resolution, emphasizing the importance of selecting an appropriate scale for accurate forest mapping. By focusing on both the methodological advancements in machine learning applications and the rigorous, detailed empirical forest data collection, this study provides a clear solution to the problem of obtaining reliable, spatially detailed forest attribute maps.

In this study, the Secchi disk depth ( Z sd ) values as an indicator of seawater clarity/transparency were estimated using the ESA (European Space Agency) Sentinel-3A and Sentinel-3B OLCI (S3/OLCI) satellite data in the Persian Gulf and the Gulf of Oman (PG&GO). To do so, two procedures were evaluated including an existing methodology developed by Doron et al. (J Geophys Res: Oceans 112(C6) 2007 and (Remote Sens Environ 115:2986–3001 2011 ) and an empirical model proposed in this research formed by employing the blue (B 4 ) and green (B 6 ) bands of S3/OLCI data. In this regard, a total number of 157 field-measured Z sd values (114 training points for calibration of the models and 43 control points for accuracy assessment of them) were observed during eight research cruises conducted by the research vessel, the Persian Gulf Explorer, in the PG&OS between 2018 and 2022. The optimum methodology was then selected based on the statistical indicators including R 2 (coefficient of determination), RMSE (root mean square error), and MAPE (mean absolute percentage error). However, after the indication of the optimal model, the data of all 157 observations were utilized for the calculation of unknown parameters of the model. The final results demonstrated that compared to the existing empirical model proposed by Doron et al. (J Geophys Res: Oceans 112(C6) 2007 and (Remote Sens Environ 115:2986–3001 2011 ), the developed model in this study which was formed based on the linear and ratio terms of B 4 and B 6 bands, has more efficiency in the PG&GO. Consequently, a model in form of Z sd  = e 1.638 B 4 / B 6 −8.241 B 4 −12.876 B 6 +1.26 was suggested for the estimation of Z sd values from S3/OLCI in the PG&GO ( R 2  = 0.749, RMSE = 2.56 m, and MAPE = 22.47%). The results also showed that the annual oscillation of the Z sd values in the GO (5–18 m) is evidently higher compared with those in the PG (4–12 m) and the SH (7–10 m) regions.

Ecosystems can undergo abrupt transitions between alternative stable states when the driver crosses a critical threshold. Dynamical systems theory shows that when ecosystems approach the point of loss of stability associated with these transitions, they take a long time to recover from perturbations, a phenomenon known as critical slowing down. This generic feature of dynamical systems can offer early warning signals of abrupt transitions. However, these signals are qualitative and cannot quantify the thresholds of drivers at which transition may occur. Here, we propose a method to estimate critical thresholds from spatial data. We show that two spatial metrics, spatial variance and autocorrelation of ecosystem state variable, computed along driver gradients can be used to estimate critical thresholds. First, we investigate cellular-automaton models of ecosystem dynamics that show a transition from a high-density state to a bare state. Our models show that critical thresholds can be estimated as the ecosystem state and the driver values at which spatial variance and spatial autocorrelation of the ecosystem state are maximum. Next, to demonstrate the application of the method, we choose remotely sensed vegetation data (Enhanced Vegetation Index, EVI) from regions in central Africa and northeast Australia that exhibit alternative states in woody cover. We draw transects (8 × 90 km) that span alternative stable states along rainfall gradients. Our analyses of spatial variance and autocorrelation of EVI along transects yield estimates of critical thresholds. These estimates match reasonably well with those obtained by an independent method that uses large-scale (250 × 200 km) spatial data sets. Given the generality of the principles that underlie our method, our method can be applied to a variety of ecosystems that exhibit alternative stable states.