Articles de revues sur le sujet « Forest systems, climate change, forest modelling, spatial analysis »

Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data.

This work introduces a methodology for assessing near-future fire weather pattern changes based on the Canadian Fire Weather Index system components (Fire Weather Index (FWI), Initial Spread Index (ISI), Fire Severity Rating (FSR)), applied in touristic areas in Greece. Four series of daily raster-based datasets for the fire seasons (May–October), concerning a historic (2006 to 2015) and a future climatology period (2036–2045), were created for the areas under consideration, based on high-resolution climate modelling with the Representative Concentration Pathway (RCP), PCR 4.5 and RCP 8.5 scenarios. The climate model data were obtained from the European Coordinated Downscaling Experiment (EURO-CORDEX) climate database and consisted of atmospheric variables as required by the FWI system, at 12.5 km spatial resolution. The final datasets of the abovementioned variables used for the study were processed at 5 km spatial resolution for the domain of interest after applying regridding based on the nearest neighbour interpolating process. Geographic Information Systems (GIS) spatial operations, including spatial statistics and zonal analyses, were applied on the series of the derived daily raster maps in order to provide a number of output thematic layers. Moreover, historic FWI percentile values, which were estimated for Greece in the frame of a past research study of the Environmental Research Laboratory (EREL), were used as reference data for further evaluation of future fire weather changes. The straightforward methodology for the assessment of the evolution of spatial and temporal distribution of Fire weather Danger due to climate change presented herewith is an essential tool for enhancing the knowledge for the decision support process for forest fire prevention, planning and management policies in areas where the fire risk both in terms of fire hazard likelihood and expected impact is quite important due to human presence and cultural prestige, such as archaeological and tourist protected areas.

Research Highlights: Validating modelling approach which combines global framework conditions in the form of climate and policy scenarios with the use of forest decision support system to assess climate change impacts on the sustainability of forest management. Background and Objectives: Forests and forestry have been confirmed to be sensitive to climate. On the other hand, human efforts to mitigate climate change influence forests and forest management. To facilitate the evaluation of future sustainability of forest management, decision support systems are applied. Our aims are to: (1) Adopt and validate decision support tool to incorporate climate change and its mitigation impacts on forest growth, global timber demands and prices for simulating future trends of forest ecosystem services in Lithuania, (2) determine the magnitude and spatial patterns of climate change effects on Lithuanian forests and forest management in the future, supposing that current forestry practices are continued. Materials and Methods: Upgraded version of Lithuanian forestry simulator Kupolis was used to model the development of all forests in the country until 2120 under management conditions of three climate change scenarios. Selected stand-level forest and forest management characteristics were aggregated to the level of regional branches of the State Forest Enterprise and analyzed for the spatial and temporal patterns of climate change effects. Results: Increased forest growth under a warmer future climate resulted in larger tree dimensions, volumes of growing stock, naturally dying trees, harvested assortments, and also higher profits from forestry activities. Negative impacts were detected for the share of broadleaved tree species in the standing volume and the tree species diversity. Climate change effects resulted in spatially clustered patterns—increasing stand productivity, and amounts of harvested timber were concentrated in the regions with dominating coniferous species, while the same areas were exposed to negative dynamics of biodiversity-related forest attributes. Current forest characteristics explained 70% or more of the variance of climate change effects on key forest and forest management attributes. Conclusions: Using forest decision support systems, climate change scenarios and considering the balance of delivered ecosystem services is suggested as a methodological framework for validating forest management alternatives aiming for more adaptiveness in Lithuanian forestry.

Abstract This study attempts to understand the climatic resilience of two forest types of central India—that is, Tectona grandis (Teak) forest of Satpura Tiger Reserve and Shorea robusta (Sal) forest of Kanha Tiger Reserve—using normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) extracted from MODIS, and climate variable data sets at highest spatial and temporal scales. Teak and Sal forests within the core area of the selected tiger reserves represent the least anthropogenic disturbances, and therefore, the observed changes in NDVI and EVI over the past 16 years could be analyzed in the context of climate change. The correlation analysis between climatic variables (minimum temperature, maximum temperature, mean temperature, and total annual rainfall) and forest response indicators (NDVI/EVI) at seasonal and annual scales revealed that Teak and Sal forests are more sensitive to change in past temperature as compared with rainfall. Also, the changes in NDVI and EVI of Sal forest are correlated more to minimum temperature, and that of Teak forest to maximum temperature. The analysis of sapling girth class of Sal and Teak further revealed that Sal as compared with Teak is more affected because of the changing climate variables of the recent past. The findings of the study will help manage forests more efficiently in the context of changing climate.

Forests store approximately as much carbon as is in the atmosphere, with potential to take in or release carbon rapidly based on growth, climate change and human disturbance. Above-ground biomass (AGB) is the largest carbon pool in most forest systems, and the quickest to change following disturbance. Quantifying AGB on a global scale and being able to reliably map how it is changing, is therefore required for tackling climate change by targeting and monitoring policies. AGB can be mapped using remote sensing and machine learning methods, but such maps have high uncertainties, and simply subtracting one from another does not give a reliable indication of changes. To improve the quantification of AGB changes it is necessary to add advanced statistical methodology to existing machine learning and remote sensing methods. This review discusses the areas in which techniques used in statistical research could positively impact AGB quantification. Nine global or continental AGB maps, and a further eight local AGB maps, were investigated in detail to understand the limitations of techniques currently used. It was found that both modelling and validation of maps lacked spatial consideration. Spatial cross validation or other sampling methods, which specifically account for the spatial nature of this data, are important to introduce into AGB map validation. Modelling techniques which capture the spatial nature should also be used. For example, spatial random effects can be included in various forms of hierarchical statistical models. These can be estimated using frequentist or Bayesian inference. Strategies including hierarchical modelling, Bayesian inference, and simulation methods can also be applied to improve uncertainty estimation. Additionally, if these uncertainties are visualised using pixelation or contour maps this could improve interpretation. Improved uncertainty, which is commonly between 30% and 40%, is in addition needed to produce accurate change maps which will benefit policy decisions, policy implementation, and our understanding of the carbon cycle.

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). 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SUMMARYSpatial simulation may be used to model the potential effects of current biodiversity approaches on future habitat modification under differing climate change scenarios. To illustrate the approach, spatial simulation models, including landscape-level forest dynamics, were developed for a semi-natural grassland of conservation concern in a southern Italian protected area, which was exposed to woody vegetation encroachment. A forest landscape dynamics simulator (LANDIS-II) under conditions of climate change, current fire and alternative management regimes was used to develop scenario maps. Landscape pattern metrics provided data on fragmentation and habitat quality degradation, and quantified the spatial spread of different tree species within grassland habitats. The models indicated that approximately one-third of the grassland area would be impacted by loss, fragmentation and degradation in the next 150 years. Differing forest management regimes appear to influence the type of encroaching species and the density of encroaching vegetation. Habitat modifications are likely to affect species distribution and interactions, as well as local ecosystem functioning, leading to changes in estimated conservation value. A site-scale conservation strategy based on feasible integrated fire and forest management options is proposed, considering the debate on the effectiveness of protected areas for the conservation of ecosystem services in a changing climate. This needs to be tested through further modelling and scenario analysis, which would benefit from the enhancement of current modelling capabilities of LANDIS-II and from combination with remote sensing technologies, to provide early signals of environmental shifts both within and outside protected areas.

Adaptation to climate change requires prediction of its impacts, especially on ecosystems. In this work we simulated the change in bird species richness in the boreal forest of Quebec, Canada, under climate change scenarios. To do so, we first analyzed which geographical and bioclimatic variables were the strongest predictors for the spatial distribution of the current resident bird species. Based on canonical redundancy analysis and analysis of variance, we found that annual temperature range, average temperature of the cold season, seasonality of precipitation, precipitation in the wettest season, elevation, and local percentage of wet area had the strongest influence on the species’ distributions. We used these variables with Random Forests, Multivariate Adaptive Regression Splines and Maximum Entropy models to explain spatial variations in species abundance. Future species distributions were calculated by replacing present climatic variables with projections under different climate change pathways. Subsequently, maps of species richness change were produced. The results showed a northward expansion of areas of highest species richness towards the center of the province. Species are also likely to appear near James Bay and Ungava Bay, where rapid climate change is expected.

SUMMARYThe present paper provides a geographically scaled analysis of adaptation to climate change using adoption of agricultural systems observed across Africa. Usingc. 9000 farm surveys, spatial logit models were applied to explain observed agricultural system choices by climate variables after accounting for soils, geography and other household characteristics. The results reveal that strong neighbourhood effects exist and a spatial re-sampling and bootstrapping approach can remove them. The crops-only system is adopted most frequently in the lowland humid forest, lowland sub-humid, mid-elevation sub-humid Agro-Ecological Zones (AEZs) and in the highlands in the east and in southern Africa. Integrated farming is favoured in the lowland dry savannah, moist savannah and semi-arid zones in West Africa and eastern coastal zones. A livestock-only system is favoured most in the mid/high-elevation moist savannahs located in southern Africa. Under a hot and dry Canadian Climate Centre (CCC) scenario, the crops-only system should move out from the currently favoured regions of humid zones in the lowlands towards the mid-/high elevations. It declines by more than 5% in the lowland savannahs. Integrated farming should increase across all the AEZs by as much as 5%, but less so in the deserts or in the humid forest zones in the mid-/high elevations. A livestock-only system should increase by 2–5% in the lowland semi-arid, dry savannah and moist savannah zones in the lowlands. Adaptation measures should be carefully scaled, up or down, considering geographic and ecological differentials as well as household characteristics, as proposed in the present study.

<p><strong>Abstract.</strong> Trentino is an Italian alpine region (about 6200&amp;thinsp;km²) with a forest coverage exceeding 60% of its whole surface. In the past, forest landscape has changed dramatically, especially in periods of forest over-exploitation.</p><p>Previous studies in some Trentino sub-regions (Val di Fassa, Paneveggio) have identified these changes and the current trend of forest growth at the expenses of open areas, such as pastures and grasslands, due to the abandonment of rural areas. This phenomenon leads to the rapid Alpine landscape change and profoundly affects the ecological features of mountain ecosystems. To be able to monitor and to take future actions about this trend it is fundamental to know in detail the historical situation of the progressive changes on the land use that occurred over Trentino.</p><p>The work aims to comprehensively reconstruct the forest cover of whole Trentino at high resolution (5&amp;thinsp;m&amp;thinsp;&amp;times;&amp;thinsp;5&amp;thinsp;m pixels) using a series of maps spanning a long period, consisting in historical maps, aerial images, remote sensed information and historical archives. The datasets were archived, processed and analyzed using the Free and Open Source Software (FOSS) GIS GRASS and QGIS. Historical maps include Atlas Tyrolensis (dated 1770), Theresianischer Kataster (dated 1859) and Italian Kingdom Forest Map (IKFM) of 1936. The aerial imagery dataset includes aerial images taken in 1954, which have been orthorectified during this research, and orthophotos available for years 1973, 1994, 2000, 2006, 2010 and 2016. Remote sensed information includes Landsat and recent Lidar data, while historical archives consist mostly in Forest Management Plans available since around 1950.</p><p>The versatility of the wide variety of modules supplied from the FOSS GRASS and QGIS enabled to perform a diverse set of analysis and pre-processing (e.g.:orthorectification) on a heterogeneous dataset of input images. We will focus on the different strategies and methodologies implemented in the FOSS GIS used to process the various types of geographic data, challenges for the future of the research and the fundamental role of the FOSS systems in this process.</p><p>Quantifying forest change in the time-span of our dataset can be used to perform further analysis on ecosystem services, such as protection from soil erosion, and on modification of biome diversity and to create future change scenarios.</p>

The vegetation dynamics and disturbance regimes of the south-west Pacific have been significantly altered following human settlement. Previously forested landscapes are now dominated by a matrix of flammable early successional vegetation within which patches of mesic, fire-sensitive forest are embedded. Future environmental change, and in particular climate change, will further affect disturbance regimes in these ecosystems. If ignition frequency and fire extent increase, then the persistence of these landscapes in their current composition and structure is uncertain. Using a spatially explicit landscape ecological model, we explored the implications of climatically altered fire regimes for landscape composition and structure in a mountain-top reserve in New Caledonia. The outcomes of the modeling suggest that increased ignition probability and vegetation flammability would lead to a maquis (heathland)-dominated landscape structurally simpler than that seen today. The feasibility of fire suppression as a means of managing altered fire regimes was explored using a series of model experiments. Fire suppression has been problematic in some systems, especially those where fire hazard increases over time. However, in this ecosystem, and others in the south-west Pacific, it may be a viable alternative for managing fire because fire hazard, in terms of flammability, peaks early in the succession and then decreases over successional time.

Forest models should in future combine the predictive power and flexibility of process-based models with the empirical information and descriptive accuracy of conventional mensuration-based models. Progress is likely to be rapid if model developers identify the potential users of their models and the needs of those users. Users include operational forest managers, planners, bureaucrats, politicians, community and environmental groups, scientists, and academics. Extant models that could be used immediately or could be adapted for use by these groups are reviewed. Currently available process-based models can provide good estimates of growth and biomass productivity at various scales; combined with conventional models they can provide information of the type required by managers and planners. Climate-driven models can provide good estimates of potential plantation productivity, while detailed process models contribute to our understanding of the way systems function and are essential for future progress. Technical challenges for the future include continued research on carbon-allocation processes, nutrient availability in soils, and nutrient uptake by trees. It is important that we have models that can be used to predict and analyze the effects of technologies such as clonal forestry and possible genetic manipulation, as well as intensive management in relation to nutrition, weed control, and disease control. Large-scale analysis of forest productivity is already possible using models driven by remote sensing; inclusion of nutrition should be a goal in this area. Moves towards active collaboration and the implementation of mixed models in operational systems, as well as improving communication between model developers and users, should ensure that practical problems are identified and fed back to modellers, which should lead to rapid progress.

Abstract Forest losses amid land use dynamics have become issues of outermost concern in the light of climate change phenomenon which has captivated the world’s attention. It is imperative to monitor land use change and to forecast forms of future land use change on a temporal and spatial basis. The main thrust of this study is to assess land use change in the lower half of the Ashanti Region of Ghana within a 40 year period. The analysis of land use change uses a combination method in Remote Sensing (RS) and Geographic Information System (GIS). Cellular Automata and Markov Chain (Cellular Automata-Markov) are utilized to predict for land use land cover (LULC) change for 2020 and 2030. The processes used include: (i) a data pre-processing (geometric corrections, radiometric corrections, subset creation and image enhancement) of epoch Landsat images acquired in 1990, 2000, and Disaster Monitoring Constellation (DMC) 2010; (ii) classification of multispectral imagery (iii) Change detection mapping (iv) using Cellular Automata-Markov to generate land use change in the next 20 years. The results illustrate that in years 2020 to 2030 in the foreseeable future, there will an upsurge in built up areas, while a decline in agricultural land use is envisaged. Agricultural land use would still be the dominant land use type. Forests would be drastically reduced from close to 50% in 1990 to just fewer than 10% in 2030. Land use decision making must be very circumspect, especially in an era where Ghana has opted to take advantage of REDD+. Studies such as this provide vital pieces of information which may be used to monitor, direct and influence land use change to a more beneficial and sustainable manner

Abstract. Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS) are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method), that combines remote sensing imagery with GIS climate modelling to estimate daily evapotranspiration (ETd) for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i) Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii) air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii) net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (TERRA/AQUA MODIS and Landsat-5 TM/Landsat-7 ETM+) and combining three different approaches to calculate the B parameter. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L.) stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, with a mean RMSE value of about 0.6 mm day−1 and an estimation error of ±30%. The poor agreement obtained using MODIS data reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and therefore further research is necessary on this issue.

Abstract. Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS) are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method), that combines remote sensing imagery with GIS-based climate modelling to estimate daily evapotranspiration (ETd) for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i) Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii) air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii) net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (Landsat-7 ETM+, Landsat-5 TM and TERRA/AQUA MODIS, with a spatial resolution of 60, 120 and 1000 m, respectively) and combining three different approaches to calculate the B parameter, which represents an average bulk conductance for the daily-integrated sensible heat flux. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L.) stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, R2 test of 0.89, with a mean RMSE value of about 0.6 mm day−1 and an estimation error of ±30 %. The poor agreement obtained using TERRA/AQUA MODIS, with a mean RMSE value of 1.8 and 2.4 mm day−1 and an estimation error of about ±57 and 50 %, respectively. This reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and therefore further research is necessary on this issue. Finally, implementing regional GIS-based climate models as inputs in ETd retrieval have has provided good results, making possible to compute ETd at regional scales.

Abstract. Climate change and land management practices are projected to significantly affect soil organic carbon (SOC) dynamics and dissolved organic carbon (DOC) leaching from soils. In this modelling study, we adopted the Century model to simulate past (1906–2012), present, and future (2013–2100) SOC and DOC levels for sandy and loamy soils typical of northwestern European conditions under three land use types (forest, grassland, and arable land) and several future scenarios addressing climate change and land management change. To our knowledge, this is the first time that the Century model has been applied to assess the effects of climate change and land management on DOC concentrations and leaching rates, which, in combination with SOC, play a major role in metal transport through soil. The simulated current SOC levels were generally in line with the observed values for the different kinds of soil and land use types. The climate change scenarios result in a decrease in both SOC and DOC for the agricultural systems, whereas for the forest systems, SOC is projected to slightly increase and DOC to decrease. An analysis of the sole effects of changes in temperature and changes in precipitation showed that, for SOC, the temperature effect predominates over the precipitation effect, whereas for DOC the precipitation effect is more prominent. A reduction in the application rates of fertilisers under the land management scenario leads to a decrease in the SOC stocks and the DOC leaching rates for the arable land systems, but it has a negligible effect on SOC and DOC levels for the grassland systems. Our study demonstrated the ability of the Century model to simulate climate change and agricultural management effects on SOC dynamics and DOC leaching, providing a robust tool for the assessment of carbon sequestration and the implications for contaminant transport in soils.

Abstract. Climate change and land management practices are projected to significantly affect soil organic carbon (SOC) dynamics and dissolved organic carbon (DOC) leaching from soils. In this modelling study, we adopted the Century model to simulate past (1906–2012), present, and future (2013–2100) SOC and DOC levels for sandy and loamy soils typical for Northwestern European conditions under three land use types (forest, grassland and arable land) and several future scenarios addressing climate change and land management change. To our knowledge, this is the first time that the Century model has been applied to assess the effects of climate change and land management on DOC concentrations and leaching rates, which, in combination with SOC, play a major role in metal transport through soil. The simulated current SOC levels were generally in line with the observed values for the different kinds of soil and land use types. The climate change scenarios result in a decrease in both SOC and DOC for the agricultural systems, whereas for the forest systems, SOC is projected to slightly increase and DOC to decrease. An analysis of the sole effects of changes in temperature and changes in precipitation showed that, for SOC, the temperature effect predominates over the precipitation effect, whereas for DOC, the precipitation effect is more prominent. A reduction in the application rates of fertilizers under the land management scenario leads to a decrease in the SOC stocks and the DOC leaching rates for the arable land systems, but has a negligible effect on SOC and DOC levels for the grassland systems. Our study demonstrated the ability of the Century model to simulate climate change and agricultural management effects on SOC dynamics and DOC leaching, providing a robust tool for the assessment of carbon sequestration and the implications for contaminant transport in soils.

Forest ecosystems are shaped by disturbances and functional features of vegetation recovery after disturbances. There is considerable variation in basic disturbance characteristics, magnitude, severity, and intensity. Disturbance legacies provide possible explanations for ecosystem resilience. The impact (length and strength) of the pool of ecosystem legacies and how they vary at different spatial and temporal scales is a most promising line of further research. Analyses of successional trajectories, ecosystem memory, and novel ecosystems are required to improve modelling in support of forests. There is growing evidence that managing ecosystem legacies can act as a driver in adaptive management to achieve goals in forestry. Managers can adapt to climate change and new conditions through anticipatory or transformational strategies of ecosystem management. The papers presented in this Special Issue covers a wide range of topics, including the impact of herbivores, wind, and anthropogenic factors, on ecosystem resilience.

Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level by using the PRISMA framework. In the study, we discuss the results of the analysis based on 329 selected papers on the main aspects of the study area published in 48 journals within the past two decades (2000–2020). In the first part of this review, we analyse characteristics of the papers, including journals, spatial extent, geographic distribution, types of remote sensing sensors, ecological zoning, tree species, spectral indices, and accuracy metrics used in the studies. The second part of this review discusses the main tendencies, challenges, and increasing added value of different remote sensing techniques in forest burnt area, burn severity, and post-fire recovery assessments. Finally, it identifies potential opportunities for future research with the use of the new generation of remote sensing systems, classification and cloud performing techniques, and emerging processes platforms for regional and large-scale applications in the field of study.

Triplochiton sleroxylon (samba) is a West and Central African forest species of high socio-economic value which is increasingly threatened by anthropogenic pressures from various sources. The aim of this study was to determine the impact of climate change on the spatial distribution of Triplochiton sleroxylon in the Guineo-Congolese region of Benin. All of 2311 occurrence data of this species were combined with current and future climate variables in the Maxent program under RCP scenarios 4.5 and 8.5 by 2055. Analysis of the spatial pattern of Triplochiton scleroxylon revealed an aggregative distribution between 1m and 7 m distance. But for a distance between 0 and 1 m and more than 7 m, the spatial pattern revealed a random spatial distribution. Under current climatic conditions, 45.17% of the study area of the Guineo-Congolese region in Benin and 61.69% of the one of protected areas are currently very suitable for the cultivation and conservation of samba. Projections to 2055 indicate a significant increase in the area of these habitats for the two scenarios used. These results show that the current and future climatic conditions of the Guineo-Congolese region in Benin remain favourable for the cultivation and conservation of this species. Unfortunately, outside protected areas, these favourable habitats are occupied by settlements and fields. Taking these results into account could effectively contribute to the sustainable conservation of this species in Benin. © 2020 International Formulae Group. All rights reserved. Keywords: Climate change, Maxent program, niche modelling, aggregative distribution, Triplochiton scleroxylon, Benin

AbstractThe provision of freshwater is essential for sustaining human life. Understanding the water provision modelling associated with the Land Use/Cover (LUC) change and climatic factors is vital for landscape water resource management. The Winike watershed is the largest tributary in the upper Omo Gibe basin of Ethiopia. This research aims to analyze the spatial and temporal change in the water yield to investigate the water yield contribution from the watershed based on the variation in input parameters. The Integrated Valuation of Ecosystem Services and Tradeoffs Tool (InVEST) water yield model was used to evaluate the spatial and temporal variation of the water yield in different years (1988, 1998, 2008 and 2018). The data required for this model include LUC data from satellite images, reference evapotranspiration, root depth, plant available water, precipitation, season factor (Z), and a biophysical table. The analysis of LUC change shows a rapid conversion of grazing land, shrubland, and forest land into cultivated land. There has been a significant variation in water provision, which increased from 1.83 × 109 m3 in 1988 to 3.35 × 109 m3 in 2018. Sub-watersheds 31, 32, and 39 in the eastern part of the watershed contributed more water due to higher precipitation and lower reference evapotranspiration. The major increase in the contribution of water yield was in built-up land by 207.4%, followed by bare land, 148.54%, and forest land by 63%. Precipitation had a greater impact on water yield estimation compared with the other input parameters. Hence, this research helps decision-makers to make informed decisions regarding new policies for LUC change improvement to maintain the water resources in the Winike watershed.

Abstract. Given the multiple abiotic and biotic stressors resulting from global changes, management systems and practices must be adapted in order to maintain and reinforce the resilience of forests. Among others, the transformation of monocultures into uneven-aged and mixed stands is an avenue to improve forest resilience. To explore the forest response to these new silvicultural practices under a changing environment, one needs models combining a process-based approach with a detailed spatial representation, which is quite rare. We therefore decided to develop our own model (HETEROFOR for HETEROgeneous FORest) according to a spatially explicit approach, describing individual tree growth based on resource sharing (light, water and nutrients). HETEROFOR was progressively elaborated within Capsis (Computer-Aided Projection for Strategies in Silviculture), a collaborative modelling platform devoted to tree growth and stand dynamics. This paper describes the carbon-related processes of HETEROFOR (photosynthesis, respiration, carbon allocation and tree dimensional growth) and evaluates the model performances for three broadleaved stands with different species compositions (Wallonia, Belgium). This first evaluation showed that HETEROFOR predicts well individual radial growth (Pearson's correlation of 0.83 and 0.63 for the European beech and sessile oak, respectively) and is able to reproduce size–growth relationships. We also noticed that the net to gross primary production (npp to gpp) ratio option for describing maintenance respiration provides better results than the temperature-dependent routine, while the process-based (Farquhar model) and empirical (radiation use efficiency) approaches perform similarly for photosynthesis. To illustrate how the model can be used to predict climate change impacts on forest ecosystems, we simulated the growth dynamics of the mixed stand driven by three IPCC climate scenarios. According to these simulations, the tree growth trends will be governed by the CO2 fertilization effect, with the increase in vegetation period length and the increase in water stress also playing a role but offsetting each other.

Fire is an important and arguably unnatural component of many wet Amazonian and Andean forest systems. Soil charcoal has been used to infer widespread human use of landscapes prior to European Conquest. An analysis of Amazonian soil carbon records reveals that the records have distinct spatial and temporal patterns, suggesting that either fires were only set in moderately seasonal areas of Amazonia or that strongly seasonal and aseasonal areas are undersampled. Synthesizing data from 300 charcoal records, an age–frequency diagram reveals peaks of fire apparently coinciding with some periods of very strong El Niño activity. However, the El Niño record does not always provide an accurate prediction of fire timing, and a better match is found in the record of insolation minima. After the time of European contact, fires became much scarcer within Amazonia. In both the Amazonia and the Andes, modern fire pattern is strongly allied to human activity. On the flank of the Andes, forests that have never burned are being eroded by fire spreading downslope from grasslands. Species of these same forests are being forced to migrate upslope due to warming and will encounter a firm artificial fire boundary of human activity.

Documenting the impacts of climate change and human activities on tropical rainforests is imperative for protecting tropical biodiversity and for better implementation of REDD+ and UN Sustainable Development Goals. Recent advances in very high-resolution satellite sensor systems (i.e., WorldView-3), computing power, and machine learning (ML) have provided improved mapping of fine-scale changes in the tropics. However, approaches so far focused on feature extraction or the extensive tuning of ML parameters, hindering the potential of ML in forest conservation mapping by not using textural information, which is found to be powerful for many applications. Additionally, the contribution of shortwave infrared (SWIR) bands in forest cover mapping is unknown. The objectives were to develop end-to-end mapping of the tropical forest using fully convolution neural networks (FCNNs) with WorldView-3 (WV-3) imagery and to evaluate human impact on the environment using the Betampona Nature Reserve (BNR) in Madagascar as the test site. FCNN (U-Net) using spatial/textural information was implemented and compared with feature-fed pixel-based methods including Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN). Results show that the FCNN model outperformed other models with an accuracy of 90.9%, while SVM, RF, and DNN provided accuracies of 88.6%, 84.8%, and 86.6%, respectively. When SWIR bands were excluded from the input data, FCNN provided superior performance over other methods with a 1.87% decrease in accuracy, while the accuracies of other models—SVM, RF, and DNN—decreased by 5.42%, 3.18%, and 8.55%, respectively. Spatial–temporal analysis showed a 0.7% increase in Evergreen Forest within the BNR and a 32% increase in tree cover within residential areas likely due to forest regeneration and conservation efforts. Other effects of conservation efforts are also discussed.

The Himalaya is a mosaic of complex socio-ecological systems (SESs) characterized by a wide diversity of altitude, climate, landform, biodiversity, ethnicity, culture, and agriculture systems, among other things. Identifying the distribution of SESs is crucial for integrating and formulating effective programs and policies to ensure human well-being while protecting and conserving natural systems. This work aims to identify and spatially map the boundaries of SESs to address the questions of how SESs can be delineated and what the characteristics of these systems are. The study was carried out for the state of Uttarakhand, India, a part of the Central Himalaya. The presented approach for mapping and delineation of SESs merges socio-economic and ecological data. It also includes validation of delineated system boundaries. We used 32 variables to form socio-economic units and 14 biophysical variables for ecological units. Principal component analysis followed by sequential agglomerative hierarchical cluster analysis was used to delineate the units. The geospatial statistical analysis identified 6 socio-economic and 3 ecological units, together resulting in 18 SESs for the entire state. The major characteristics for SESs were identified as forest types and agricultural practices, indicating the influence and dependency of SESs on these two features. The database would facilitate diverse application studies in vulnerability assessment, climate change adaptation and mitigation, and other socio-ecological studies. Such a detailed database addresses particularly site-specific characteristics to reduce risks and impacts. Overall, the identified SESs will help in recognizing local needs and gaps in existing policies and institutional arrangements, and the given methodological framework can be applied for the entire Himalayan region and for other mountain systems across the world.

ABSTRACTOngoing climate change is expected to alter forests by affecting forest productivity, with implications for the ecological functions of these systems. Despite its great dendrochronological potential, little research has been conducted into the use of wood density as a proxy for determining sensitivity to climate variability in Mexico. The response of Abies durangensis Martínez, in terms of wood density and growth ring width, to monthly climatic values (mean temperature, accumulated precipitation and the drought index SPEI) was analyzed through correlation analysis. Abies durangensis presents a high response, in terms of radial growth, to climatic conditions. Tree-ring widths are more sensitive to hydroclimatic variables, whereas wood density values are more sensitive to temperature. In particular, mean (MeanD) and minimum (MND) wood density values are more sensitive to climate than maximum (MXD). We found very marked spatial variations that indicate that A. durangensis responds differently to drought conditions depending on the indices of density.

The appetite for up-to-date information about the earth’s surface is ever increasing, as such information provides a basis for a large number of applications. These include the earth’s resource detection and evaluation, land cover and land use change monitoring together with other vast environmental studies such as climate change assessment. Due to the advantages of repetitive data acquisition, the synoptic view, together with the varied spatial resolution it provides, and its available historically achieved dataset, remote sensing earth observation has become the major preferred data source for various earth studies. This study assesses land cover change detection of the land cover products (2013_2014 and 2017_2018) derived from earth observation.There are vast number of change detection methodologies and techniques with some still emerging. This study embarked on post classification change detection methodology which entailed morphological and spectral filtering techniques. The 10 land cover classes that were assessed for change detection are: natural wooded land, shrubland, grassland, waterbodies, wetlands, barren lands, cultivated, built-up, planted forest together with mines and quarries. The change detection accuracy result was 74.97%. Through the likelihood analysis, the likelihood for change to occur (e.g. cultivated to grassland) and unlikelihood of change to occur (e.g. built-up to planted forest), resulted in 72.2% areas of potential realistic change.The change detection results, further depend on the quality, compatibility and accuracy of the input land cover datasets. The application of different ancillary data together with different modelling techniques for land cover classification also affect the true reflectance of land cover change detection. Therefore extra caution should be exercised when analysing change detection so as to provide true and reliable changes.

Ecological vulnerability is the focus of research on global environmental impact, regional sustainable development, ecological civilization, and green development. There are eight deserts and four sandy lands in northern China. The ecological environment is sensitive to climate change and human activities. It is of great significance to carry out long-term sequential ecological vulnerability assessments. Therefore, taking northern China as the research area, this paper selects 13 data indicators such as climate, topography, and soil based on the ecological sensitivity-ecological recovery-ecological pressure model (SPR) and uses the spatial principal component analysis method (SPCA) to quantitatively evaluate the spatial and temporal differentiation characteristics and driving forces of ecological vulnerability in this area from 1980 to 2020. The results showed that areas with extreme, severe, and moderate vulnerability dominated northern China, accounting for 74.58% of the total area. The analysis revealed a decrease in ecological vulnerability from west to east and north to south. Meanwhile, from the perspective of timing, the overall level of ecological vulnerability showed an upward trend before 2000, and the overall level of ecological vulnerability continued to decline after 2000, and the quality of the ecological environment improved. During the study period, areas in northern China with severe vulnerability and slight vulnerability showed a change of 15.53% and -14.01%, respectively. The main reason for the change in ecological vulnerability is the frequent transformation between forest land, grassland, water, and cultivated land. In addition, the study found a spatial autocorrelation of ecological vulnerability of northern China and a significantly positive correlation. After 2000, the spatial aggregation of vulnerability was high-high cluster, which was mainly distributed in northwest China. The study’s findings will provide a robust scientific basis for ecosystem management and sustainable development.

The latest international climate documents emphasize the great importance of protective forest stands in ensuring the sustainable development of agriculture, and the main requirement is the use of the forest-forming factor by landowners in the interests of improving the environment. In Russia, until recently, the ecological significance of forest plantations was underestimated, which created the ground for discussions about their effectiveness. In this regard, a new approach is proposed that emphasizes the positive impact of forests, including sustainable development, environmental security of the agricultural sector and reducing the degradation of agricultural land. The purpose of the work was an economic assessment and regularities of the dynamics of humus-regulating and nitrogen-phosphorus-potassium (NPK)-regulating efficiency of protective forest plantations on lands with deflation-hazardous soils. By means of a system analysis, the change in the soil cover of land use due to the influence of forest plantations on the balance of soil fertility elements in forested cells is comprehensively analyzed. The different spatial placement of trees from each other under different degrees of deflation in semiarid conditions is modeled. These models are used to determine the nature of the dynamics of soil nutrients in forested areas: in protection zones and outside protection. It is established that the anti-deflationary effect of agroforestry depends on the indicator of the protective forest cover of the land, the level of deflationary danger, and the operational life of the plantings. In semiarid conditions, it increases in proportion to the increase in the protection of land and amounts to EUR 376–EUR 4222 ha−1. With an increase in the intensity of deflation to the level of dust storms, the prevented damage from the loss of soil nutrients increases almost four times. In systems of plantings from early-maturing fast-growing rocks, the anti-deflationary effect is 6–7% higher on an average annual basis than in systems of plantings from long-lasting, slow-growing rocks. The greatest efficiency of forest reclamation in ensuring a positive balance of humus and NPK substances in the soil (EUR 1002–EUR 4222 ha−1) is achieved when placing plantings after 15 H. The study will confirm the need to subsidize the integration of trees into farm systems.

Long-term (21–30 years) erosional responses of rainforest terrain in the Upper Segama catchment, Sabah, to selective logging are assessed at slope, small and large catchment scales. In the 0.44 km 2 Baru catchment, slope erosion measurements over 1990–2010 and sediment fingerprinting indicate that sediment sources 21 years after logging in 1989 are mainly road-linked, including fresh landslips and gullying of scars and toe deposits of 1994–1996 landslides. Analysis and modelling of 5–15 min stream-suspended sediment and discharge data demonstrate a reduction in storm-sediment response between 1996 and 2009, but not yet to pre-logging levels. An unmixing model using bed-sediment geochemical data indicates that 49 per cent of the 216 t km −2 a −1 2009 sediment yield comes from 10 per cent of its area affected by road-linked landslides. Fallout 210 Pb and 137 Cs values from a lateral bench core indicate that sedimentation rates in the 721 km 2 Upper Segama catchment less than doubled with initially highly selective, low-slope logging in the 1980s, but rose 7–13 times when steep terrain was logged in 1992–1993 and 1999–2000. The need to keep steeplands under forest is emphasized if landsliding associated with current and predicted rises in extreme rainstorm magnitude-frequency is to be reduced in scale.

Abstract. Climate impact assessments require information about climate change at regional and ideally also local scales. In dendroecological studies, this information has traditionally been obtained using statistical methods, which preclude the linkage of local climate changes to large-scale drivers in a process-based way. As part of recent efforts to investigate the impact of climate change on forest ecosystems in Bavaria, Germany, we developed a high-resolution atmospheric modelling dataset, BAYWRF, for this region over the thirty-year period of September 1987 to August 2018. The atmospheric model employed in this study, the Weather Research and Forecasting (WRF) model, was configured with two nested domains of 7.5 and 1.5 km grid spacing centred over Bavaria and forced at the outer lateral boundaries by ERA5 reanalysis data. Using an extensive network of observational data, we evaluate (i) the impact of using grid analysis nudging for a single-year simulation of the period of September 2017 to August 2018 and (ii) the full BAYWRF dataset generated using nudging. The evaluation shows that the model represents variability in near-surface meteorological conditions generally well, although there are both seasonal and spatial biases in the dataset that interested users should take into account. BAYWRF provides a unique and valuable tool for investigating climate change in Bavaria with high interdisciplinary relevance. Data from the finest-resolution WRF domain are available for download at daily temporal resolution from a public repository at the Open Science Framework (Collier, 2020; https://doi.org/10.17605/OSF.IO/AQ58B).

Abstract. First we review several syntheses of paleodata (pollen, lake-levels) showing the climate variations in China and Mongolia from the last glacial maximum to Present and in particular the precipitation increase at mid Holocene related to enhanced monsoon. All these results concur to a much enhanced monsoon on most of China during the first half of the Holocene. Second we present, in some details, a temporal study of a core (Lake Bayanchagan, Inner Mongolia) located in an arid region at the edge of the present East Asian Monsoon (EAM) influence and then sensitive to climatic change. This study involves pollen data together with other macro-remains and stable isotope curve to obtain a robust climate reconstruction. This study shows a long wet period between 11 000 and 5000 years BP divided in two parts, a warmer one from 11 000 and 8000 (marked by large evapotranspiration) and a cooler one more favourable to forest expansion. Third, we present a spatial study based on pollen data only and covering all China and Mongolia at 6000 years BP, but using a mechanistic modelling approach, in an inverse mode. It has the advantage to take into account environmental context different from the present one (lower atmospheric CO2, different seasonality). This study shows temperature generally cooler than present one in southern China, but a significant warming was found over Mongolia, and a slightly higher in northeast China. Precipitation was generally higher than today in southern, northeast China, and northern Mongolia, but lower or similar to today in northwest China and north China. Enhanced EAM was then found in the southern half of China and in northeast China.

Abstract. First we review several syntheses of paleodata (pollen, lake-levels) showing the climate variations in China and Mongolia from the last glacial maximum to Present and in particular the precipitation increase at mid Holocene related to enhanced monsoon. All these results concur to a much enhanced monsoon on most of China during the first half of the Holocene. Second we present, in some details, a temporal study of a core (Lake Bayanchagan, Inner Mongolia) located in an arid region at the edge of the present East Asian Monsoon (EAM) influence and then sensitive to climatic change. This study involves pollen data together with other macro-remains and stable isotope curve to obtain a robust climate reconstruction. This studies shows a long wet period between 11 000 and 5000 years BP divided in two parts, a warmer one from 11 000 and 8000 (marked by large evapotranspiration) and a cooler one more favourable to forest expansion. Third, we present a spatial study based on pollen data only and covering all China and Mongolia at 6000 years BP, but using a mechanistic modelling approach, in an inverse mode. It has the advantage to take into account environmental context different from the present one (lower atmospheric CO2, different seasonality). This study shows temperature generally cooler than present one in southern China, but a significant warming was found over Mongolia, and a slightly higher in Northeast China. Precipitation was generally higher than today in southern, Northeast China, and northern Mongolia, but lower or similar to today in Northwest China and North China. Enhanced EAM was then found in the southern half of China and in Northeast China.

SUMMARYThe availability of spatial data sourced from either field-derived or satellite-based systems has created new opportunities to estimate and/or monitor changes in carbon sequestration rates, climate change impacts or the potential habitat alterations occurring across large landscapes. However, an effort to create models is not standardized, in part, due to different needs and data sources available for the models. For example, data may have different spatial resolutions with varying degrees of complexity in regards to inputs and statistical methods. This study determines effects of 20, 15, 10, five and one km sampling resolutions on detection of changes in net primary productivity (NPP), occupancy selection criteria for areas to be included in the sample and identification of significant variables impacting NPP in Indonesia forests. Production forest designated for selective harvest was used to define the sampling areas. Variances explained by predictive models were similar across cell sizes although relative importance of variables was different. Partial dependence plots were used to search for potential thresholds or tipping points of NPP change as affected by an independent variable such as minimum daytime temperature. Applying different cell occupancy selection rules significantly changed the overall distribution of NPP values. The magnitude of those changes within a cell size varied with changes in cell size. The mean estimated NPP for production forests across Indonesia differed significantly at every sampling resolution and occupancy selection criteria. Lows ranged from 1.107 to 1.121 kg C m−2yr−1for the 1-km cell size for the three occupancy selection criteria with highs ranging from 1.245 to 1.189 kg C m−2yr−1for the 20-km cell size. The difference in NPP values between these two cell sizes for the three occupancy selection criteria extrapolates to a range in annual biomass of 132 × 106to 66 × 106t for the total area of production forests in Indonesia.

Studying the temporal pattern of savanna gross primary productivity (GPP) is essential for predicting the response of the biome to global environmental changes. In this study, MODIS satellite data coupled with eddy covariance based flux measurements were used to estimate GPP using a remote sensing based light use efficiency model across a significant rainfall gradient in the Northern Territory (NT) region of Australia. Closed forest that occurred in wet and often fireproof environments assimilated (GPP) 4–6 times more carbon than grasslands and Acacia woodlands that grow in arid environments (<600 mm annual rainfall). However, due to their small spatial extent, closed forests contributed <0.5% of the regional budget compared to savanna woodlands (86%) and grasslands (32%). Annual rainfall was found to exert a significant influence on GPP for different vegetation types except for closed forest which was less sensitive to above-average rainfall. Interannual variability in GPP showed that arid ecosystems had a higher variation (>20%) compared to woodlands and forest (∼5%). This variation in GPP was correlated with that of rainfall (R2 = 0.88, p<0.05). Analysis of the impact of wettest and driest years on GPP showed a strong positive correlation between the magnitude of the relative maxima in rainfall and maxima in GPP (R2 = 0.89, p<0.05). In contrast, the relative rainfall minima exhibited an insignificant relationship with relative GPP minima (R2 = 0.45, p = 0.07). These findings provide valuable information on the carbon uptake across the savanna biome and show the sensitivity of different vegetation systems to rainfall, a variable that may change in quantity and variability with projected climate change. Such data also show regions of high levels of carbon that could be linked with savanna management to protect the resources in the Australian savannas.

Non-glaciated mountain headwater catchments feature high-resolution geomorphic archives, which provide important insight into erosive processes and sediment dynamics in mountain ranges. As such the Valle de la Fueva catchments in the southern Pyrenees present high-lying talus remnants, extensive denudation surfaces (pediments), deeply incised tributary ravines, and low-lying fluvial-cut terraces. Based on geomorphic analyses and absolute dating using terrestrial cosmogenic nuclides and optically stimulated luminescence, a (late stage) catchment erosion model for the Valle de la Fueva was elaborated and indicates successive development stages of (i) lasting pedimentation under cold-climate conditions during Marine Isotope Stages 4–2, (ii) rapid fluvial dissection, sediment remobilization and downcutting of ravines in response to the last major climate transition and establishing interglacial conditions, and (iii) late stage fluvial incision after 3–4 ka due to regionally increased flood magnitudes, and/or intensification of agriculture and forest management. Valle de la Fueva headwater catchment analysis indicated that the styles and magnitudes of basin surface processes were directly correlated with the amplitude and nature of paleoclimatic changes, modified by the interplay of environmental parameters. In contrast to large-scale fluvial systems, mountain headwater catchments seemed to be less afflicted with temporal and spatial averaging biases. They are thus useful targets for investigating direct climate change effects, surface process coupling, and non-linear response mechanisms in Quaternary fluvial systems.

Abstract. Large amount of organic carbon is stored in high latitude soils. A substantial proportion of this carbon stock is vulnerable and may decompose rapidly due to temperature increases that are already greater than the global average. It is therefore crucial to quantify and understand carbon exchange between the atmosphere and subarctic/arctic ecosystems. In this paper, we combine an arctic-enabled version of the process-based dynamic ecosystem model, LPJ-GUESS (version LPJG-WHyMe-TFM) with comprehensive observations of terrestrial and aquatic carbon fluxes to simulate long-term carbon exchange in a subarctic catchment comprising both mineral and peatland soils. The model is applied at 50 m resolution and is shown to be able to capture the seasonality and magnitudes of observed fluxes at this fine scale. The modelled magnitudes of CO2 uptake generally follow the descending sequence: birch forest, non-permafrost Eriophorum, Sphagnum and then tundra heath during the observation periods. The catchment-level carbon fluxes from aquatic systems are dominated by CO2 emissions from streams. Integrated across the whole catchment, we estimate that the area is a carbon sink at present, and will become an even stronger carbon sink by 2080, which is mainly a result of a projected densification of birch forest and its encroachment into tundra heath. However, the magnitudes of the modelled sinks are very dependent on future atmospheric CO2 concentrations. Furthermore, comparisons of global warming potentials between two simulations with and without CO2 increase since 1960 reveal that the increased methane emission from the peatland could double the warming effects of the whole catchment by 2080 in the absence of CO2 fertilization of the vegetation. This is the first process-based model study of the temporal evolution of a catchment-level carbon budget at high spatial resolution, integrating comprehensive and diverse fluxes including both terrestrial and aquatic carbon. Though this study also highlights some limitations in modelling subarctic ecosystem responses to climate change including aquatic system flux dynamics, nutrient limitation, herbivory and other disturbances and peatland expansion, our application provides a mechanism to resolve the complexity of carbon cycling in subarctic ecosystems while simultaneously pointing out the key model developments for capturing complex subarctic processes.

A rapid increase in the world’s population over the last century has triggered the transformation of the earth surface, especially in urban areas, where more than half of the global population live. Ghana is no exception and a high population growth rate, coupled with economic development over the last three decades, has transformed the Greater Accra region into a hotspot for massive urban growth. The urban extent of the region has expanded extensively, mainly at the expense of the vegetative cover in the region. Although urbanization presents several opportunities, the environmental and social problems cannot be underestimated. Therefore, the need to estimate the rate and extent of land use/land cover changes in the region and the main drivers of these changes is imperative. Geographic Information Systems (GIS) and remote sensing techniques provide effective tools in studying and monitoring land-use/land-cover change over space and time. A post classification change detection of multiple Landsat images was conducted to map and analyse the extent and rate of land use/land cover change in the region between 1991 and 2015. Subsequently, the urban extent of the region was forecasted for the year 2025 using the Markov Chain and the Multi-Layer Perceptron neural network, together with drivers representing proximity, biophysical, and socio-economic variables. The results from the research revealed that built-up areas increased by 277% over the 24-year study period. However, forest areas experienced massive reduction, diminishing from 34% in 1991 to 6.5% in 2015. The 2025 projected land use map revealed that the urban extent will massively increase to cover 70% of the study area, as compared to 44% in 2015. The urban extent is also anticipated to spill into the adjoining districts mainly on the western and eastern sides of the region. The success of this research in generating a future land-use map for 2025, together with the other significant findings, demonstrates the usefulness of spatial models as tools for sustainable city planning and environmental management, especially for urban planners in developing countries.

Ecosystem goods and services derived from estuaries have sustained coastal livelihoods in the Western Indian Ocean (WIO) region throughout recorded history. Estuaries provide fertile and seasonally irrigated space for planting crops, mangrove products for construction and fuel, and fish as a protein source. Human population growth and an escalating demand for natural resources threaten estuarine critical habitats and their functioning, exacerbated by the effects of climate change. Decadal and seasonal land-use and land-cover (LULC) changes in the Ruvu Estuary in Tanzania were investigated through analysis of Landsat 5/8 and Sentinel-2 satellite images. The estuary is river-dominated and truncated near the coast during high river flow, with tidal influence extending approximately 12 km upstream during low river flow. LULC change detection targeting nine classes (water, developed, barren, forest, grasslands, cultivated, mangroves, wetlands and mudflats) showed that estuary-associated wetlands and mangroves had declined significantly over the past two decades (1995-2016) making way for developed land (growth of Bagamoyo Town), cultivated land (agricultural expansion with increasing population) and grasslands (coastal habitat changes). Seasonal LULC changes were conversion of wetlands to cultivated land after the wet season, and transformation of fallow wetlands to grasslands. The estuarine fishery relied on a small number of mainly freshwater and marine migrant species, compared to a highly diverse mix of mainly marine species in the nearby coastal fishery. The sparsity of quantitative fisheries data, spectral confusion when modelling land-cover change, and absence of household survey data to assess livelihood activities remain major information gaps. Generalized recommendations for improving socio-ecological change studies in WIO estuarine systems are provided.

An ever-increasing demand for agriculture while conserving biodiversity, maintaining livelihoods, and providing critical ecosystem services is one of the largest challenges for tropical land management across the Central American Isthmus today. Climatic and anthropogenic drivers threaten to cause changes in the forest cover and composition for this region, and therefore, understanding the dynamics of these systems and their variability across space and through time is important for discerning current and future responses. Such information is of value especially for risk mitigation, planning, and conservation purposes. The understanding of the forests, water, and land use for this region through time is currently limited, yet it is essential for understanding current patterns of change, particularly with reference to: (i) forest fragmentation; (ii) water availability; and (iii) land management. Through the examination of biotic (e.g., pollen, diatoms, and Sporormiella) and abiotic (e.g., δ 18O, CaCO3, and magnetic susceptibility) proxies, extracted from environmental archives, evidence for longer-term environmental changes can be inferred and linked to drivers of change including climate, burning, and human activities. Proxy environmental data from terrestrial depositional archives across the Central American Isthmus were identified and mapped following best practice for systematic evidence synthesis. Results from the evidence base were summarised to show the spatial and temporal extent of the published datasets. A total of 12,474 articles were identified by a comprehensive search in three major bibliographic databases. From these, 425 articles were assessed for relevance at full-text, and 149 fully met inclusion criteria for the review. These articles yielded 648 proxy records in 167 study sites that were mapped on an interactive map with filters to allow full exploration of the evidence base. Just under half of the studies were published in the last decade. Most studies extracted their data from lake sediments, with a focus on moist tropical forests in lowland sites in Guatemala, Belize, and Mexico. The largest data gaps in the evidence base are Honduras, Nicaragua, Panama, and El Salvador. There are also significant evidence gaps for dry tropical forests, coniferous forests, mangroves, and grasslands. Most of the studies assessed had methodological or presentational limitations that make future meta-analysis difficult and significantly affect the ability to draw conclusions that are helpful for future decision-making. A degree of standardisation, transparency, and repeatability in reporting would be beneficial to harness the findings of the existing evidence base and to shape future research in this geographical area. The systematic map of the evidence base highlights six key review topic areas that could be targeted, if the raw data could be obtained, including: (i) dating uncertainty and standardising reporting; (ii) land use change across space and time; (iii) dispersal pathways of agriculture; (iv) the role and impacts of fire and burning; (v) changes in hydro-climate, water availability, and the risk of tropical storms; and (vi) forest resilience and recovery.

Severe convective windstorms and tornadoes regularly hit the territory of Russia causing substantial damage and fatalities. An analysis of the climatology and formation environments of these events is essential for risk assessments, forecast improvements and identifying of links with the observed climate change. In this paper, we present an analysis of severe convective windstorms, i.e., squalls and tornadoes reported between 1984 and 2020 in the Perm region (northeast of European Russia), where a local maximum in the frequency of such events was previously found. The analysed database consists of 165 events and includes 100 squalls (convective windstorms), 59 tornadoes, and six cases with both tornadoes and squalls. We used various information to compile the database including weather station reports, damage surveys, media reports, previously presented databases, and satellite images for windthrow. We found that the satellite images of damaged forests are the main data source on tornadoes, but their role is substantially lower for windstorm events due to the larger spatial and temporal scale of such events. Synoptic-scale environments and associated values of convective indices were determined for each event with a known date and time. Similarities and differences for the formation conditions of tornadoes and windstorms were revealed. Both squalls and tornadoes occur mostly on rapidly moving cold fronts or on waving quasi-stationary fronts, associated with low-pressure systems. Analyses of 72-h air parcel backward trajectories shows that the Caspian and Aral Seas are important sources of near-surface moisture for the formation of both squalls and tornadoes. Most of these events are formed within high CAPE and high shear environments, but tornadic storms are generally characterised by a higher wind shear and helicity. We also differentiated convective storms that caused forest damage and those did not. We found the composite parameter WMAXSHEAR is the best discriminator between these two groups. In general, storm events causing windthrow mainly occur under conditions more favourable for deep well-organised convection. Thus, forest damage can be considered as an indicator of the storm severity in the Perm region and in adjacent regions with forest-covered area exceeding 50%.

Agriculture is considered one of the most vulnerable sectors to climate change. In addition to rainfed agriculture, irrigated crops such as rice have been developed in recent decades along the Senegal River. This new crop requires reliable information and monitoring systems. Remote sensing data have proven to be very useful for mapping and monitoring rice fields. In this study, a rice classification system based on machine learning to recognize and categorize rice is proposed. Physical interpretations of rice with other land cover classes in relation to the spectral signature should identify the optimal periods for mapping rice plots using three machine learning methods including Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART). The database is composed of field data collected by GPS and high spatial resolution (10 to 30 m) satellite data acquired between January and May 2018. The analysis of the spectral signature of different land cover show that the ability to differentiate rice from other classes depends on the level of rice development. The results show the efficiency of the three classification algorithms with overall accuracies and Kappa coefficients for SVM (96.2%, 94.5%), for CART (97.6%, 96.5%) and for RF (98% 97.1%) respectively. Unmixing analysis was made to verify the classification and compare the accuracy of these three algorithms according to their performance.

Abstract. The frequency of flood events has increased in recent years most probably due to the climate change. Flood mapping is thus essential for flood modelling, hazard and risk analyses and can be performed by using the data of optical and microwave satellite sensors. Although optical imagery-based flood analysis methods have been often used for the flood assessments before, during and after the event; they have the limitation of cloud coverage. With the increasing temporal availability and spatial resolution of SAR (Synthetic Aperture Radar) satellite sensors, they became popular in data provision for flood detection. On the other hand, their processing may require high level of expertise and visual interpretation of the data is also difficult. In this study, a fusion approach for Sentinel-1 SAR and Sentinel-2 optical data for flood extent mapping was applied for the flood event occurred on August 8th, 2018, in Ordu Province of Turkey. The features obtained from Sentinel-1 and Sentinel-2 processing results were fused in random forest supervised classifier. The results show that Sentinel-2 optical data ease the training sample selection for the flooded areas. In addition, the settlement areas can be extracted from the optical data better. However, the Sentinel-2 data suffer from clouds which prevent from mapping of the full flood extent, which can be carried out with the Sentinel-1 data. Different feature combinations were evaluated and the results were assessed visually. The results are provided in this paper.

In order to foresee possible changes in the elementary composition of Arctic river waters, complex studies with extensive spatial coverage, including gradients in climate and landscape parameters, are needed. Here, we used the unique position of the Ob River, draining through the vast partially frozen peatlands of the western Siberia Lowland and encompassing a sizable gradient of climate, permafrost, vegetation, soils and Quaternary deposits, to assess a snap-shot (8–23 July 2016) concentration of all major and trace elements in the main stem (~3000 km transect from the Tom River confluence in the south to Salekhard in the north) and its 11 tributaries. During the studied period, corresponding to the end of the spring flood-summer baseflow, there was a systematic decrease, from the south to the north, of Dissolved Inorganic Carbon (DIC), Specific Conductivity, Ca and some labile trace elements (Mo, W and U). In contrast, Dissolved Organic Carbon (DOC), Fe, P, divalent metals (Mn, Ni, Cu, Co and Pb) and low mobile trace elements (Y, Nb, REEs, Ti, Zr, Hf and Th) sizably increased their concentration northward. The observed latitudinal pattern in element concentrations can be explained by progressive disconnection of groundwaters from the main river and its tributaries due to a northward increase in the permafrost coverage. A northward increase in bog versus forest coverage and an increase in DOC and Fe export enhanced the mobilization of insoluble, low mobile elements which were present in organo-ferric colloids (1 kDa—0.45 µm), as confirmed by an in-situ dialysis size fractionation procedure. The chemical composition of the sampled mainstream and tributaries demonstrated significant (p < 0.01) control of latitude of the sampling point; permafrost coverage; proportion of bogs, lakes and floodplain coverage and lacustrine and fluvio-glacial Quaternary deposits of the watershed. This impact was mostly pronounced on DOC, Fe, P, divalent metals (Mn, Co, Ni, Cu and Pb), Rb and low mobile lithogenic trace elements (Al, Ti, Cr, Y, Zr, Nb, REEs, Hf and Th). The pH and concentrations of soluble, highly mobile elements (DIC, SO4, Ca, Sr, Ba, Mo, Sb, W and U) positively correlated with the proportion of forest, loesses, eluvial, eolian, and fluvial Quaternary deposits on the watershed. Consistent with these correlations, a Principal Component Analysis demonstrated two main factors explaining the variability of major and trace element concentration in the Ob River main stem and tributaries. The DOC, Fe, divalent metals and trivalent and tetravalent trace elements were presumably controlled by a northward increase in permafrost, floodplain, bogs, lakes and lacustrine deposits on the watersheds. The DIC and labile alkaline-earth metals, oxyanions (Mo, Sb and W) and U were impacted by southward-dominating forest coverage, loesses and eluvial and fertile soils. Assuming that climate warming in the WSL will lead to a northward shift of the forest and permafrost boundaries, a “substituting space for time” approach predicts a future increase in the concentration of DIC and labile major and trace elements and a decrease of the transport of DOC and low soluble trace metals in the form of colloids in the main stem of the Ob River. Overall, seasonally-resolved transect studies of large riverine systems of western Siberia are needed to assess the hydrochemical response of this environmentally-important territory to on-going climate change.

The Abbay River Basin, which originates in Ethiopia, is a major tributary and main source of the Nile River Basin. Land cover and vegetation in the Abbay River Basin is highly susceptible to climate change. This study was conducted to investigate the trends of climate change for a period of thirty-six years (1980–2016) within selected stations of the basin by using the innovative trend analysis method, Mann–Kendall test, and Sen’s slope estimator test to investigate the mean annual precipitation and temperature variables. Changes in land cover and vegetation in the Abbay River Basin were studied for a period of thirteen years (2001–2013) by using remote sensing, GIS analysis, land cover classification, and vegetation detection methods to assess the land cover and vegetation in the basin. In addition, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Transformation Matrix were employed to analyze the spatial and temporal patterns of land cover and vegetation impacted by changes in climate. The result reflects that the trend of average annual temperature was remarkably increased (Φ = 0.12, Z = 0.75) in the 36-year period, and the temperature was increased by 0.5°C, although precipitation had slightly decreased during the same period. In the thirteen years’ period, forest land and water resource decreased by 3429.62 km2 and 81.45 km2, respectively. In contrast, an increment was observed in grassland (2779.33 km2), cultivated land (535.34 km2), bare land (43.08 km2), urban land (0.65 km2), and wetland (152.66 km2) in the same period. In the study, it was also observed a decrease of an NDVI value by 0.1 was observed in 2013 in the southern part of the basin. The findings of the present study illustrate a significant change in eco-hydrological conditions in the ARB with an adverse impact on the environment. Hydroclimatic changes caused the increase in temperature and decreasing trend in precipitation which significantly impacted the land cover and vegetation in the basin. The changes in land cover were mostly caused by global and local climate influence which mainly affects the hydroclimate and eco-hydrology systems of the basin. The result is consistent with that of the previous studies conducted elsewhere. The findings of this paper could help researchers to understand the eco-hydrological condition of the study basin and become a foundation for further studies.

From a climate change perspective, the governance of natural common-pool resources—the commons—is a key point in the challenge of transitioning to sustainability. This paper presents the main strategic advances of the São Paulo Urban Living Laboratory (ULL) regarding Food, Energy and Water (FEW Nexus) analysis and modelling at the border of a high biodiverse forest in a peri-urban region in southeast Brazil. It is a replicable and scalable method concerning FEW governance. The FEW Nexus is an analytical guide to actions that will enable a colossal set of innovative processes that the transition to sustainability presupposes. Sustainable governance of the FEW dimensions, seen as an innovation-based process, is approached by a decision making tool to understand the past and future dynamics of the system. The governance framework is based on a multi-criteria and multi-attribute set of sustainability-relevant factors used as indicators to model complex system dynamics (SD) and the stakeholders’ future expectations through a Delphi approach. Based on the three main dimensions of the Ecosystem Services Approach—Physical and Material Conditions, Attributes of Communities, and Rules-in-Use—the tool comprises thirteen specific sustainability indicators such as water and carbon footprints, land use social development, payment for ecosystem services, and land use gain indices. Its development was designed to generate a long-term network of socioenvironmental stakeholders’ decision making processes and collective learning about a higher level of sustainable systems. System Dynamics modelling demonstrates the associations between sustainability indicators and the impacts of payment for ecosystem services on the land use social development index, or on the trophic state index. The Delphi foresight approach, using the Promethee-Gaia method, allows us to understand the positions of multiple agents regarding the transition process. In this context, decision making tools can be very useful and effective in answering the “how to” questions of ULLs and paving the way for transition, providing collective planning and decision support frameworks for sustainability transition management.

The United Nations Sustainable Development Goals (SDGs) and their corresponding targets are significantly interconnected, with many interactions, synergies, and trade-offs between individual goals across multiple temporal and spatial scales. This paper proposes a framework for the Integrated Assessment Modelling (IAM) of a complex deltaic socio-ecological system in order to analyze such SDG interactions. We focused on the Sundarban Biosphere Reserve (SBR), India, within the Ganges-Brahmaputra-Meghna Delta. It is densely populated with 4.4 million people (2011), high levels of poverty, and a strong dependence on rural livelihoods. It is adjacent to the growing megacity of Kolkata. The area also includes the Indian portion of the world’s largest mangrove forest––the Sundarbans––hosting the iconic Bengal Tiger. Like all deltaic systems, this area is subject to multiple drivers of environmental change operating across scales. The IAM framework is designed to investigate socio-environmental change under a range of explorative and/or normative scenarios and explore associated policy impacts, considering a broad range of subthematic SDG indicators. The following elements were explicitly considered: (1) agriculture; (2) aquaculture; (3) mangroves; (4) fisheries; and (5) multidimensional poverty. Key questions that can be addressed include the implications of changing monsoon patterns, trade-offs between agriculture and aquaculture, or the future of the Sundarbans’ mangroves under sea-level rise and different management strategies. The novel, high-resolution analysis of SDG interactions allowed by the IAM will provide stakeholders and policy makers the opportunity to prioritize and explore the SDG targets that are most relevant to the SBR and provide a foundation for further integrated analysis.

Abstract. In topographically complex watersheds, landscape position and vegetation heterogeneity can alter the soil water regime through both lateral and vertical redistribution, respectively. These alterations of soil moisture may have significant impacts on the spatial heterogeneity of biogeochemical cycles throughout the watershed. To evaluate how landscape position and vegetation heterogeneity affect soil CO2 efflux (FSOIL) we conducted observations across the Weimer Run watershed (373 ha), located near Davis, West Virginia, for three growing seasons with varying precipitation (2010 – 1042 mm; 2011 – 1739 mm; 2012 – 1244 mm; precipitation data from BDKW2 station, MesoWest, University of Utah). An apparent soil temperature threshold of 11 °C at 12 cm depth on FSOIL was observed in our data – where FSOIL rates greatly increase in variance above this threshold. For analysis, FSOIL values above this threshold were isolated and examined. Differences in FSOIL among years were apparent by elevation (F4,633 = 3.17; p = 0.013) and by vegetation cover (F4, 633 = 2.96; p = 0.019). For the Weimer Run watershed, vegetation exerts the major control on soil CO2 efflux (FSOIL), with the plots beneath shrubs at all elevations for all years showing the greatest mean rates of FSOIL (6.07 μmol CO2 m-2 s-1) compared to plots beneath closed-forest canopy (4.69 μmol CO2 m-2 s-1) and plots located in open, forest gaps (4.09 μmol CO2 m-2 s-1) plots. During periods of high soil moisture, we find that CO2 efflux rates are constrained and that maximum efflux rates in this system occur during periods of average to below average soil water availability. These findings offer valuable insight into the processes occurring within these topographically complex, temperate and humid systems, and the interactions of abiotic and biotic factors mediating biogeochemical cycles. With possible changing rainfall patterns as predicted by climate models, it is important to understand the couplings between water and carbon cycling at the watershed and landscape scales, and their potential dynamics under global change scenarios.

Land Use Land Cover (LULC) changes analysis is one of the most useful methodologies to understand how the land was used in the past years, what types of detections are to be expected in the future, as well as the driving forces and processes behind these changes. In Ethiopia, Africa, the rapid variations of LULC observed in the last decades are mainly due to population pressure, resettlement programs, climate change, and other human- and nature-induced driving forces. Anthropogenic activities are the most significant factors adversely changing the natural status of the landscape and resources, which exerts unfavourable and adverse impacts on the environment and livelihood. The main goal of the present work is to review previous studies, discussing the spatiotemporal LULC changes in Ethiopian basins, to find out common points and gaps that exist in the current literature, to be eventually addressed in the future. A total of 25 articles, published from 2011 to 2020, were selected and reviewed, focusing on LULC classification using ArcGIS and ERDAS imagine software by unsupervised and maximum likelihood supervised classification methods. Key informant interview, focal group discussions, and collection of ground truth information using ground positioning systems for data validation were the major approaches applied in most of the studies. All the analysed research showed that, during the last decades, Ethiopian lands changed from natural to agricultural land use, waterbody, commercial farmland, and built-up/settlement. Some parts of forest land, grazing land, swamp/wetland, shrubland, rangeland, and bare/ rock out cropland cover class changed to other LULC class types, mainly as a consequence of the increasing anthropogenic pressure. In summary, these articles confirmed that LULC changes are a direct result of both natural and human influences, with anthropogenic pressure due to globalisation as the main driver. However, most of the studies provided details of LULC for the past decades within a specific spatial location, while they did not address the challenge of forecasting future LULC changes at the watershed scale, therefore reducing the opportunity to develop adequate basin-wide management strategies for the next years.

Remote Sensing (RS) and Geographic Information System (GIS) have been established as indispensable tools in the assessment of Land use / Land cover (LULC) change. RS and GIS are important for the monitoring, modelling and mapping of land use and land cover changes across a range of spatial and temporal scales, in order to assess the extent, direction, causes, and effects of the changes. Change detection has provided suitable and wide-ranging information to various decision support systems for natural resource management and sustainable development. The main objective of the study is to assess and evaluate the extent and direction of changes in LULC of Aniocha North Local Government Area (LGA), Delta State, Nigeria to explain the changes and identify some of their effects on both the livelihoods of the local people and the local environment, and also to explore some of the conservation measures designed to overcome problems associated with land use and land cover changes. Landsat 7 Enhanced Thematic Mapper (ETM+) of 2002 with 30 meters resolution and landsat 7 Enhanced Thematic Mapper (ETM) 2014satellite images as well as GIS techniques were used to monitor the changes and to generate maps of the LULC of the area in these periods. Supervised Land Use/Land Cover classification algorithm (Maximum likelihood with null class) was used in the analysis of classification. The classification result of LandSat ETM+ (2002) revealed that farmland accounted for 36.34% of the total LULC class, followed by savannah which accounted for 24.15%. Forest built up area, and waterbody constituted 20.42%, 16.46% and 2.62% respectively. Also, the result of LandSat ETM (2014) shows that forest accounted for 38.59% followed by farmland with 30.93%. Built up area covers 25.55% while savannah and river cover 2.86% and 2.08% respectively. The classification shows 83.26 % average accuracy and 79.16 % overall accuracy for 2002 while the 2014 accuracy assessment showed 95.06% average accuracy and 94.99% overall accuracy. Growing population pressure and its associated problems, such as the increasing demand for land and trees, poor institutional and socio-economic settings, and also unfavorable government policies, such as lack of land tenure security and poor infrastructure development, have been the major driving forces behind the LULC changes.