Search

Context For nearly three years, the COVID-19 pandemic has disrupted human well-being and livelihoods, communities, and economies in myriad ways with consequences for social-ecological systems across the planet. The pandemic represents a global shock in multiple dimensions that has already, and is likely to continue to have, far-reaching effects on land systems and on those depending on them for their livelihoods. Objectives We focus on the observed effects of the pandemic on landscapes and people composing diverse land systems across the globe. Methods We highlight the interrelated impacts of the pandemic shock on the economic, health, and mobility dimensions of land systems using six vignettes from different land systems on four continents, analyzed through the lens of socio-ecological resilience and the telecoupling framework. We present preliminary comparative insights gathered through interviews, surveys, key informants, and authors’ observations and propose new research avenues for land system scientists. Results The pandemic’s effects have been unevenly distributed, context-specific, and dependent on the multiple connections that link land systems across the globe. Conclusions We argue that the pandemic presents concurrent “natural experiments” that can advance our understanding of the intricate ways in which global shocks produce direct, indirect, and spillover effects on local and regional landscapes and land systems. These propagating shock effects disrupt existing connections, forge new connections, and re-establish former connections between peoples, landscapes, and land systems.

Introgressive hybridization is a process that enables gene flow across species barriers through the backcrossing of hybrids into a parent population. This may make genetic material, potentially including relevant environmental adaptations, rapidly available in a gene pool. Consequently, it has been postulated to be an important mechanism for enabling evolutionary rescue, that is the recovery of threatened populations through rapid evolutionary adaptation to novel environments. However, predicting the likelihood of such evolutionary rescue for individual species remains challenging. Here, we use the example of Zosterops silvanus, an endangered East African highland bird species suffering from severe habitat loss and fragmentation, to investigate whether hybridization with its congener Zosterops flavilateralis might enable evolutionary rescue of its Taita Hills population. To do so, we employ an empirically parameterized individual-based model to simulate the species' behaviour, physiology and genetics. We test the population's response to different assumptions of mating behaviour and multiple scenarios of habitat change. We show that as long as hybridization does take place, evolutionary rescue of Z. silvanus is likely. Intermediate hybridization rates enable the greatest long-term population growth, due to trade-offs between adaptive and maladaptive introgressed alleles. Habitat change did not have a strong effect on population growth rates, as Z. silvanus is a strong disperser and landscape configuration is therefore not the limiting factor for hybridization. Our results show that targeted gene flow may be a promising avenue to help accelerate the adaptation of endangered species to novel environments, and demonstrate how to combine empirical research and mechanistic modelling to deliver species-specific predictions for conservation planning.

Crop planting patterns are an important component of agricultural land systems. These patterns have been significantly changed due to the combined impacts of climatic changes and socioeconomic developments. However, the extent of these changes and their possible impacts on the environment, terrestrial landscapes and rural livelihoods are largely unknown due to the lack of spatially explicit datasets including crop planting patterns. To fill this gap, this study proposes a new method for spatializing statistical data to generate multitemporal crop planting pattern datasets. This method features a two-level model that combines a land-use simulation and a crop pattern simulation. The output of the first level is the spatial distribution of the cropland, which is then used as the input for the second level, which allocates crop censuses to individual gridded cells according to certain rules. The method was tested using data from 2000 to 2019 from Heilongjiang Province, China, and was validated using remote sensing images. The results show that this method has high accuracy for crop area spatialization. Spatial crop pattern datasets over a given time period can be important supplementary information for remote sensing and thus support a wide range of application in agricultural land systems.

Land degradation (LD) in Mali is prevalent and leads to an enduring environmental and humanitarian crisis. Farmers' ecological knowledge has proven to be a valuable tool in addressing its challenges. How farmers perceive LD affects how they deal with induced risks, and their responses to these perceptions will shape restoration options and outcomes. Therefore, this study assessed farmers' perceptions of LD along a climatic gradient in three regions of Mali. We interviewed 270 farmers, and we analyzed their responses using descriptive statistics and Spearman rank‐order correlation. We found that the respondents were aware of LD and have identified its key indicators and its impacts on their livelihoods. Moreover, we found that farmers' perceptions are not influenced by gender, age, or education level, but rather by agricultural training, participation in agricultural labor, the practice of fallowing, shortage of firewood, livestock, household size, appearance of some plant species and famine. Additionally, farmers' perceptions of LD vary along the climatic gradient as they correlate to different variables in each agro‐ecological zone. LD's impacts, however, decrease in severity along the north–south gradient, although they are linked to the same variables. As LD is seen through a reduction of ecosystem services provisioning capacity because of the local communities' heavy dependence on natural resources, actions should be geared towards agronomic and vegetative land management options. Such actions should prioritize context‐specific soil and water conservation techniques and proven indigenous practices.

Land use and land cover change (LULCC) is considered one of the major drivers of climate change, although climate change can also foster direct or indirect influences leading to LULCC. The objective of the presented study is to offer a strategic observation frame as the land use and land cover (LULC) transitions are grouped to define the cover flows (CFs). The Küçük Menderes River Basin (KMRB), which is located in the west of Turkey was examined as the case study. Through LULCC modelling via the employment of multi-layer perceptron (MLP), cellular automata (CA), and Markov Chain methods, future LULC maps were projected up to the horizon of 2050. Hydrologic responses of the basin to LULCC were determined by the developed hydrologic model, which is generated by the Soil and Water Assessment Tool (SWAT). The superimposed impacts of the examined effects of LULCC have been investigated by the CF types. This way, the individual impacts of the CFs have been assessed. In the case of the KMRB, projected annual runoffs for the year 2050 cover map represent a 9.06% reduction and the major responsible CF type for this reduction is the conversion from forest to non-irrigated agricultural land cover by 22.90%.

Made available in DSpace on 2021-06-25T10:56:06Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-07-01 Canopy height is an important attribute that allows characterizing the forest vertical structure and analyze changes in vegetation cover over time. The objective of this study is to develop an approach for a spatio-temporal analysis of the tropical forest canopy using multi-temporal photogrammetric images. The datasets based on film and digital cameras are used to generate canopy height models and extract structural variables (tree height, relative variance between tree heights, and density of higher trees in the upper canopy). The combination of these variables is used in the analysis. Each variable is segmented into ordinal categorical classes in its respective dataset with temporal class gradients being obtained between the periods of the multi-temporal datasets. Experiments were conducted in a tropical forest under regeneration and with diversity of tree species in different successional stages. Three sets of images (years 1978, 2010, and 2017) were used for analyzing canopy cover changes. A classification based on histograms of gradient classes indicated and quantified the most frequent behavior of the canopy over time. The results showed that the most significant variations in cover changes could be explained by 13 classes of temporal gradients, which described 88% of the canopy. This classification was validated with field data collected in sample plots. From the results, it can be concluded that the proposed approach provides accurate assessments of the spatio-temporal canopy cover changes for forest management. Department of Cartography UNESP - Universidade Estadual Paulista, Rua Roberto Simonsen 305 Department of Remote Sensing and Photogrammetry Finnish Geodetic Institute FGI National Land Survey of Finland Department of Cartography UNESP - Universidade Estadual Paulista, Rua Roberto Simonsen 305

Crop cultivation of northern peatlands emitted large amount of CO2 over the period 850–2010., When a peatland is drained and cultivated, it behaves as a notable source of CO2. However, we lack temporally and spatially explicit estimates of carbon losses from cultivated peatlands. Using a process-based land surface model that explicitly includes representation of peatland processes, we estimate that northern peatlands converted to croplands emitted 72 Pg C over 850–2010, with 45% of this source having occurred before 1750. This source surpassed the carbon accumulation by high-latitude undisturbed peatlands (36 to 47 Pg C). Carbon losses from the cultivation of northern peatlands are omitted in previous land-use emission assessments. Adding this ignored historical land-use emission implies an 18% larger terrestrial carbon storage since 1750 to close the historical global carbon budget. We also show that carbon emission per unit area decrease with time since drainage, suggesting that time since drainage should be accounted for in inventories to refine land-use emissions from cultivated peatlands.

In the Aral Sea region, significant land use/cover change (LUCC) occurred in the past 50 years, especially the shrinking of Aral Sea due to unreasonable usage of water resources under intensified agricultural activities. However, to date, regional climatic feedbacks on fine-scale exerted by such LUCC in Central Asia have not been studied clearly. In this study, ALARO-SURFEX regional climate model was used to perform climate simulations under different underlying surface scenarios with 4 km spatial resolution to explore the impacts of historical LUCC on summer climate during 1980-2015. Our results show that compared to default land surface conditions, the modified ones improved the model's ability in simulating temperature, precipitation, and surface energy fluxes. During the period 1980-2015, LUCC accelerated the warming trend, reduced the summer precipitation and altered allocation of surface energy fluxes. Exposed dry bottom of Aral Sea has undergone the most conspicuous warming, which caused increase of the 2 m maximum temperature, average temperature, and diurnal temperature range by 2.56 +/- 0.88 degrees C, 1.04 +/- 0.53 degrees C, and 3.42 +/- 1.10 degrees C, respectively, while minimum temperature decrease by 1.14 +/- 0.56 degrees C. The summer precipitation (mainly convective precipitation) decreased by about 2.33 mm overlay the exposed dry bottom of Aral Sea and approximately 400 km "buffer" region in its eastern side. Additionally, the energy balance changed as follows: -47.9, 50.19, -78.67, and -23.72 W m(-2) for net radiation, sensible heat, latent heat, and soil heat, respectively. Quantified contribution of LUCC on regional climate provides useful information for developing mitigation and adaption strategies under the global warming threat.

Tropical montane forests are important reservoirs of carbon and biodiversity and have a central role in the hydrological cycle. They are, however, very fragmented and degraded, leaving isolated remnants across the landscape. These montane forest remnants have considerable differences in forest structure, depending on factors such as tree species composition and degree of forest degradation. Our objectives were (1) to analyse the reliability of airborne laser scanning (ALS) in modelling forest structural heterogeneity, as described by the Gini coefficient (GC) of tree size inequality; (2) to determine whether models are improved by including tree species-sensitive spectral-temporal metrics from the Landsat time series (LTS); and (3) to evaluate differences between three forest remnants and different forest types using the resulting maps of predicted GC. The study area was situated in Taita Hills, Kenya, where indigenous montane forests have been partly replaced by single-species plantations. The data included field measurements from 85 sample plots and two ALS data sets with different pulse densities (9.6 and 3.1 pulses m(-2)). GC was modeled using beta regression. We found that GC was predicted more accurately by the ALS data set with a higher point density (a cross-validated relative root mean squared error (rRMSE(CV)) 13.9%) compared to ALS data set with lower point density (rRMSE(CV) 15.1%). Furthermore, important synergies exist between ALS and LTS metrics. When combining ALS and LTS metrics, rRMSE(CV) was improved to 12.5% and 13.0%, respectively. Therefore, if the LTS metrics are included in models, ALS data with lower pulse density are sufficient to yield similar accuracy to more expensive, higher pulse density data acquired from the lower altitude. In Ngangao and Yale, forest canopy has multiple layers of variable tree sizes, whereas elfin forests in Vuria are of more equal tree size, and the GC value ranges of the indigenous forests are 0.42-0.71, 0.20-0.74, and 0.17-0.76, respectively. The single-species plantations of cypress and pine showed lower values of GC than indigenous forests located in the same remnants in Yale, whereas Eucalyptus plantations showed GC values more similar to the indigenous forests. These results show the usefulness of GC maps for identifying and separating forest types as well as for assessing their distinctive ecologies.

The ecological environment and water resources of the Han River Basin (HRB) are incredibly susceptible to global warming. Naturally, the analysis of future runoff in HRB is believed to offer a theoretical basis for water resources management and ecological protection in HRB. The purpose of this study is to investigate and forecast the effects of climate change and land use change on runoff in the HRB. This study uses CMIP6 data to simulate three future climate change scenarios (SSP126, SSP245 and SSP585) for changes in precipitation and temperature, a CA-Markov model to simulate future land use change scenarios, and the Budyko framework to predict future runoff changes. The results show that: (1) Between 1974 and 2014, annual runoff (R) and annual precipitation (P) in the HRB decline not so significantly with a rate of 1.3673 mm/a and 1.2709 mm/a, while maximum temperature (Tmax) and minimum temperature (Tmin) and potential evapotranspiration (E0) show a non-significantly increasing trend with 0.0296 °C/a, 0.0204 °C/a and 1.3313 mm/a, respectively. Precipitation is considered as main contributor to the decline in Han River runoff, accounting for 54.1%. (2) In the HRB, overall precipitation and temperature are estimated to rise in the coming years, with all other hydrological variables. The comparison of precipitation rise under each scenario is as follows: SSP126 scenario > SSP585 scenario > SSP245 scenario. The comparison of the temperature increase under each scenario is as follows: SSP585 scenario > SSP245 scenario > SSP126 scenario. (3) In the HRB, farmland and grassland land will continue to decline in the future. The amount of forest acreage is projected to decline but not so significantly. (4) The future runoff of the HRB shows an increasing trend, and the future runoff varies in different scenarios and periods. Under the land use scenarios of maintaining LUCC1992–2014 and LUCC2040 and LUCC2060, the R change rates in 2015–2040 are 8.27–25.47% and −8.04–19.35%, respectively, and the R in 2040–2060 are 2.09–13.66% and 19.35–31.52%. At the same time, it is very likely to overestimate the future runoff of the HRB without considering the changes in the land use data of the underlying surface in the future.

Although erosional impacts of rainforest logging are well established, changes in hydrological dynamics have been less explored especially in the post-logging recovery phase following repeat-logging cycles and mature phase of oil palm plantation cycles. This study addresses this gap by comparing hydrological characteristics of five catchments in a steep land area of Sabah, Malaysian Borneo on a gradient of disturbance and recovery – twice-logged forest, 22 years recovery (LF2); multiple-logged forest, 8 years recovery (LF3); mature oil palm, 20 years old (OP); and two primary forests (PF and VJR) as controls. Each catchment was instrumented with water depth (converted to discharge), conductivity, temperature, and turbidity sensors, and a raingauge connected to a solar-powered datalogger recording data at 5-minute intervals from November 2011 to August 2013. Data were analysed via the flow-duration curve (FDC) supplemented by the runoff coefficient (RR) and coefficient of variation in discharge (QVAR) for aggregated characteristics, as well as via a combination of the Dunn's test and multiple-regression at the storm event scale for focused hydrological dynamics. Results show that OP is characterised by a relatively low RR (0.357) but with high responsiveness during storm events and very low baseflow (38.4% of total discharge). Discharge in the LF3 (RR = 0.796) is always the highest while having an intermediate level of responsiveness. LF2 with longer-term recovery shown a reduction in terms of discharge (RR = 0.640). Being the benchmark, the undisturbed forest (PF) has the most buffered storm response with the highest baseflow (67.9% of total discharge). Stormflow and baseflow are anomalously high and low respectively in the near-primary VJR catchment, but this probably reflects the shallow soils and short-stature rainforest associated with its igneous and metamorphic lithology. From a management aspect, although hydrological recovery is more advanced in the 22 years than in the 8-years post-logging catchment, full recovery is yet to be achieved and might be hastened by enrichment planting of the degraded forest. The low baseflow and flashy nature of the mature oil palm have major implications for downstream water supply in ENSO periods and flooding in La Nina periods. Steep lands in the humid tropics are best avoided from any form of landscape disturbance.

Deforestation threatens the earth's biodiversity and the ecosystem services upon which humans depend. Formal regulation is a key mechanism by which governments seek to protect forests. However, whether regulation can effectively protect remaining areas of the most threatened and most heavily cleared forests is unknown. We addressed this question using forest loss data for Queensland, Australia between 2000 and 2014 under existing vegetation clearing regulation (Vegetation Management Act 1999). This regulation is specifically designed to provide the greatest protection for threatened forest types that have already lost the greatest amount of their original extent. Importantly, enforcement and governance of this regulation is relatively strong allowing an assessment of regulation design. We applied path analysis to model the direct and indirect effects (mediated by variables representing deforestation pressure) of forest protection level on clearing rates. There was strong evidence for a decline in clearing rates over time, except of clearing for non-agricultural purposes. However, threatened forest types, which have already lost > 70% of their original extent and should have the greatest level of protection under the regulation, continue to be cleared 2.7–2.9 times faster than non-threatened forest types. There was also little evidence that the regulation has driven greater reductions over time in the clearing rates of threatened versus non-threatened forests types. There was much greater support for the indirect than direct effect of protection level. This is because protection level was correlated with variables associated with deforestation pressure, resulting in higher clearing rates for threatened compared to non-threatened forest types. We hypothesise that this arises because the additional protection afforded to threatened relative to non-threatened forests is insufficient to counter the continuing higher level of deforestation pressure on threatened forests. We argue that a potential solution is to build explicit targets for forest retention into regulation, below which no further forest loss is permitted. This could be combined with spatially targeted enforcement and incentive strategies where threats are highest.

Facing a growing and more affluent world population, changing climate and finite natural resources, world food systems will have to change in the future. The aim of the Agrimonde-Terra foresight study was to build global scenarios linking land use and food security, with special attention paid to overlooked aspects such as nutrition and health, in order to help explore the possible future of the global food system. In this article, we seek to highlight how the resulting set of scenarios contributes to the debate on land use and food security and enlarges the range of possible futures for the global food system. We highlight four main contributions. Combining a scenario building method based on morphological analysis and quantitative simulations with a tractable and simple biomass balance model, the proposed approach improves transparency and coherence between scenario narratives and quantitative assessment. Agrimonde-Terra's scenarios comprise a wide range of alternative diets, with contrasting underlying nutritional and health issues, which accompany contrasting urbanization and rural transformation processes, both dimensions that are lacking in other sets of global scenarios. Agrimonde-Terra's scenarios share some similarities with existing sets of global scenarios, notably the SSPs, but are usually less optimistic regarding agricultural land expansion up to 2050. Results suggest that changing global diets toward healthier patterns could also help to limit the expansion in agricultural land area. Agrimonde-Terra's scenarios enlarge the scope of possible futures by proposing two pathways that are uncommon in other sets of global scenarios. The first proposes to explore possible reconnection of the food industry and regional production within supranational regional blocs. The second means that we should consider that a 'perfect storm', induced by climate change and an ecological crisis combined with social and economic crises, is still possible. Both scenarios should be part of the debate as the current context of the COVID-19 pandemic shows.

The atmosphere and the land surface interact in multiple ways, for instance though the radiative-energy balance, the water cycle or the emission-deposition of natural and anthropogenic compounds. By modifying the land surface, land-use and land-cover changes (LULCCs) and land management changes (LMCs) alter the physical, chemical and biological processes of the biosphere and therefore all land-atmosphere interactions, from local to global scales. Through socio-economic drivers and regulatory policies adopted at different levels (local, regional, national or supranational), human activities strongly interfere in the land-atmosphere interactions, at those activities lead to a patchwork of natural, semi-natural, agricultural, urban and semi-urban areas. In this context, urban and peri-urban areas are of particular attention since land transformation can lead to important environmental impacts and affect the health and life of millions of people. The objectives of this review is to synthesize the existing experimental and modelling works that investigate physical, chemical and/or biogeochemical interactions between land surface and the atmosphere mainly in urban or peri-urban landscapes at regional and local scales. In the context of LULCCs, the importance of land-atmosphere interactions for climate and air quality have been analysed in many studies published over the last years, with a large range of spatial and temporal scales investigated. The conclusions from such a synthesis is first that (i) the description of land-use and land-management (e.g. areas concerned, type of crops, whether or not they are irrigated, quantity of fertilizers used and actual seasonality of application), including surface properties and emission sources, is inexistent (or very poor) in global and even more in regional climate models. Not taking into account these characteristics may bias the regional projections used for impact studies. (ii) Land-atmosphere interactions are often specific to the case study analysed; therefore, in this perspective, one can hardly propose general solutions or recommendations. (iii) Adaptation strategies, proposed after the evaluation of climatic impacts on the targeted resource have been derived, but are often biased as they do not account for feedbacks on local/regional climate. (iv) There is space for considering atmospheric chemistry, through land-atmosphere interactions, as a decision parameter for land-management, helping to maintain air quality and supporting ecosystem functioning. (v) There is a lack of an integrated tool, which includes the many different processes of importance in an operational model, to test different land use or land management scenarios at the scale of a territory.

The quantification of CO2 emissions from anthropogenic land use and land use change (eLUC) is essential to understand the drivers of the atmospheric CO2 increase and to inform climate change mitigation policy. Reported values in synthesis reports are commonly derived from different approaches (observation-driven bookkeeping and process-modelling) but recent work has emphasized that inconsistencies between methods may imply substantial differences in eLUC estimates. However, a consistent quantification is lacking and no concise modelling protocol for the separation of primary and secondary components of eLUC has been established. Here, we review differences of eLUC quantification methods and apply an Earth System Model (ESM) of Intermediate Complexity to quantify them. We find that the magnitude of effects due to merely conceptual differences between ESM and offline vegetation model-based quantifications is ~ 20 % for today. Under a future business-as-usual scenario, differences tend to increase further due to slowing land conversion rates and an increasing impact of altered environmental conditions on land-atmosphere fluxes. We establish how coupled Earth System Models may be applied to separate secondary component fluxes of eLUC arising from the replacement of potential C sinks/sources and the land use feedback and show that secondary fluxes derived from offline vegetation models are conceptually and quantitatively not identical to either, nor their sum. Therefore, we argue that synthesis studies should resort to the "least common denominator" of different methods, following the bookkeeping approach where only primary land use emissions are quantified under the assumption of constant environmental boundary conditions.

To date land-change science has devoted little attention to spatial policy and planning in urban landscapes despite the widely accepted premise that planning affects urban land change. This is primarily due to lack of relevant data and an underdeveloped theoretical understanding regarding the impact of spatial planning on urban land change. To be able to better analyse the role of spatial planning in urban development we need to distinguish: 1) the intentions expressed in the plans; 2) the means of implementation of the plans through governance processes and 3) the role of external conditions influencing implementation. Based on a synthesis of the current literature on how spatial planning is implemented in land-change models, and drawing from the literature on planning evaluation, we sketch a research agenda to further develop the understanding of these three components and their interconnections as well as their application in quantitative land-change modelling approaches for urban regions.

West African savannas are subject to regular fires, which have impacts on vegetation structure, biodiversity and carbon balance. An efficient and accurate mapping of burned area associated with seasonal fires can greatly benefit decision making in land management. Since coarse resolution burned area products cannot meet the accuracy needed for fire management and climate modelling at local scales, the medium resolution Landsat data is a promising alternative for local scale studies. In this study, we developed an algorithm for continuous monitoring of annual burned areas using Landsat time series. The algorithm is based on burned pixel detection using harmonic model fitting with Landsat time series and breakpoint identification in the time series data. This approach was tested in a savanna area in southern Burkina Faso using 281 images acquired between October 2000 and April 2016. An overall accuracy of 79.2% was obtained with balanced omission and commission errors. This represents a significant improvement in comparison with MODIS burned area product (67.6%), which had more omission errors than commission errors, indicating underestimation of the total burned area. By observing the spatial distribution of burned areas, we found that the Landsat based method misclassified cropland and cloud shadows as burned areas due to the similar spectral response, and MODIS burned area product omitted small and fragmented burned areas. The proposed algorithm is flexible and robust against decreased data availability caused by clouds and Landsat 7 missing lines, therefore having a high potential for being applied in other landscapes in future studies.

A universal turnaround has been detected in many countries of the World from shrinking to expanding forests. The forest area of western Europe expanded already in the 19th century. Such early trends of forest resources cannot be associated with the rapid rise of atmospheric carbon dioxide nor with the anthropogenic climate change, which have taken place since the mid 20th century. Modern, most recent spatial patterns of forest expansions and contractions do not correlate with the geography of climate trends nor with dry versus moist areas. Instead, the forest resources trends of nations correlate positively with UNDP Human Development Index. This indicates that forest resources of nations have improved along with progress in human well-being. Highly developed countries apply modern agricultural methods on good farmlands and abandon marginal lands, which become available for forest expansion. Developed countries invest in sustainable programs of forest management and nature protection. Our findings are significant for predicting the future of the terrestrial carbon sink. They suggest that the large sink of carbon recently observed in forests of the World will persist, if the well-being of people continues to improve. However, despite the positive trends in domestic forests, developed nations increasingly outsource their biomass needs abroad through international trade, and all nations rely on unsustainable energy use and wasteful patterns of material consumption.

Land use changes have been recognized to have considerable impacts on water; and vice versa, changes in water use and governance may have implications on land use and governance. This study analyzes recent land use/land cover (LULC) changes, and how changes in land use and water governance are perceived to affect land use and water-related risks in three case-study areas exposed to frequent flooding and inadequate or deteriorating water quality. The areas studied included the Vantaa basin in Finland, a section of the Grijalva basin in Mexico, and the Lower Xe Bang Fai basin in Laos. We show how there are complex and context-specific interrelationships between land use, water governance, and water-related risks in each study area. In a remote sensing analysis of LULC changes during the past 30 years, we found that LULC changes have been the most dramatic in Xe Bang Fai, Laos in the form of expanding agriculture and built-up areas; however, there has also been an expansion of built-up areas in the two other sites. According to our stakeholder scenario workshop data, analysis of policy documents and field visits, the nexus between land, water and risks is recognized to some extent in each study area. There have been modest shifts toward more integrated land use and water governance in Vantaa and Grijalva, while the integrated governance seems to have been most absent in Xe Bang Fai. Tighter integration of land and water policies is needed in all the three cases to manage the land use changes in a way that their effects on water-related risks will be minimized.

Tropical rainforests support a large proportion of the Earth's plant and animal species within a restricted global distribution, and play an important role in regulating the Earth's climate. However, the existing knowledge of forest types or habitats is relatively poor and there are large uncertainties in the quantification of carbon stock in these forests. Airborne Laser Scanning, using LiDAR, has advantages over other remote sensing techniques for describing the three-dimensional structure of forests. With respect to the habitat requirements of different species, forest structure can be defined by canopy height, canopy cover and vertical arrangement of biomass. In this study, forest patches were identified based on classification and hierarchical merging of a LiDAR-derived Canopy Height Model in a tropical rainforest in Sumatra, Indonesia. Attributes of the identified patches were used as inputs for k-medoids clustering. The clusters were then analysed by comparing them with identified forest types in the field. There was a significant association between the clusters and the forest types identified in the field, to which arang forests and mixed agro-forests contributed the most. The topographic attributes of the clusters were analysed to determine whether the structural classes, and potentially forest types, were related to topography. The tallest clusters occurred at significantly higher elevations (> 850 m) and steeper slopes (> 26 degrees) than the other clusters. These are likely to be remnants of undisturbed primary forests and are important for conservation and habitat studies and for carbon stock estimation. This study showed that LiDAR data can be used to map tropical forest types based on structure, but that structural similarities between patches of different floristic composition or human use histories can limit habitat separability as determined in the field.

Remote sensing data play an important role in the monitoring of forest changes. Methods are needed to provide objective estimates of forest loss to support monitoring efforts at various scales, and with increasing public availability of remote sensing data, accurate deforestation measurements at high temporal resolution are becoming more realistic. While several time series based methods have recently been described in the literature, there are few studies focusing on tropical forest areas, where low data availability and complex change processes present challenges to forest disturbance monitoring. Here, we present a robust data-driven method to track tropical deforestation and degradation based on Landsat time series data. Based on the previously reported Breaks For Additive Season and Trend Monitor (BFAST Monitor) method (Verbesselt etal., 2012), we show that BFAST Monitor, when applied to Landsat NDVI time series data using sequentially defined monitoring periods, can be used to track small-scale forest disturbances annually in an Afromontane forest system in southern Ethiopia. Using an ordinal logistic regression (OLR) approach, change magnitude, calculated based on differences between observed and expected values in a monitoring period, was found to be an essential predictor variable for disturbances. After applying a NDVI change magnitude threshold of − 0.065, overall accuracy was estimated to be 78%, and both producer's and user's accuracy of the disturbance class were estimated to be 73%. The method and results presented here are relevant to tropical countries engaged in REDD + for whom data availability and complex forest change dynamics limit the ability to reliably track forest disturbances over time.

Stomatal conductance (gs) affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (LSM). In common with many LSMs, CABLE does not differentiate between gs model parameters in relation to plant functional type (PFT), but instead only in relation to photosynthetic pathway. We constrained the key model parameter "g1", which represents plant water use strategy, by PFT, based on a global synthesis of stomatal behaviour. As proof of concept, we also demonstrate that the g1 parameter can be estimated using two long-term average (1960–1990) bioclimatic variables: (i) temperature and (ii) an indirect estimate of annual plant water availability. The new stomatal model, in conjunction with PFT parameterisations, resulted in a large reduction in annual fluxes of transpiration (~ 30% compared to the standard CABLE simulations) across evergreen needleleaf, tundra and C4 grass regions. Differences in other regions of the globe were typically small. Model performance against upscaled data products was not degraded, but did not noticeably reduce existing model–data biases. We identified assumptions relating to the coupling of the vegetation to the atmosphere and the parameterisation of the minimum stomatal conductance as areas requiring further investigation in both CABLE and potentially other LSMs. We conclude that optimisation theory can yield a simple and tractable approach to predicting stomatal conductance in LSMs.

Conservation of land resources is a promising strategy for sustainable agricultural intensification in order to adapt dryland farming systems to climate, market and other stresses. At a local level, factors that drive the adoption of conservation measures operate and interact in specific ways. Linking our knowledge of the local specifications of these drivers to regional and global patterns of vulnerability can significantly enhance our understanding of land-based adaptation to global change. However, the factors that influence the adoption of conservation practices remain actively debated. Therefore, this study presents a meta-analysis of case studies that investigate the adoption of soil and water conservation measures, as an important approach to resource conservation. Synthesising 63 adoption cases in the drylands of western Africa, this meta-analysis reveals a multitude of factors that drive the adoption of soil and water conservation practices. The drivers differ strongly between particular practices and methods of analysis used in the case studies. Contributing to the broader debate on resource conservation, the findings highlight the adoption of soil and water conservation measures as an emergent property of farming systems. They demonstrate the need to better understand the socio-ecological foundation of adoption and the pathways along which adoption evolves in space and time. This study concludes with methodological principles to advance future research on the factors that drive the adoption of soil and water conservation measures as a pre-requisite of improving land-based adaptation efforts.

Biodiversity underpins the health of ecosystems and the services they provide to society. Yet biodiversity is in rapid decline globally, despite commitments by world leaders to reduce the rate of loss (1). Monitoring is an essential part of biodiversity conservation, allowing governments and civil society to identify problems, develop solutions, and assess progress (2). Satellite imagery has emerged as a vital tool for monitoring the status of environmental parameters relevant to biodiversity conservation (3-5). Tackling a global challenge like biodiversity loss requires the assembly of global information products. Satellite remote sensing is especially useful at generating consistent observation records of key drivers of biodiversity change (i.e., land cover and land use dynamics, climate variables, and sea surface conditions) at a global level (6-8). A recent review of the needs of the biodiversity research and conservation communities for satellite remote sensing (9) uncovered three factors, which are rooted in government and commercial policies and actions, that ultimately have a disproportionate impact on the utility of satellite data for understanding changes in biodiversity. These factors are data continuity, data affordability, and data access., JRC.H.5-Land Resources Management

Policy and decision makers have to make difficult choices to improve the food security of local people against the background of drastic global and local changes. Ex-ante impact assessment using integrated models can help them with these decisions. This review analyses the state of affairs of the multi-scale modelling of policy interventions, with an emphasis on applications in developing countries and livestock systems. Existing models do not sufficiently capture the complexity of human-environment interactions across different scales, and especially the link between landscape and local market levels, and national and sub-national level policies and markets is missing. The paper suggests a step wise approach with increasing data needs to bridge this gap. Improvements need to be made at the description of effects of the distribution of local markets on price formation and the representation of farm diversity within a landscape. Analyses in contrasting agro-ecological systems are needed to derive generic summary functions that can be used as input for macro level model analyses. This is especially pertinent for macro level descriptions of crop and livestock production in relation to price developments and of the mosaic of different agricultural land use responses in regions with contrasting socio-economic conditions and developments.

Predicting soil erosion potential is important in watershed management. A rapidly growing Iranian population and climate change are expected to influence land use and soil sustainability. in recent years, northern Iran has experienced significant land use changes due to internal migration along the Caspian coast and conversion of forests and rangelands. Considering the effect of these changes in the future, the purpose of this study is to forecast land use patterns and investigate soil erosion scenarios using the Revised Universal Loss Equation and Markov Cellular Automata. Data from 1981 to 2011 were used as a baseline to estimate changes that might occur in 2030. The results reveal that the mean erosion potential will increase 45% from the estimated 104.52 t ha(-1) year(-1) in the baseline period. Moreover, the results indicate that land use change from forest area to settlements will be the most significant factor in erosion induced by land use change, showing the highest correlation among erosional factors. Projecting land use change and its effect on soil erosion indicate that conversion may be unsustainable if change occurs on land that is not suited to the use. The method predicts soil erosion under different scenarios and provides policymakers a basis for altering programs related to land use optimization and urban growth. Those results indicated the necessity of appropriate policies and regulations particularly for limiting land use changes and urban sprawl in areas of unfavorable soil erosion risk factors. Research Center for Spatial and Organizational Dynamics (CIEO) Portuguese Foundation for Science (FCT)