Editorial: Land degradation assessment with earth observation

For decades now, land degradation has been identified as one of the most pressing problems facing the planet. Alarming estimates are often published by the academic community and intergovernmental organisations claiming that a third of the Earth’s land surface is undergoing various degradation processes and almost half of the world’s population is already residing in degraded lands. Moreover, as land degradation directly affects vegetation biophysical processes and leads to changes in ecosystem functioning, it has a knock-on effect on habitats and, therefore, on numerous species of flora and fauna that become endangered or/and extinct

Modelling land cover change in a Mediterranean environment using Random Forests and a multi-layer neural network model

© 2016 IEEE.The present study seeks to identify the changes that have taken place in the Mediterranean island of Lesvos (Greece) between 1995 and 2007 in the seven main land cover types of the island. We also attempt to predict the changes that will occur by the year 2019. Three Landsat 5 TM summer scenes were used spanning 12 years. A combination of Random Forests (RF) classification with expert rules was then applied for achieving high overall classification accuracies (95%, 94% and 91%, respectively). The 1995 and 2001 classified data were then used to train a multi-layer perceptron neural network (MLPNN) model and predict land cover for the year 2007. Seven possible transitions were included in the MLPNN model which was trained with the 1995 and 2001 classified data successfully: accuracy rate of 93% after 5000 iterations. The quantity of change in each transition was modelled through Markov chain analysis. The modelling results for 2019 provide an anticipated prediction for the end of the decade: economic activity will remain centred to the agricultural sector, as crops and olive groves will expand. A rather unanticipated prediction is the significant increase in the area of forests

Landsat-based woody vegetation cover monitoring in Southern African savannahs

Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000km2 covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5m-pixel colour aerial photography to collect >15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80% and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot-spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas

Eco-geomorphological connectivity and coupling interactions at hillslope scale in drylands: Concepts and critical examples

The diagnosis of land degradation requires a deep understanding of ecosystem functioning and evolution. In dryland systems, in particular, research efforts must address the redistribution of scarce resources for vegetation, in a context of high spatial heterogeneity and non-linear response. This fact explains the prevalence of eco-hydrological perspectives interested in runoff processes and, the more recent, focused on connectivity as an indicator of system resource optimisation. From a geomorphological perspective and reviewing the concepts of eco-hydro-geomorphological interactions operating in ecosystems, this paper explores the effects of erosion on vegetation configuration through two case studies at different spatio-temporal scales. We focus on the structure-function linkage, specifically on how morphological traits relate with different stages in the erosional sequence, both in the abiotic and the biotic domain. Results suggest that vegetation dynamics are affected by structural boundary conditions at both scales, i.e. by surface armouring related with rock fragments at the patch scale, and by the degree of hillslope-channel coupling at the hillslope scale. Our preliminary results can serve as new working hypotheses about the structure-function interplay on hillslopes. All this, taking advantage of the recent technological achievements for acquiring very high-resolution geospatial data that offer new analytical possibilities in a range of scales

Modelling glacial lake outburst flood impacts in the Bolivian Andes

The Bolivian Andes have experienced sustained and widespread glacier mass loss in recent decades. Glacier recession has been accompanied by the development of proglacial lakes, which pose a glacial lake outburst flood (GLOF) risk to downstream communities and infrastructure. Previous research has identified three potentially dangerous glacial lakes in the Bolivian Andes, but no attempt has yet been made to model GLOF inundation downstream from these lakes. We generated 2-m resolution DEMs from stereo and tri-stereo SPOT 6/7 satellite images to drive a hydrodynamic model of GLOF flow (HEC-RAS 5.0.3). The model was tested against field observations of a 2009 GLOF from Keara, in the Cordillera Apolobamba, and was shown to reproduce realistic flood depths and inundation. The model was then used to model GLOFs from Pelechuco lake (Cordillera Apolobamba) and Laguna Arkhata and Laguna Glaciar (Cordillera Real). In total, six villages could be affected by GLOFs if all three lakes burst. For sensitivity analysis, we ran the model for three scenarios (pessimistic, intermediate, optimistic), which give a range of ~ 1100 to ~ 2200 people affected by flooding; between ~ 800 and ~ 2100 people could be exposed to floods with a flow depth ≥ 2 m, which could be life threatening and cause a significant damage to infrastructure. We suggest that Laguna Arkhata and Pelechuco lake represent the greatest risk due to the higher numbers of people who live in the potential flow paths, and hence, these two glacial lakes should be a priority for risk managers

High-resolution wetness index mapping: A useful tool for regional scale wetland management

Wetland ecosystems are key habitats for carbon sequestration, biodiversity and ecosystem services, yet in many they localities have been subject to modification or damage. In recent years, there has been increasing focus on effective management and, where possible, restoration of wetlands. Whilst this is highly laudable, practical implementation is limited by the high costs and unpredictable rates of success. Accordingly, there is a need for spatial information to guide restoration, ideally at the regional scale that land managers operate. In this study, we use high-resolution Light Detection and Ranging (LiDAR)-derived elevation, in conjunction with regional soil and land cover maps, to model the wetness potential of an area of conservation importance in north-west England. We use the Compound Topographic Index (CTI) as a measure for the site-specific wetness and potential to be receptive to wetland restoration. The resulting model is in agreement with the regional-scale distribution of wetlands and is clearly influenced by the topographic and soil parameters. An assessment of three representative case studies highlights the small scale features that determine the potential wetness of an area. For each site, the model results conform to the expected patterns of wetness, highlighting restoration and management activity. Furthermore, areas showing high potential wetness that may be suitable for wetland habitat creation, are highlighted. The increasing availability of LiDAR data at regional and national scales will allow studies of this nature to be undertaken at previously unobtainable resolutions. Simple models, such as implemented here, benefit from explainability and relatability and have clear potential for use by managers and conservation agencies involved in wetland restoration

A Random Forest-Cellular Automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales

This paper explores potential future land use/cover (LUC) dynamics in the Attica region, Greece, under three distinct economic performance scenarios. During the last decades, Attica underwent a significant and predominantly unregulated process of urban growth, due to a substantial increase in housing demand coupled with limited land use planning controls. However, the recent financial crisis affected urban growth trends considerably. This paper uses the observed LUC trends between 1991 and 2016 to sketch three divergent future scenarios of economic development. The observed LUC trends are then analysed using 27 dynamic, biophysical, socio-economic, terrain and proximity-based factors, to generate transition potential maps, implementing a Random Forests (RF) regression modelling approach. Scenarios are projected to 2040 by implementing a spatially explicit Cellular Automata (CA) model. The resulting maps are subjected to a multiple resolution sensitivity analysis to assess the effect of spatial resolution of the input data to the model outputs. Findings show that, under the current setting of an underdeveloped land use planning apparatus, a long-term scenario of high economic growth will increase built-up surfaces in the region by almost 24%, accompanied by a notable decrease in natural areas and cropland. Interestingly, in the case that the currently negative economic growth rates persist, artificial surfaces in the region are still expected to increase by approximately 7.5% by 2040

Ultra-high resolution sampling with UAVs for optimising fractional woody cover characterisations in dryland savannahs

Dryland savannahs are crucial for understanding carbon cycling and storage and for their provision of ecosystem services. Globally, the accurate mapping of the woody savannah component and its characteristics is especially important as it provides input to carbon emissions models. Moreover, in the southern African region, the encroachment of unpalatable woody species over large expanses of palatable grasses has received a lot of attention as it directly affects the livelihoods of local populations. Over these scales, Earth observation technologies are seen as the only viable means for mapping and monitoring the characteristics of woody vegetation. However, the commonly applied sampling and validation approach incorporating point woody samples identified over aerial photography or very-high resolution data (e.g. via Google Earth) is problematic as the satellite data used for the mapping, with a pixel size of 10 – 30 m, rarely consists of pure woody vegetation. To bridge this spatial gap between what is identified in the point-based samples and what is included in the 10-30m pixel, we employ a UAV-based 2D and 3D sampling strategy. We incorporate point samples collected from Google Earth in a 400km2 area of the Northwest Province of South Africa together with UAV-collected RGB and 3D mosaics, in order to optimise the mapping of fractional woody cover. We test the approach using both Landsat-8 and Sentinel-2 data in order to assess the applicability at both 10 and 30m scales. We also test the accuracy of two different machine learning classification approaches: random forests and support vector machines. Our 2D/3D UAV-based sampling approach provides higher fractional woody cover classification results than simply incorporating the ‘traditional’ point samples from aerial photography or Google Earth. Our suggested methodology can provide much needed assistance to fractional woody vegetation monitoring efforts in Southern African savannahs where the process is partly related with bush encroachment and land degradation

Multi-temporal land-cover classification and change analysis with conditional probability networks: The case of Lesvos Island (Greece)

This study uses a series of Landsat images to map the main land-cover types on the Mediterranean island of Lesvos, Greece. We compare a single-year maximum likelihood classification (MLC) with a multi-temporal maximum likelihood classification (MTMLC) approach, with time-series class labels modelled using a first-order hidden Markov model comprising continuous and discrete variables. A rigorous validation scheme shows statistically significant higher accuracy figures for the multi-temporal approach. Land-cover change accuracies were also greatly improved by the proposed methodology: from 46% to 70%. The results show that when only two dates are used, the mapping of land use/cover is unreliable and a large number of the changes identified are due to the individual-year commission and omission errors

Estimating elephant density using motion-sensitive cameras: challenges, opportunities, and parameters for consideration

With extinction rates far exceeding the natural background rate, reliable monitoring of wildlife populations has become crucial for adaptive management and conservation. Robust monitoring is often labor intensive with high economic costs, particularly in the case of those species that are subject to illegal poaching, such as elephants, which require frequent and accurate population estimates over large spatial scales. Dung counting methods are commonly employed to estimate the density of elephants; however, in the absence of a full survey calibration, these can be unreliable in heterogeneous habitats where dung decay rates may be highly variable. We explored whether motion-sensitive cameras offer a simple, lower cost, and reliable alternative for monitoring in challenging forest environments. We estimated the density of African savanna elephants (Loxodanta africana) in a montane forest using the random encounter model and assessed the importance of surveying parameters for future survey design. We deployed motion-sensitive cameras in 65 locations in the Aberdare Conservation Area in Kenya during June to August in 2015 to 2017, for a survey effort of 967 days, and a mean encounter rate of 0.09 ± 0.29 (SD) images/day. Elephants were captured in 16 locations. Density estimates varied between vegetation types, with estimates ranging from 6.27/km2 in shrub, 1.1/km2 in forest, 0.53/km2 in bamboo (Yushania alpine), and 0.44/km2 in the moorlands. The average speed of animal movement and the camera detection zone had the strongest linear associations with density estimates (R = −0.97). The random encounter model has the potential to offer an alternative, or complementary method within the active management framework for monitoring elephant populations in forests at a relatively low cost