Elias Symeonakis

Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions

Land degradation and desertification has been ranked as a major environmental and social issue for the coming decades. Thus, the observation and early detection of degradation is a primary objective for a number of scientific and policy organisations, with remote sensing methods being a candidate choice for the development of monitoring systems. This paper reviews the statistical and ecological frameworks of assessing land degradation and desertification using vegetation index data. The development of multi-temporal analysis as a desertification assessment technique is reviewed, with a focus on how current practice has been shaped by controversy and dispute within the literature. The statistical techniques commonly employed are examined from both a statistical as well as ecological point of view, and recommendations are made for future research directions. The scientific requirements for degradation and desertification monitoring systems identified here are: (I) the validation of methodologies in a robust and comparable manner; and (II) the detection of degradation at minor intensities and magnitudes. It is also established that the multi-temporal analysis of vegetation index data can provide a sophisticated measure of ecosystem health and variation, and that, over the last 30 years, considerable progress has been made in the respective research.

A comparison of rainfall estimation techniques for sub-Saharan Africa

Interpolated rain-gauge data were compared to Meteosat-based precipitation estimates for sub-Saharan Africa. Validation was carried out using a dataset from a very dense gauge network in South Africa, on a point-to-pixel (PO–PI) as well as on a pixel-to-pixel (PI–PI) basis. Error criteria computed at the gauged pixels indicate that overall the interpolated estimates perform similarly to the satellite-based data: they provide good estimates of lower but underestimate larger precipitation amounts. It is concluded that the satellite estimates are more fitted for the operational modelling of processes such as surface runoff and soil erosion, especially in the developing areas where resources are scarce.

10-Daily soil erosion modelling over sub-Saharan Africa

Soil erosion is considered to be one of the greatest environmental problems of sub-Saharan Africa. This paper investigates the advantages and disadvantages of modelling soil erosion at the continental scale and suggests an operational methodology for mapping and quantifying 10-daily water runoff and soil erosion over this scale using remote sensing data in a geographical information system framework. An attempt is made to compare the estimates of this study with general data on the severity of soil erosion over Africa and with measured rates of soil loss at different locations over the continent. The results show that the measured and estimated rates of erosion are in some areas very similar and in general within the same order of magnitude. The importance and the potential of using the soil erosion estimates with simple models and easily accessible free data for various continental-scale environmental applications are also demonstrated.

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.

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

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.

Incorporating Density in Spatiotemporal Land Use/Cover Change Patterns: The Case of Attica, Greece

This paper looks at the periodic land use/cover (LUC) changes that occurred in Attica, Greece from 1991 to 2016. During this period, land transformations were mostly related to the artificial LUC categories; therefore, the aim was to map LUC with a high thematic resolution aimed at these specific categories, according to their density and continuity. The classification was implemented using the Random Forests (RF) machine learning algorithm and the presented methodological framework involved a high degree of automation. The results revealed that the majority of the expansion of the built-up areas took place at the expense of agricultural land. Moreover, mapping and quantifying the LUC changes revealed three uneven phases of development, which reflect the socioeconomic circumstances of each period. The discontinuous low-density urban fabric started to increase rapidly around 2003, reaching 7% (from 2.5% in 1991), and this trend continued, reaching 12% in 2016. The continuous as well as the discontinuous dense urban fabric, almost doubled throughout the study period. Agricultural areas were dramatically reduced to almost half of what they were in 1991, while forests, scrubs, and other natural areas remained relatively stable, decreasing only by 3% in 25 years.

Optimisation of Savannah Land Cover Characterisation with Optical and SAR Data

Accurately mapping savannah land cover at the regional scale can provide useful input to policy decision making efforts regarding, for example, bush control or overgrazing, as well as to global carbon emissions models. Recent attempts have employed Earth observation data, either from optical or radar sensors, and most commonly from the dry season when the spectral difference between woody vegetation, crops and grasses is maximised. By far the most common practice has been the use of Landsat optical bands, but some studies have also used vegetation indices or SAR data. However, conflicting reports with regards to the effectiveness of the different approaches have emerged, leaving the respective land cover mapping community with unclear methodological pathways to follow. We address this issue by employing Landsat and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data to assess the accuracy of mapping the main savannah land cover types of woody vegetation, grassland, cropland and non-vegetated land. The study area is in southern Africa, covering approximately 44,000 km2. We test the performance of 15 different models comprised of combinations of optical and radar data from the dry and wet seasons. Our results show that a number of models perform well and very similarly. The highest overall accuracy is achieved by the model that incorporates both optical and synthetic-aperture radar (SAR) data from both dry and wet seasons with an overall accuracy of 91.1% (±1.7%): this is almost a 10% improvement from using only the dry season Landsat data (81.7 ± 2.3%). The SAR-only models were capable of mapping woody cover effectively, achieving similar or lower omission and commission errors than the optical models, but other classes were detected with lower accuracies. Our main conclusion is that the combination of metrics from different sensors and seasons improves results and should be the preferred methodological pathway for accurate savannah land cover mapping, especially now with the availability of Sentinel-1 and Sentinel-2 data. Our findings can provide much needed assistance to land cover monitoring efforts to savannahs in general, and in particular to southern African savannahs, where a number of land cover change processes have been related with the observed land degradation in the region.

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.

Detecting Vegetation Change in Response to Confining Elephants in Forests Using MODIS Time-Series and BFAST

Afromontane forests are biodiversity hotspots and provide essential ecosystem services. However, they are under pressure as a result of an expanding human population and the impact of climate change. In many instances electric fencing has become a necessary management strategy to protect forest integrity and reduce human-wildlife conflict. The impact of confining hitherto migratory elephant populations within forests remains unknown, and monitoring largely inaccessible areas is challenging. We explore the application of remote sensing to monitor the impact of confinement, employing the Breaks For Additive Season and Trend (BFAST) time-series decomposition method over a 15-year period on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) datasets for two Kenyan forests. Results indicated that BFAST was able to identify disturbances from anthropogenic, fire and elephant damage. Sequential monitoring enabled the detection of gradual changes in the forest canopy, with degradation and regeneration being observed in both sites. Annual rates of forest loss in both areas were significantly lower than reported in other studies on Afromontane forests, suggesting that installing fences has reduced land-use conversion from human-related disturbances. Negative changes in EVI were predominantly gradual degradation rather than large-scale, abrupt clearings of the forest. Results presented here demonstrate that BFAST can be used to monitor biotic and abiotic drivers of change in Afromontane forests.

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.