Land Degradation Processes Research Articles

Map-based soil crusting susceptibility assessment using pedotransfer Rules, CORINE and NDVI: A preliminary study in Greece

Map-based assessment of land degradation processes supports national monitoring and protection of the soil resources, in line with the Land Degradation Neutrality (LDN) and the Sustainable Development Goals (SDGs) initiatives. Soil crusting susceptibility, a complex expression of land degradation, with fundamental impact on soil functions, has not yet been estimated in Greek territory. To address this knowledge gap, current research uses well-known pedotransfer rules (PRLeB), partially adjusted to suit the local soil database format. The PRLeB, already applied at European scale, examine the soil crusting problem as a function of fundamental soil characteristics. From the first analysis a paradox emerged. Moderately and highly crusting prone soils were encountered in forested areas. This paradox indicated the need for a second formalization of the soil crusting problem (ePRLeB). The CORINE Land Cover (CLC) 2018 and a seasonal vegetation cover index (Normalized Difference Vegetation Index -NDVI) were used to divide the study area into categories and to capture the temporal variability of the problem, accordingly. Three land use categories emerged. The water bodies and the artificial areas were characterized as Non-evaluated areas (NE). Areas with considerable vegetation coverage (NDVI greater than 0.5) were characterized as Non-prone to crusting (NC), and the rest of the areas were characterized as Potentially prone to crusting (PC). The PRLeB approach was applied only to the PC areas. In both formalizations, the physicochemical and textural aspects of soil crusting adopted the same PRLeB. With the first formalization, 15% and 39% of the soils in Greece were characterized as very low and low susceptible to soil crusting respectively. With the second formalization 58 – 63% of the soils were characterized by no and 5 – 7% by very low crusting susceptibility. Current work explored two fundamentally different facets of the soil crusting problem. The first formalization, which can be expressed as the soil susceptibility to crusting, considers crusting as an inherent soil process. The second formalization, expressed as soil crusting susceptibility encounters crusting as a complex phenomenon involving natural but not anthropogenic factors. The study indicated that vegetation and seasonality are important factors in the map-based assessment of the observable soil crusting phenomenon. Also, it was highlighted that the unambiguous problem specification is a critical aspect for complex problem assessment and communication, particularly within an interdisciplinary context.

Applying RUSLE for soil erosion estimation in Romania under current and future climate scenarios

Soil erosion is one of the main land degradation processes, often enhanced by unsuitable human activities. In the context of global climate change, soil erosion also exhibits quantitative and qualitative changes induced by the temporally dynamic erosion factors, namely rainfall erosivity and land use. Our study attempts to forecast the evolution of soil erosion rates under climate change scenarios over the Romanian territory. Firstly, we apply the Revised Universal Soil Loss Equation (RUSLE) methodology to achieve a spatial model of soil erosion for the current climate conditions. Secondly, we use projections of rainfall erosivity to derive soil erosion models for two future climate change scenarios (Representative Concentration Pathway - RCP 4.5 and 8.5) and two periods of time (2041–2060, 2061–2080). Thirdly, we compute the differences between future projected and current erosion rates to identify spatial patterns of soil erosion evolution. The results show that, under the influence of rainfall erosivity dynamics, soil erosion tends to increase during the next decades. Overall, an enhancement of soil erosion rates on 84–90% of the country is expected to take place. Most of the erosion rates will increase slightly, with <1 t ha−1 yr−1, from an overall average of 2.94 t ha−1 yr−1, at present, to 3.67 (2061–2080, RCP 8.5) – 3.74 (2041–2060, RCP 8.5) t ha−1 yr−1 at country level. Locally, the erosion rates may increase with >5–10 t ha−1 yr−1 on areas estimated to cumulate >8000 km2. The most affected areas will be the soils situated in hilly and plateau regions, leading to an enhancement of their fertility degradation.

The current state of the Artemisia diffusa, Caroxylon orientale pastures on the Eastern chink of the Ustyurt plateau (Uzbekistan)

The article presents the current state of the Artemisia diffusa, Caroxylon orientale pasture difference on the Eastern chink of the Karakalpak Ustyurt. Due to climate change, the processes of land degradation and desertification affect the state of biodiversity of the Karakalpak part of Ustyurt. The role of Artemisia diffusa as a valuable forage plant, balancing the forage units of other compilers of pasture differences, is noted. The area of the sagebrush type of pastures, the nature of the soil cover, the percentage of projective coverage, landscape plant species, their placement, the presence of poisonous plants, the yield of feed mass, the list of plant species, as well as the recommended seasonality of the studied pasture difference are determined. At the same time, the pasture difference is recommended to be used as year-round pastures.

Diverse values of nature shape human connection to dryland landscapes in Spain

Understanding the nature's values and landscape preferences that determine the connection between people and nature may provide key insights to unravel some of the major sustainability challenges. However, knowledge of such factors, how they vary across landscapes, or whether they reflect more fundamental causes of (dis)connection from nature is still scarce. In this study, we focused on Spanish drylands, which are frequently undervalued by the local population and impacted by land degradation processes but provide important contributions to sustaining human well-being. We developed a methodological approach for understanding human-nature connectedness across ten representative landscapes of Spanish drylands. We carried out 191 face-to-face surveys to assess how people prefer different landscapes and identify which type of nature's value explain landscape preferences. The findings showed that specific landscapes were preferred differently by the public these preferences were supported by diverse values of nature. For instance, greenhouses were chosen as non-preferred landscape by locals, while protected littoral and desert were identified as the most preferred landscapes. Instrumental value was mainly assigned to greenhouses mostly based on the economic profits they provide, while other preferred landscapes such as traditional agricultural and high mountains were mostly related to relational value. Results show how the understanding of human-landscape connections can help to guide future conservation efforts as well as to foster conservation behavior.

A multi-data approach to evaluate progress towards land degradation neutrality in Central Asia

Central Asia hosts one of the largest continuous grassland areas on our planet, that is of vital importance for food security, biodiversity and carbon sequestration. However, this region is also subjected to some of the most intense land degradation processes, related to large scale land use change and climate change.To combat land degradation and pursue sustainable land management the concept of land degradation neutrality (LDN) has been proposed. LDN assessments are recommended to be based on global data in regions that lack sufficient regional data such as Central Asia. However, it remains unclear how the selection of datasets influences the estimated extent of land degradation. We followed the LDN framework and first calculated changes to three LDN indicators: land productivity, land cover, and Soil Organic Carbon (SOC). We then evaluated the impact of using different regional and global land cover data on the extent of land degradation in the region. Finally, we calculated a regionally specific integrated indicator on soil quality that we used to assess the “like-for-like” principle which enables counterbalancing losses and gains.Our results indicate that particularly the selection of land cover data has a significant impact on the overall assessment of degradation state. The extent of land between 2000 and 2019 varies considerably depending on the data and ranges between 15 and 34 % of the whole Central Asian region. Our findings reveal that the area of degraded land on high quality soils is twice as high as the area of high quality soils where the land condition has improved (12 % versus 6 %). In areas with low soil quality, 13 % was subject to degradation, and 10 % improved.Whilst the degradation extent varies according to the selected land cover datasets, our results demonstrates that Central Asia is undergoing land degradation affecting high quality soils. Sensitivity analysis of multiple datasets can reduce the risk of misjudgement in degradation extent assessments, especially in regions with mosaic ecosystems, such as grasslands.

Estimation of surface soil erosion using the RUSLE Model of Jalpaiguri Sadar Block in Jalpaiguri District, West Bengal, India

Land degradation is a pervasive environmental and “economic challenge of the present time, especially in developing countries. Soil erosion caused by water is considered as one of the major types of land degradation processes. Soil loss estimation and detection of soil erosion-prone areas are the most important for agricultural planning and various other land management plannings in the recent era. The amount of annual average soil loss was calculated by using the Revised Universal Soil Loss Equation (RUSLE) model. This model was popularized for identifying soil loss zone areas or zones and soil erosion risk areas. This study provided a creditable prediction of soil erosion and the probable soil erosion severity zones of Jalpaiguri Sadar Block in Jalpaiguri District, West Bengal. GIS Environment was used to create the raster layer in the RUSLE factors. RUSLE data layers including rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and conservation practice (P) factors were calculated to account for the average annual soil loss of the study area. The results of this work show that the range of soil erosion is &gt;100.00 tons hac-1year-1 to &lt;25.00 tons hac-1year-1 while the amount of soil erosion is more in the upper part of the Jalpaiguri Sadar block, mainly along the right bank of the Tista river and the bank of the Karala river, because the soil texture is very loose in nature and the area also receive heavy rainfall. Therefore, the high level of risk of soil erosion can be checked through the processes of various soil conservation techniques.

ISSUE ON LAND DEGRADATION IN KAZAKHSTAN

The scientific article is dedicated to the pressing issue of land degradation, which is observed and studied by the global scientific community, including Kazakhstani scientists. The classification and study of the state of degraded territories are of paramount importance as they are linked to further monitoring of the problem, determining the financial costs of implementing protective measures, and promoting sustainable development of these landscapes. It has been established that more than 90% of agricultural lands in Kazakhstan are subject to degradation processes, and this indicator varies depending on the type of land and their spatial distribution. When monitoring pastures, data from the Terra and Aqua satellites were used to determine the degradation of pastures. Based on the results of comparing the degree of damage to pastures obtained by remote methods and on the basis of ground surveys, it was found that the reliability of the correct determination of a weak degree of damage corresponds to 75%, an average degree of damage - 85% and a severe degree of damage - 90%. The authors of the article propose a classification of Kazakhstan's lands according to the Intergovernmental Panel on Climate Change and identify types of land degradation in Kazakhstan according to the «Wocat». The identified patterns of degradation processes in the territory of Kazakhstan are classified according to the Intergovernmental Panel on Climate Change levels. Directions for enhancing the sustainability of agricultural lands are determined based on classification criteria. The materials of this scientific research are recommended for use in land resource management and the development of agro-, phyto-, and agroforestry measures aimed at increasing the sustainability of desert agro-landscapes in Kazakhstan.

A novel method for detecting soil salinity using AVIRIS-NG imaging spectroscopy and ensemble machine learning

Soil salinization is one of the major land degradation processes spread over millions of hectares of global land. Hyperspectral Remote Sensing (HRS) coupled with modern data mining approaches help in real-time and cost-effective assessment or monitoring of salt-affected soils. This study aimed at predicting soil salinity across five sites in India using the Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) data in low to moderately salt-affected cropland soils. We have identified four unique spectral absorption features having sensitivity towards soil salinity through a hybrid feature selection algorithm. Soil electrical conductivity (EC) was estimated using different machine learning (ML) based models such as random forest (RF), gradient boosting machines (GBM), and deep learning (DL). An ensemble of RF and DL models showed the best performance with the coefficient of determination (R2) of 0.89 and 0.55 and normalized root-mean-squared error of 0.15 and 0.16 in training and test datasets, respectively. We also proposed a new hyperspectral soil salinity index using Shannon entropy-based aggregation of selected absorption features. The newly proposed index outperformed other majorly used remote sensing-based salinity indices. It also showed a strong correlation with measured EC (r = 0.68) and ML-predicted soil EC (r = 0.78), both being significant at 1% level of significance. The index was effective in classifying HRS images into six distinct salinity classes. We also assessed the feasibility of applying the proposed salinity index for future hyperspectral missions through the simulation of various spectral-spatial resampling scenarios and estimated the optimal spectral and spatial resolution for salinity prediction. The hyperspectral salinity index can be directly estimated from HRS data without the need for time-consuming and expensive field samplings and used as a proxy to evaluate soil salinity status under field conditions.

Prewinter soil water regime affects the post-winter cracking position on gully sidewall and slumping soil dynamics in Northeast China

Gully erosion is one of the most destructive land degradation processes. Soil slumping along cracks on a gully sidewall is a crucial process of gully development, yet the factors controlling the dynamics and amount of slumped soil remain unclear. It was hypothesized that soil slumping along the cracks in summer might be affected by the prewinter soil water regime, as it determines the depth of freezing-induced fissures within the soil profile, which affects the depth and dynamics of the cracks on the gully sidewall. To test this hypothesis, the soil water regime was manipulated by infiltrating 250 mm of water into the plots placed with one edge 10, 30 and 50 cm away from a gully sidewall before winter. This plot layout eventually resulted in four treatments with different amounts of infiltrated water. The isolines of near-saturation soil water contents appeared closer to the ground and the sidewall under the higher infiltration treatments before and after winter. Soil freezing started from the topsoil on 14 Nov. 2017 and penetrated the 1-m soil profile within 24 days. Soil thawing started from both the topsoil and subsoils (1 m depth) on 13 Apr. 2018 and ended on 24 Apr. 2018. The cracks that appeared on the sidewalls in Apr. had similar lengths and widths at the depths with a near-saturation soil water content (between 25 and 45 g kg−1). The cracks appeared at deeper position on gully sidewall under lower infiltration treatments. The soil mass slumped from the sidewall was greater in Apr. and less from Jul. to Aug. under higher infiltration treatments. The annual total of slumped soil mass was the lowest in the low infiltration treatment, where the near-saturation isolines were the deepest. These findings suggested that the prewinter soil water regime along the slope controlled the depth of freezing within the soil profile. The freezing depth determined the depth and time of soil cracking and slumping on the gully sidewalls the following year. Our study implies that gully development through soil slumping depends on pre-winter slope hydrology, which should be considered in modeling and controlling gully development.

Evaluating land degradation by gully erosion through soil erosion indices and rainfall thresholds

Among the land degradation processes, gully erosion is the one that poses more environmental and societal challenges in arid regions. Predicting spatiotemporal gully development in a region under changing conditions is important to adopt proper mitigation measures. Here we investigate the Ghapan-Olya watershed in Golestan province in Iran, which is affected by many erosional landforms, including gully phenomena. We apply the pixel-based distributed LANDPLANER model to predict where rainfall induced gullies will occur by exploiting input maps including UAV data, the region soil, and seasonal land use information. We compare our topographic thresholds and an erosion index with field observations through the application of quantitative metrics such as sensitivity, specificity, fallout, precision, and recall. Our study reveals that the spatial density of the gully’s location is more frequently predicted in the areas with an altitude of about 200–300 m, steep slope (between 15 and 30 degrees), and low average accumulation value (< 100) in the southeast facing slope. We obtain higher values of erosion index and topographic threshold for the minimum curve number where intense rainfalls are more frequent and where land use and cover conditions are more predisposing for gully occurrence. We obtain the largest values of soil erosion indices in the fall scenario when the daily rainfall is 80 mm (6.27), followed by the summer scenario with 80 mm daily rainfall (4.88), and spring again with 80 mm daily rainfall (2.99). In addition, topographic threshold maps illustrate the largest amount of soil erosion for the curve number scenario (without considering daily rainfall) in autumn. Our approach allows simulating gully erosion under changing conditions.

Land cover degradation in the reference and monitoring periods of the SDG Land Degradation Neutrality Indicator for Switzerland

Land degradation impacts most terrestrial biomes across all world regions. To address global change challenges emanating from degrading natural resources, many countries voluntarily set themselves the goal of achieving Land Degradation Neutrality (LDN). Baseline conditions are to be established over a reference period (2000 – 2015). With seven more years to monitor this Sustainable Development Goal 15 indicator — SDG15.3.1 until the year 2030, the scientific basis for operationalizing LDN is still evolving. The non-availability of annual land use-land cover maps of sufficient resolution in various countries, among them Switzerland, is a major factor hampering the assessment at national and local levels. Land cover change is assessed for seven land categories (that is, Tree-covered area, Grassland, Cropland, Wetland, Artificial Surfaces, Otherland, and Waterbody). Land cover change is a major LDN sub-indicator required to assess the proportion of degraded land to total land area (SDG15.3.1). Annual land cover datasets from 2015 to 2020 were produced at 10 m from Sentinel-1 and Sentinel-2 images using a Remote Sensing and Geographic Information System-based workflow. An evaluation of degradation in land cover is presented in support of the operationalization of LDN in Switzerland. Drawing upon the understanding that changes made to land use-land cover may act as precursors to land degradation processes, transitions relating to the loss of natural cover were identified based on the land cover change criteria during the reference and the monitored periods. The criteria were developed for relating land cover transitions to degrading and non-degrading processes. Such transitions were grasslands to tree-covered areas and croplands to artificial surfaces due to settlement development. For example, the amount of cropland converted to artificial surface areas was greater in absolute terms during the monitored period than during the reference period. Also, the regeneration of natural cover involving transitions from otherland to grassland and from grasslands to tree-covered areas was found. Overall trends between 2000 and 2020 in Switzerland are the increasing settlement areas in the Central Plateau, the Alpine valleys of Valais and Ticino, largely at the expense of croplands and the bush encroachment of pastures such as in the Jura. This study’s contributions include an enhanced workflow for annual land cover mapping for the entire Switzerland and the adaptation of the land cover change criteria to fit the Swiss context. The proposed mapping method has the potential to fill the gap between the production cycles of the Swiss Corine and Arealstatistik land use data.

Modeling of Gully Erosion in Ethiopia as Influenced by Changes in Rainfall and Land Use Management Practices

Gully erosion is one of the most extreme land degradation processes that exhibit spatial and temporal variation depending on topography, soil, climate, and land use and management characteristics. This study investigated the impact of changes in rainfall, land use/land cover (LULC), and land use management (LUM) practices on gully erosion in two midland watersheds (treated Kecha and untreated Laguna) in the Upper Blue Nile basin of Ethiopia by using the LANDPLANER model in combination with intensive field measurements and remote sensing products. We simulated gully erosion under past (in 2005), present (in 2021), and three potential future curve number conditions, each time under four rainfall scenarios (10, 30, 60, and 100 mm) using the dynamic erosion index (e), static topographic (esp), and erosion channel (esp_channel) thresholds. Density plot analyses showed that gullies frequently occur in low-lying gentle slope areas with relatively higher curve number values. The best predictions of gullies identified through true positive rates (TPR) and true negative rates (TNR) were achieved considering the static esp_channel &gt; 1 for Kecha (TPR = 0.667 and TNR = 0.544) and the dynamic e &gt; 0.1 for 60 mm of rainfall in Laguna (TPR = 0.769 and TNR = 0.516). Despite the 10 mm rainfall having negligible erosion-triggering potential in both watersheds, the 60 and 100 mm rainfall scenarios were 4–5 and 10–17 times, respectively, higher than the 30 mm rainfall scenario. While the LULC change in the untreated Laguna watershed increased the impact of rainfall on gully initiation by only 0–2% between 2005 and 2021, the combination of LULC and LUM significantly reduced the impact of rainfall in the treated Kecha watershed by 64–79%. Similarly, the gully initiation area in Kecha was reduced by 28% (from 33% in 2005 to 5% in 2021) due to changes in LULC and LUM practices, whereas Laguna showed little increment by only 1% (from 42% in 2005 to 43% in 2021) due to LULC change. In addition, the future predicted alternative land use planning options showed that gully initiation areas in Laguna could be reduced by 1% with only LULC conversion; 39% when only LUM practices were implemented; and 37% when both were combined. These results indicate that LUM practices outweigh the impact of LULC on gully erosion in the studied paired watersheds. Overall, LANDPLANER successfully simulated the spatio-temporal variation of gully erosion with scenario-based analyses and hence can be used to predict gullies in the study area and other data-scarce regions with similar agro-ecological settings.

Soil acidification induced variation of nitrifiers and denitrifiers modulates N2O emissions in paddy fields

Soil acidification is a major land degradation process globally, and impacts soil nitrogen (N) transformation. However, it is still not well known how soil acidification affects net N mineralization and nitrification, especially N-cycling microbes and nitrous oxide (N2O) emissions. Hence, three soils characterized by different soil pH values (5.5, 6.3, and 7.7) were collected from the paddy fields, and experiments were conducted to evaluate the effect of soil acidification on net N mineralization and nitrification, and N2O emissions. Compared to those in the soils with pH 7.7 and 6.3, net N mineralization, net nitrification, and N2O emissions were decreased by 75–76 %, 89–91 %, and 19–48 %, respectively, in the soil with pH 5.5, while net N nitrification and N2O emissions decreased by 18 % in the soil with pH 6.3 when compared to those in the soil with pH 7.7. The significantly decreased net nitrification in the soils with pH 6.3 and 5.5 was mainly attributed to the limited N availability and abundance of nitrification-related microbes including ammonia-oxidizing bacteria and complete ammonia-oxidizers. The decrease in N2O emissions of soils with pH 6.3 and 5.5 had mainly resulted from decreasing nitrification and denitrification via suppressing microbes including nirS and fungal nirK and limiting N availability. Hence, this study provides new insights and improves our understanding of how soil acidification regulates N mineralization, nitrification, and N2O emissions in paddy soils, which gives guidance on developing N management strategies for sustainable production and N2O mitigation in acid soils.

Gullies and badlands of India: Genesis, geomorphology and land management

AbstractGully erosion is a formidable land degradation process globally. It is omnipresent across India, wherein two of the largest badlands regions in the world also exist. However, despite being affected by widespread gullying, gully erosion research in India has been rather limited, with key aspects of gully formation, morphologies and dynamics remaining unknown. Through this comprehensive systematic review, we synthesise previous studies on gully erosion in India and in turn highlight pivotal knowledge gaps. The review starts with a discussion of the causal factors of gullying in India, which underlines how gully initiation in several regions was an aftermath of deforestation and overgrazing. Contrastingly, the badlands of Central and Western India have mainly developed in response to natural triggers like neotectonics and Holocene climate change. The section on mapping highlights how mapping methodologies have not only been dictated by the availability of imagery and/or means of data processing, but also the mapping purpose—that is, individual gully channels or entire badlands. Although a few studies applying concepts of fractal geometry to characterise badlands’ geomorphology are innovative and unique, the most striking research gaps we have identified also pertain to understanding and quantification of gully geomorphology and erosion dynamics in different regions of India. Our review reveals that gullies of peninsular India have been the least studied, followed by those of the Himalayan and Sub‐Himalayan region. Although the literature provides interesting examples of linkages between badlands development and the wider geomorphic evolution of the landscape, better chronological understanding is required to disentangle the relative importance of natural and anthropogenic drivers of landscape change in India's badlands. Large‐scale mapping of gully characteristics, quantification of gully morphologies, gully erosion rates and its share in catchment sediment budget across various physiographic regions or river basins of the country also constitute important areas for future research.

Effects of contour farming and tillage practices on soil erosion processes in a hummocky watershed. A model-based case study highlighting the role of tramline tracks

To reduce water erosion, one of the most severe land degradation processes on arable soils, conservation measures such as contour farming or reduced tillage practices can be applied. The general effectiveness of these measures on reducing soil erosion is well understood, however, little is known about the influence on tramline tracks, which have considerable impact on the transport of sediment and runoff. This study assesses the impact of reduced and conventional tillage and slope-related tramline directions on runoff and soil erosion rates at catchment level. For this purpose, a published and freely available dataset was used consisting of soil loss and runoff data modelled for a single soil erosion event considering courses and structural properties of tramlines (cf. Saggau et al., 2022). The orientation of tramlines against the slope direction was defined as contouring efficiency (Cef) and calculated by an approach that relates slope and tramline directions. Finally, results of the statistical analyses are compared with mapped soil erosion data. Results show that i) high-resolution derivation of Cef, based on tramlines, shows reasonable results in capturing variations in slope curvatures within the fields, ii) increased Cefs can significantly reduce runoff and soil erosion in tramlines and, iii) reduced tillage indicates significantly lower runoff and soil erosion rates in tramlines and cultivated areas. The results reveal that tramlines have strong interrelations with surface runoff, water connectivity and soil erosion. The consideration of tramlines in process-based models should be employed in other model approaches as it improves assessments of soil erosion risk and efficiencies of conservation measures, which are necessary in taking appropriate actions in support of soil and water conservation.

Monitoring of Land Degradation in Greece and Tunisia Using Trends.Earth with a Focus on Cereal Croplands

Land degradation (LD) processes are widespread in drylands worldwide and are accelerated by climate change. As a result, food security and livelihoods are at risk. Thus, there is a need to monitor LD trends, especially in agricultural areas. Mediterranean countries, including Tunisia and Greece, are concerned due to the presence of drivers and pressures causing land degradation. Through the Trends.Earth plugin, the SDG 15.3.1 indicator can be implemented to map LD status. In this study, we mapped LD in Greece and Tunisia for the recommended baseline period of 2001–2015 and the selected reporting period of 2016–2020. The land productivity was assessed within Trends.Earth using the MODIS MOD13Q1 product, while the default datasets were used for the other sub-indicators. The main findings are: (i) the percentage of degraded land decreased from the baseline to the reporting period from 4.83% to 2.62% of total area in Greece and 9.97% to 6.26% in Tunisia—degradation rates that differ from those reported to the UNCCD (United Nations Convention to Combat Desertification) by the respective national authorities; (ii) the dominant land condition in Greece was improved, while in Tunisia, it was stable; (iii) land productivity presented a similar trend through the SDG 15.3.1 indicator over both countries, including the net land productivity dynamics over croplands; (iv) based on analysis using plant functional types performed with MODIS MCD12Q1, the highest portion of degraded land in Greece was located in grasslands and in Tunisia in cereal croplands (after desert areas); and (v) with a focus on LD over cereal croplands, the portion of degraded areas appeared to decrease in both Greece and Tunisia. The percentage was higher in Tunisia, representing 16.52% of the total degraded land during the reporting period compared to 10.83% in Greece. All the above stress the need to foster the adoption of sustainable land management practices, especially in Tunisia, and speed up the implementation of measures to achieve LD neutrality.

Temporal remote sensing based soil salinity mapping in Indo-Gangetic plain employing machine-learning techniques

Soil salinization is one of the most active land degradation processes, affecting predominantly arid, semi-arid, and dry sub-humid regions and leading to decreased agricultural yields. The Indo-Gangetic plain, which includes the irrigated command areas with arid and semi-arid climatic conditions are severely affected by secondary soil salinization. Assessing the spatial and temporal extent as well as the severity of salinization is an important step for adoption of proper reclamation measures to boost agricultural productivity in the salt affected areas. The study was conducted with this background to evaluate the extent and severity of soil salinization in alluvial plains of Mathura district of Uttar Pradesh, India. In this study, the satellite data of 6 months from January 2019 to June 2019 were pre-processed and various spectral indices were generated in Google Earth Engine. Remote sensing techniques provides an ideal platform for addressing this problem at larger scales and thus we employed Sentinel-2 derived vegetation and salinity spectral indices for distinguishing temporal change in severity of soil salinization and map the salinity as a function of these indices for the entire study area. The time series salinity analysis showed that among the various spectral indices Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Soil Index (NDSI) had a clear differentiation between slight, moderate and severe salinity class in the first three months of the study period and the Salinity Index II (SI_II) could differentiate for the first four months. Further, two machine learning algorithms namely Random Forest (RF) and Support Vector Machine (SVM), were used to create soil salinity prediction models making use of the soil Electrical Conductivity (EC) values of 115 ground-sampling sites as the predictand variable and the optimal spectral indices as the predictor variables. Further, we evaluated the prediction ability of different models using 12 and 24 variables combination using R2 and RMSE values. The prediction accuracy of the RF model was found to be slightly higher than that of the SVM model, and the spatial distribution pattern of soil salinity predicted by the two models were comparable. We concluded that spectral indices combined with machine learning techniques have the potential for low cost reliable spatial and temporal soil salinity distribution mapping for planning and implementation of salinity reclamation measures.

LANDSCAPE DYNAMICS IN THE SOMEȘ CORRIDOR. CASE STUDY: GURUSLĂU DEPRESSION (ROMANIA)

Changes in land use patterns induced by different agricultural practices are reflected territorially through transformations at the level of elementary landscape units, with an impact on territorial identity and cohesion. The aim of this study is to highlight the dynamics of the territorial structures in the post-communist period (1990-2018), diachronically reflected in the transformations of the landscape of the Guruslău Depression, using the landscape metrics. The main direction of the scientific research was based on the analysis of land use changes and the identification of the spatial elements of structural-landscape distinction with impact on land degradation process. The evaluation of the landscape dynamics in the current context uses several effective metrics and tools, which increasingly require the identification of interdisciplinary methods of analysis, with a decisive impact on territorial development. Besides, the present approach is also motivated by the increasing environmental impact of climate change. The methodology used in the present paper is based both on the geoprocessing of vector data using GIS tools and correlated spatial analysis, and on the identification of landscape types using a new process of reclassifying land use categories, according to a set of landscape definition variables. The results of the research highlighted both the particularities of landscape transformations that occurred in the reference interval, as well as the favourable conditions for addressing biocultural diversity, by identifying traditional agricultural practices and the resilience of geographical landscapes given the adaptation to changing development strategies. Meanwhile, by detecting the landscape structures affected by change, in correlation with the impact induced on the biodiversity of the territory, the present study has a wide applicability in the most appropriate implementation of local development policies, as well as in identifying the forms of sustainable valorisation of the landscape in the study area.

A comprehensive method for refining essential SDGs variables for land degradation monitoring based on the DPSIR framework

ABSTRACT The United Nations adopted 17 Sustainable Development Goals (SDGs) to address societal, economic and environmental sustainability issues. The efficiency of SDGs monitoring could be improved by essential variables (EVs), which can help to better deal with massive data, interdisciplinary knowledge and workloads. However, in practice, effectively combining EVs with SDGs monitoring remains challenging. In this paper, we proposed a refining method of essential SDGs variables (ESDGVs) to land degradation. Firstly, we selected northwest China as our experimental region and extracted a group of variables related to land degradation from SDG indicators based on the DPSIR framework. Next, we identify the essential ones using a combined qualitative and quantitative methods with the criteria of feasibility, spatialization, and relevance which considered the issues of data acquisition, monitoring scale, and closeness to the land degradation. Finally, we analysed the monitoring role of ESDGVs. Results show that, compared to conventional observations, ESDGVs facilitate the monitoring and evaluation of regional SDGs with reduced efforts. And both climate and human activities have a facilitating or inhibiting effect on land degradation processes. In the future, we hope to have more mature data sets and consider adding more SDG indicators for ESDGVs’ refinement.

Semi-automatic extraction of land degradation processes using multi sensor data by applying object based classification technique

Semi-automatic extraction of land degradation processes using multi sensor data by applying object based classification technique