Mapping land degradation in the Massili River Basin, Burkina Faso: a spatio-temporal analysis of contributing factors

Increasing environmental and socioeconomic transformations in African drylands are driving land degradation. Using the Composite Land Degradation Index, this study assessed physical, chemical and biological degradation by determining their extent and severity. Palapye, an agro-pastoral region in eastern Botswana was used as a case study. Land degradation maps (status and indicators) were created with data from the field, soil chemical properties and image interpretation. Areas in the vicinity of settlements with Luvisols at elevations between 773 and 893 m were most degraded, implying impacts from human activities. This study developed a comprehensive list of of land degradation indicators for Botswana and created additional symbols for mapping indicators. Creation of these reference data for 2015 will facilitate the monitoring of land degradation in Palapye. The integrative and spatially explicit procedure utilized in this study can be adapted for assessing and validating local-level land degradation baseline and estimates towards operationalizing Land Degradation Neutrality in all countries.

Land degradation is intrinsically complex and involves decisions by many agencies and individuals, land degradation map- ping should be used as a learning tool through which managers, experts and stakeholders can re-examine their views within a wider semantic context. In this paper, we introduce an analytical framework for mapping land degradation, developed by World Overview for Conservation Approaches and technologies (WOCAT) programs, which aims to develop some thematic maps that serve as an useful tool and including effective information on land degradation and conservation status. Consequently, this methodology would provide an important background for decision-making in order to launch rehabilitation/remediation actions in high-priority intervention areas. As land degradation mapping is a problem-solving task that aims to provide clear information, this study entails the implementation of WOCAT mapping tool, which integrate a set of indicators to appraise the severity of land degradation across a representative watershed. So this work focuses on the use of the most relevant indicators for measuring impacts of different degradation processes in El Mkhachbiya catchment, situated in Northwest of Tunisia and those actions taken to deal with them based on the analysis of operating modes and issues of degradation in different land use systems. This study aims to provide a database for surveillance and monitoring of land degradation, in order to support stakeholders in making appropriate choices and judge guidelines and possible suitable recommendations to remedy the situation in order to promote sustainable development. The approach is illustrated through a case study of an urban watershed in Northwest of Tunisia. Results showed that the main land degradation drivers in the study area were related to natural processes, which were exacerbated by human activities. So the output of this analytical framework enabled a better communication of land degradation issues and concerns in a way relevant for policymakers.

This paper examines a feature-level fusion framework for detecting and mapping land degradation (LD) and enabling sustainable land management (SLM) in semi-arid areas using optical sensors and Synthetic Aperture Radar (SAR) satellite data. The objectives of this review were to (i) determine the trends and geographical location of land degradation mapping publications, (ii) to identify and report current challenges pertaining to mapping LD using multiscale remote sensing data, (iii) to recommend a way forward for monitoring LD using multiscale remote sensing data. The study reviewed 78 peer-reviewed research articles published over the past 24 years (1998–2022). Image fusion has the potential to be more useful in various remote sensing applications than individual sensor image data, making it more informative and valuable in the interpretation process. In addition, this review discusses the importance of SAR and optical image fusion, pixel-level techniques, applications, and major classes of quality metrics for objectively assessing fusion performance. The literature review alluded that the SAR and optical image fusion in the detection and mapping of land degradation and enabling sustainable land management has not been fully explored. Advanced techniques such as the fusion of SAR and optical satellite imageries need to be incorporated for the detection and mapping of LD, as well as the promotion of SLM in halting LD in South African drylands and around the world. We conclude that there is scope for further research on the fusion of SAR and optical images, as new micro-wave and optical sensors with higher resolution are introduced on a regular basis. The results of this review contribute to a better understanding of the applications of SAR and optical image fusion in future research in the severely degraded drylands of southern Africa. KEY RESEARCH GAPS The fusion of SAR and optical data still remains an open challenge. The future of different remote sensing applications lies in this kind of fusion. Land degradation is one of the greatest challenges amongst the environmental problems in South Africa, causing a reduction in the capacity of the land to perform ecosystem functions and services that support society and development. Yet, in South Africa, there are no studies that have widely investigated the potential for a fusion of SAR and optical data to detect and map land degradation and SLM practices. This paper established a baseline for understanding the application of a fusion of SAR and optical data as rapid tools for mapping, monitoring, and evaluating LD, as well as the impacts of SLM practices in South Africa’s degraded drylands.

Land degradation is a serious environmental stress, which is identified and analysed through advanced remote sensing technique at a global level. Vegetal degradation, soil erosion, salinity and alkalinity, deforestation, changes in land cover are a few factors, which raise the severity of desertification and land degradation. The changes caused by these factors lower the land and food production and eventually leads to environmental and socio-economic sustainability. Periodical monitoring and observation of the factors, which are the root causes for desertification/land degradation can be evaluated through remote sensing and modelling techniques. The present chapter aims to review the various remote sensing sensors and the recent techniques in mapping desertification and land degradation processes. Remote sensing and GIS techniques serve as an aid to assess and monitor various land degradation processes, and to compare trends across spatial and temporal scales. In the future, the convergence of high-resolution data products with modern classification and modelling techniques could be explored more broadly to assess and obtain more detailed information on monitoring and modelling desertification and land degradation.KeywordsDesertificationLand degradationSensorsRemote sensingRecent techniques

Connotative land degradation mapping: A knowledge-based approach to land degradation assessment

The purpose of this study is to compare the role of spectral and spatial resolutions in mapping land degradation from space‐borne imagery using Landsat ETM+ and ASTER data as examples. Land degradation in the form of salinization and waterlogging in Tongyu County, western Jilin Province of northeast China was mapped from an ETM+ image of 22 June 2002 and an ASTER image recorded on 24 June 2001 using supervised classification, together with several other land covers. It was found that the mapping accuracy was achieved at 56.8% and higher for moderately degraded (e.g. salinized) farmland, and over 80% for severely degraded land (e.g. barren) from both ASTER and ETM+ data. The spatial resolution of the ASTER data exerts only a negligible effect on the mapping accuracy. The 30 m ETM+ outperforms the ASTER image of both 15 m and 30 m resolution in consistently generating a higher overall accuracy as well as a higher user's accuracy for barren land. The inferiority of ASTER data is attributed to the highly repetitive spectral content of its six shortwave infrared bands. It is concluded that the spectral resolution of an image is not as important as the information content of individual bands in accurately mapping land covers automatically.

Land degradation takes place in a number of ways, such as erosion by water and wind, acidification and salinization, and makes land unfit for humans as well as for soil environments. In most parts of the globe, land degradation has become a major issue. To mitigate this issue the need is to comprehend its causes, effects and level of seriousness. Researchers all over the world have been analysing this issue and have created evaluation and checking strategies. Many varied techniques, such as experts' views, field estimations, subject perceptions, land users' critiques, profitability adjustments, remote sensing detection and modelling approaches, are some of the ways to deal with land degradation evaluations at various levels. It has also been seen that in the past there has been an absence of adequate and coordinated tracking and evaluation strategies, which are recognized as a significant controlling component for resisting desertification. Remote sensing gives a superior comprehension of land degradation phenomena and the variables that drive them. A literature review of the past couple of decades shows that huge sets of research papers were published which depicts the commitment of remote sensing techniques in mapping land deterioration and degradation. This chapter presents the application of remote sensing techniques in land degradation assessment, soil mapping, soil moisture studies, soil fertility, soil resource studies and related subthemes.

The Indian subcontinent, comprising of seven countries, is heavily populated and has a large area under desertification and land degradation. The extent and severity of land degradation vary across this vast land area. The terms, desert, desertification, land degradation and degraded lands/wastelands have been elaborated. The causes, both proximal and underlying as well as the consequences of desertification are discussed. Indicators for mapping, monitoring and assessment of desertification and land degradation has been discussed. Methodology for mapping and monitoring of desertification and land degradation in the Indian sub continent, using satellite data, has been presented.

Abstract. Land degradation is a global barrier to ecological, economic and sustainable developments. Climate change, natural disasters, human activities may result changes in soil organic carbon content, land productivity and land use/cover. Climate change is accelerating and expanding these degraded areas. If land destruction is not minimized, cause increasing population, inappropriate land use, climate change and rapid depletion of natural resources etc. in the coming years. It is estimated that land degradation and desertification will be the most important environmental problems. Mapping of land degradation using remote sensing techniques; determining sensitive areas for land degradation and taking protection measures; sustainable management of natural resources, ensuring sustainable agricultural production, etc. are the key factors. This study was conducted in the Seyhan basin, which is suffer from soil loss processes, changes in land cover and land use. These indicators are; trends in land productivity dynamics, land cover change and change of soil organic carbon stocks. The data set utilized to reveal the land degradation was including; 1 km resolution Land Productivity from JRC GLOBAL (1999–2013) and 250 m resolution NDVI from MOD13Q1 (2000–2015), Land Cover ESA CCI's with 300 m resolution LC (2000–2015), SOC stock from LUCAS (JRC) with 250 m resolution, 2000–2018 data from CORINE. The land degradation of the Seyhan basin was mapped using the specified land degradation indicators together with the One Out All Out (1OAO) rule.

The information on the extent and spatial distribution of various kinds of degraded lands is essential for strategic planning and development of degraded lands. Processes of land degradation can be broadly grouped into physical, chemical, and vegetal (biological) degradation. The physical processes include land degradation mainly due to water and wind erosion, compaction, crusting, and waterlogging. The chemical process includes salinization, alkalization, acidification, pollution, and nutrient depletion. The vegetal or biological processes on the other hand are reduction of organic matter content in the soils and degradation of vegetation. The use of remote sensing and geographic information system (GIS) techniques makes land degradation estimation and its spatial distribution feasible with reasonable costs and better accuracy in larger areas. The use of spaceborne multispectral data shown its potential in deriving information on the nature, extent, spatial distribution, and magnitude of various kinds of degraded lands. Assessment and monitoring of land degradation through remote sensing offer a series of advantages such as consistency of data, fairly near real-time reporting, and a source for having spatially explicit data. The integration of high-resolution remote sensing data and digital elevation models derived from satellites data like Cartosat-1 and Cartosat-2 and Light Detection and Ranging (LiDAR) with ground data has immense potential in assessment and monitoring of land degradation in local scales. In this chapter, application of remote sensing and GIS in assessment and mapping of physical, chemical, and vegetal degradation has been discussed. The study indicates that integrated remote sensing and GIS applications have immense potential in assessment, mapping and monitoring of land degradation with reasonable cost and better accuracy in larger areas that would otherwise require large inputs of human and material resources.

Land degradation, or desertification, is specific to arid, semi-arid, and dry sub-humid regions. The rate of thisphenomenon is high in developing countries such as Iran. This research investigated desertification and mapping ofdesertification in Abuzaydabad, near Kashan, Iran, with an emphasis on land criteria using an IMDPA model.Different studies have assessed land degradation or desertification and resulted in the production of different regionalmodels. The application of such models to another region requires reinvestigation of the criteria and adjustments forlocal conditions. The present study used the newest and best model for assessment. Three key regional criteria weredefined for desertification: geology-geomorphology, soil, and wind erosion. A working unit map was made using ageomorphologic method and land use in each working unit was determined. Thematic databases were integrated andenhanced using GIS and its spatial modeling function. Using the developed land degradation or desertificationmapping, it was found that of the total study area (16161 ha), medium desertification was found in 4792 ha and highdesertification was found in 11369 ha.

Land degradation monitoring is of vital importance to provide scientific information for promoting sustainable land utilization. This paper presents an expert knowledge and BP-ANN-based approach to detect and monitor land degradation in an effort to overcome the deficiencies of image classification and vegetation index-based approaches. The proposed approach consists of three generic steps: (1) extraction of knowledge on the relationship between land degradation degree and predisposing factors, which are NDVI and albedo, from domain experts; (2) establishment of a land degradation detecting model based on the BP-ANN algorithm; and (3) land degradation dynamic analysis. A comprehensive analysis was conducted on the development of land degradation in the Ordos Plateau of China in 1990, 2000 and 2010. The results indicate that the proposed approach is reliable for monitoring land degradation, with an overall accuracy of 91.2%. From 1990–2010, a reverse trend of land degradation is observed in Ordos Plateau. Regions with relatively high land degradation dynamic were mostly located in the northeast of Ordos Plateau. Additionally, most of the regions have transferred from a hot spot of land degradation to a less changed area. It is suggested that land utilization optimization plays a key role for effective land degradation control. However, it should be highlighted that the goals of such strategies should aim at the main negative factors causing land degradation, and the land use type and its quantity must meet the demand of population and be reconciled with natural conditions. Results from this case study suggest that the expert knowledge and BP-ANN-based approach is effective in mapping land degradation.

Remote sensing (RS) techniques based on multispectral satellite-acquired data have demonstrated an unequalled potential to detect, quantify, monitor, and map land degradation. However, RS data alone do not provide information on how land degradation affects the socio-political aspects and the economy of the population living in the affected regions. We developed the Continuous Cycle of Land Degradation (CCoLD) to quantify the severity of the land degradation in the Upper East Region (UER) of Ghana and combined it with the RS-based Normalized Difference Vegetation Index (NDVI) using Global Inventory Modeling and Mapping Studies (GIMMS) NDVI, ground data, and food-production data. In addition, we carried out a field study in the UER, a semi-arid transitional region that plays an important food-production role in Ghana, and compared the results with multi-temporal RS imagery. As well as the general ground measurements, the field study included questionnaires asking local residents to assess the impact of land degradation on their quality of life. The RS data show widespread localized degradation; the field study, supported by crop-production data, also suggests overall extensive land degradation. However, field evidence suggests ecological succession where locally adapted horsetail grasses were displaced by environmentally efficient, short-lived, quick-maturing, and dense grasses. A convergence of evidence suggests that land degradation is in the advanced stage and that more focused, community-based efforts would be needed to combat land degradation and restore the ecosystem's integrity.

The ongoing desertification and aeolian erosion processes in the southern Gobi Desert are ranked as one of the most significant global environmental disasters. In this study, we analyzed the decadal progress of eolian erosion in the southern Gobi Desert and traced key factors controlling intensified land degradation (LD) and sand and dust (SD) generation employing satellite data and climatic variables. Columnar dust mass density from climatic data re‐analyses as a major SD tracer was combined with the Mann–Kendall (MK) method and the empirical orthogonal function processor. Validation was performed by using ground data sets and field evidence from reference locations. The results revealed that (1) LD/SD patterns and hotspots in the Gobi Desert are significantly controlled by the distribution and trend of precipitation; (2) climatic conditions in the Mongolian Gobi Desert have shifted towards an unfavourable direction with respect to the LD/SD occurrence; (3) surface conditions in southeastern Mongolia have somehow decoupled from the weather factors and the transition zone between the desert and the vegetated terrain has gradually expanded probably due to anthropogenic activities. The correlation analyses between all candidate driving factors of LD/SD indicated that a major control mechanism of spatiotemporal migration of LD/SD in the southern Gobi Desert is the change in precipitation, whereas anthropogenic activity holds a secondary control. The results obtained can be used to prioritize intervention zones in the frame of land use planning processes aimed at adapting to climate change and mitigating LD and SD generation in source areas.

Land degradation in Tongyu County, Northeast China was mapped from the visible, near infrared, and shortwave infrared bands of ASTER data using the per pixel-based maximum likelihood and object-based image classification methods, comparatively. In both methods the land covers were mapped into nine categories, three of which were related to land degradation. It is found that the ASTER image of 15 m spatial resolution allowed the mapping to be achieved at an overall accuracy of 70.6% using the pixel-based method. The accuracy for degraded land was slightly higher at 73.3%. If mapped from the same image segmented at 10 pixels (150 m) using the object-oriented method, the overall accuracy rose to 74.2%. However, the accuracy of severely degraded (i.e., bare ground) decreased, and the accuracy of degraded land also decreased to 65.8%. The overall accuracy rose to 76% if the classification was performed to the same image segmented at 20 pixels. However, the accuracy for degraded land was lowered further to 64.6%, even though the accuracy of bare ground was improved to 82.1%. It is concluded that object-oriented image classification does not fare much better than pixel-based image classification in mapping degraded lands from moderate-spatial-resolution satellite data such as ASTER due to their fragmented and discontinuous spatiality. At the 15 m resolution level, scale does not seem to exert a noticeable impact on the object-based classification accuracy.