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

Bär, Valentin; Akinyemi, Felicia O.; Speranza, Chinwe Ifejika

doi:10.1016/j.ecolind.2023.110252

Cited by 12 publications

(2 citation statements)

References 25 publications

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“…Globally, land degradation is on the rise, primarily as a result of changes in land cover and land use (LCLU). These changes are especially evident in the conversion of forests into cropland and settlements (Potapov et al, 2022;Hu et al, 2021;Winkler et al, 2021;Bär et al, 2023). Surprisingly, it has been discovered that global land cover and land use changes are actually four times greater than previously estimated, largely due to uncertainties in the methods and data sets used (Winkler et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Integrated use of the CA-Markov model and the Trends.Earth module to enhance the assessment of land cover degradation: Application in the Upper Zambezi Basin, southern Africa

Zimba,

Banda,

Mbewe

et al. 2024

Preprint

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This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata-Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the European Space Agency Climate Change Initiative (ESA CCI) global LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.34 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the ESA CCI global LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of -0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. By leveraging the predictive power of the CA-Markov model and the capabilities of the LCD model, as evidenced in this study, valuable information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.

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Section: Introductionmentioning

confidence: 99%

Integrated use of the CA-Markov model and the Trends.Earth module to enhance the assessment of land cover degradation: Application in the Upper Zambezi Basin, southern Africa

Zimba,

Banda,

Mbewe

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…However, less attention has been provided to Land Cover change and, currently, a comprehensive assessment of Land Cover change across all categories of the Arealstatistik at the national scale is still missing. Indeed, most research concentrates on a specific LC class, such as forest cover change [31] or cropland and grassland [32], producing information from LC data like a nationwide habitat map [33], LC degradation [34] or studying the effects on Gross Primary Production [35] Based on these previous considerations, this study aimed to explore the spatiotemporal patterns of Land Cover change in Switzerland using the official LC data for Switzerland, therefore filling the identified gap by providing a first extensive assessment. To achieve this objective, the following questions were investigated:…”

Section: Introductionmentioning

confidence: 99%

Exploring Switzerland’s Land Cover Change Dynamics Using a National Statistical Survey

Thomas

Giuliani

2023

Land

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Timely and reliable Land Use and Cover change information is crucial to efficiently mitigate the negative impact of environmental changes. Switzerland has the ambitious objective of being a sustainable country while remaining an attractive business location with a high level of well-being. However, this aspiration is hampered by increasing pressures that are significantly impacting the environment and putting serious demands on land. In the present study, we used the national Land Cover (LC) dataset, named ArealStatistik, produced by the Federal Statistical Office, to explore the spatiotemporal patterns of Land Cover in Switzerland, providing a comprehensive assessment of land cover change at the national scale. Results indicate that, in general, Switzerland has undergone small, spatially dispersed, dynamic, and gradual change trends, with high rates of transition between low growing Brush Vegetation and forest LC classes in recent years. These pixel-level trends are more important in the lower altitude plateau and Jura regions, while greater changes in the spatial configuration of LC are observed in the alpine regions. However, findings also suggest that identifying drivers and understanding the rate of change are limited by the spatial resolution and temporal update frequency of the ArealStatistik. The ability to understand these drivers would benefit from a high-resolution annual LC dataset. Such a data product can be produced using the ArealStatistik together with dense satellite data time-series and Machine/Deep Learning techniques.

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Determination of Land Degradation in Romania at a Local Scale Using Advanced Analytical Techniques

Temerdek‐Ivan,

Benedek,

Török

et al. 2024

Land Degrad Dev

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Land degradation (LD) threatens the food security and general welfare of many people globally. Land Degradation Neutrality (LDN) is a pivotal goal within the global sustainability agenda, particularly under Sustainable Development Goal (SDG) 15.3.1, which measures the proportion of land that is degraded over total land area. Romania, with its diverse landscapes and significant agricultural sector, faces notable challenges in this area. Assessing this SDG indicator serves to identify vulnerable areas and assists policymakers in defining the necessary strategic instruments and actions. Most previous studies have assessed land degradation using low‐ or moderate‐resolution data without clearly identifying the triggering factors, thereby limiting the ability to detect changes at the sub‐national level and analyze in detail the influence of these factors on the environment. This research is the first to examine the relationship between the proportion of land that is degraded and 20 potential triggering factors in cities and municipalities, aiming to identify key drivers of land degradation and provide insights for urban planning and policy‐making. The study integrates a series of high and medium‐resolution statistical, geospatial, and EO data sources to effectively assess land degradation and identify triggering factors in 103 municipalities and 216 cities. Using multiple regression analysis (MRA) and the Random Forest Method (RF), this study analyzed various predictive factors influencing LD, revealing that changes in land cover, temperature, and multi‐annual average precipitation, along with atmospheric pollutants (CO, SO2) and Land Surface Temperature, significantly contribute to land degradation in large cities. These factors accounted for approximately 83.3% (MRA) and 96.7% (RF) of the variation in the land degradation indicator, underscoring their roles in influencing land degradation trends. According to the results, 2.3% of Romania's area was degraded during the 2015–2022 period, 58% remained stable, 38% showed improvements, and the remaining 1.7% represented water bodies. By identifying vulnerable communes and counties, and the triggering factors, the study supports the implementation of appropriate measures to combat LD, a crucial step in Romania's efforts to achieve LDN by 2030.

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

Cited by 12 publications

References 25 publications

Integrated use of the CA-Markov model and the Trends.Earth module to enhance the assessment of land cover degradation: Application in the Upper Zambezi Basin, southern Africa

Integrated use of the CA-Markov model and the Trends.Earth module to enhance the assessment of land cover degradation: Application in the Upper Zambezi Basin, southern Africa

Exploring Switzerland’s Land Cover Change Dynamics Using a National Statistical Survey

Determination of Land Degradation in Romania at a Local Scale Using Advanced Analytical Techniques

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