An Overview of the Emergence and Challenges of Land Reclamation: Issues and Prospect

Ukhurebor, Kingsley Eghonghon; Aigbe, Uyiosa Osagie; Onyancha, Robert Birundu; Ndunagu, Juliana Ngozi; Osibote, Otolorin Adelaja; Emegha, Joseph Onyeka; Balogun, Vincent A.; Kusuma, Heri Septya; Darmokoesoemo, Handoko

doi:10.1155/2022/5889823

Cited by 20 publications

(8 citation statements)

References 56 publications

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“…The efficacy of remedial interventions may be compromised depending on the extent of these interactions. Most interventions rely on historical detection of changes in LCLU, which can hinder effective remedial outcomes due to potential differences in future changes to the landscape structure (Kgaphola et al, 2023;Ukhurebor et al, 2022;Bajocco et al, 2012).…”

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

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“…Taking into account the results of analytical studies, the study of climatic changes at both the regional and local levels is relevant today, so it is important to achieve our goal to trace the deviation trends of the main climatic parameters on the territory of individual regions and even settlements [3,4,[20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Study of contemporary climate changes in the Ukrainian humid zone (on the example of the Volyn Region)

Kuzmych,

Voropai,

Kharlamov

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Modern agricultural production is under the direct influence of climatic changes, which is also reflected in the water regime formation of the soil during the cultivation of crops. Calculations were made and graphical materials were created for average daily, average decadal, and average monthly values of temperatures and the amount of precipitation per day, month, and year. The influence of modern climatic factors on the water regime formation of the soil (according to the data of the weather station of the city of Kovel, Volyn region) over the last 11-year period has been established. It was determined that the distribution of precipitation is as follows: the share of light precipitation (< 3 mm) is on average 13.9%, moderate (4–14 mm) is 43.3%, significant (15–49 mm) is 36.4%, strong (50–79 mm) is 5.4%, and extraordinary (80–100 mm) is 1% of the total amount. It was determined that the active layer of the soil is formed due to the influence of moderate and significant precipitation. Informational materials on the distribution of atmospheric precipitation by year in quantitative and percentage indicators and their repeatability are presented in an interactive visual report with graphic materials (dashboard), which allows the processing of large arrays of data. It was determined that modern climatic changes (increased air temperature, uneven distribution, change in the nature, intensity, and structure of precipitation, and the increase in the number of cases of heavy downpours that are local in the warm period of the year) don’t allow for the effective accumulation of moisture in the active soil layer. The obtained information materials are the basis for developing a methodology for substantiating the parameters of the management of the soil-water regime on reclaimed lands, taking into account the peculiarities of its formation in modern economic conditions and climate changes.

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“…Salinity in the soil can be acquired naturally or by land‐use activities such as cleaning mines, extracting resources like oil, and agricultural activities (Shaygan & Baumgartl, 2022). Further, the condition of the soil, soil type, irrigation activity, depth of groundwater, and salinity of the water source, as well as land management practices employed, can have an impact on soil salinization (Ukhurebor et al, 2022). Arid and semi‐arid regions are the main salt‐affected areas (Ferreira et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Assessment of salt‐affected soil in Indo‐Gangetic Plain of Uttar Pradesh and prediction using regression model

Hiranmai,

Neeraj

2023

Land Degrad Dev

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In the present study, multiple sites in the Indo‐Gangetic Plain (IGP) were examined, and most of the sites were found to be non‐saline, with electrical conductivity (EC) values below 4.0 dSm−1. The highest value of EC 8.5 dSm−1 was reported for samples collected from JAN‐2, which, followed by site RBL‐3, also shows a high value of EC 6.5 dSm−1. The highest CEC (cmolkg−1) was reported in samples collected from site JAN‐1 (8.4), JAN‐2 (8.2), and JAN‐3 (8.21), and the lowest at RBL‐2 (3.99). The highest exchangeable sodium percentage (ESP) value of 34.07% was observed at RBL‐3, indicating a potential salinity risk associated with elevated sodium contents. The analysis showed that salt was present at all study sites, and there were strong correlations between the variables examined. The relationship between measured ESP and predicted ESP shows a strong correlation with an r2 value of 0.91, indicating the applicability of ESP–sodium adsorption ratio (SAR). Paired samples t‐test results p‐value = 0.27 indicated no significant difference (p > 0.05) between the soil ESP values predicted by the soil ESP–SAR model and those obtained through laboratory testing. The statistical results of the study suggested that the soil ESP–SAR model was reliable for predicting ESP values. The soil ESP differences between the two methods were normally distributed, and the 95% limits of agreement were calculated as −7.79 and 5.55. The mean difference between the ESP–SAR model and the “Measured” method was calculated as 2.24, representing the average deviation between the two. The study presents the ESP–SAR model as a cost‐effective and efficient method for estimating soil ESP and assessing soil salinity. By analyzing soil properties and validating the model, it provides valuable insights for policymakers and stakeholders in combating agricultural land degradation in the IGP. The novelty lies in its comprehensive evaluation of soil salinity indices, reinforcing the applicability of the ESP–SAR model for future research and agricultural management.

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An Overview of the Emergence and Challenges of Land Reclamation: Issues and Prospect

Cited by 20 publications

References 56 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

Study of contemporary climate changes in the Ukrainian humid zone (on the example of the Volyn Region)

Assessment of salt‐affected soil in Indo‐Gangetic Plain of Uttar Pradesh and prediction using regression model

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