Assessment of land degradation ‘on the ground’ and from ‘above’

Kirui, Oliver K.; Mirzabaev, Alisher; Braun, Joachim von

doi:10.1007/s42452-021-04314-z

Cited by 32 publications

(16 citation statements)

References 44 publications

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“…There are currently no sub-national studies for KK districts. With around 27% of the area in KK being degraded, it is less affected by LD compared to national assessments found in [2,3] or [11]. However, the comparison with these studies is difficult, as they used different monitoring periods (ending in the 2000s and 2016) and only a subset of the methodology (LP trend) and coarse resolution imagery (i.e., 8 km AVHRR data).…”

Section: Discussionmentioning

confidence: 99%

“…This suggests that our study brought LD assessment in Tanzania one step further by assessing three components of LD according to the SDG 15.3.1 indicator. Further, using significantly higher spatial resolution, spatial datasets allowed us to reveal spatial patterns of LD beyond pixel sizes of 8 km [2,3,11] or 1 km [24].…”

Section: Discussionmentioning

confidence: 99%

“…Tanzania is a hot spot of LD, with more than half its area showing signs of degradation [2,11]. It has the highest annual forest area net loss in East Africa and the fifthhighest worldwide [12].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the existing LD maps have a coarse spatial resolution and provide inconsistent estimates of the affected area [8]. For example, previous estimates of the extent of LD in Tanzania range from 41% to half of the country [2,3,11]. These variations emanate from differences in definitions of LD, monitoring methods and lack of appropriate data [6,21].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessment of Land Degradation in Semiarid Tanzania—Using Multiscale Remote Sensing Datasets to Support Sustainable Development Goal 15.3

et al. 2021

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Monitoring land degradation (LD) to improve the measurement of the sustainable development goal (SDG) 15.3.1 indicator (“proportion of land that is degraded over a total land area”) is key to ensure a more sustainable future. Current frameworks rely on default medium-resolution remote sensing datasets available to assess LD and cannot identify subtle changes at the sub-national scale. This study is the first to adapt local datasets in interplay with high-resolution imagery to monitor the extent of LD in the semiarid Kiteto and Kongwa (KK) districts of Tanzania from 2000–2019. It incorporates freely available datasets such as Landsat time series and customized land cover and uses open-source software and cloud-computing. Further, we compared our results of the LD assessment based on the adopted high-resolution data and methodology (AM) with the default medium-resolution data and methodology (DM) suggested by the United Nations Convention to Combat Desertification. According to AM, 16% of the area in KK districts was degraded during 2000–2015, whereas DM revealed total LD on 70% of the area. Furthermore, based on the AM, overall, 27% of the land was degraded from 2000–2019. To achieve LD neutrality until 2030, spatial planning should focus on hotspot areas and implement sustainable land management practices based on these fine resolution results.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Land Degradation in Semiarid Tanzania—Using Multiscale Remote Sensing Datasets to Support Sustainable Development Goal 15.3

et al. 2021

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“…Due to the complexity of land degradation, there are no universal guidelines for selecting the exact variables that influence this phenomenon. But, according to different studies done in this field, the most usual variables which influence LD are soil salinity (Jabbar & Chen, 2008; Sadeghiravesh et al, 2021), quantitative and qualitative changes in groundwater (Nasirian et al, 2019), reduction and change in vegetation cover (Masoudi et al, 2018; Kirui et al, 2021), rainfall (Zhang et al, 2020), slope inclination (Sadeghiravesh et al, 2021), altitude (Abdel‐Kader, 2019), and land‐use (Bestelmeyer et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

et al. 2022

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Although land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.

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Ensuring sustainable water security through sustainable land management: Research evidences for policy

Dagnachew,

Gebrehiwot,

Bewket

et al. 2024

World Water Policy

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Sustainable land management (SLM) practices is a key to reducing rates of land degradation and has proven to ensure water security by increasing soil moisture availability, decreasing surface runoff, decreasing soil erosion, increasing infiltration, and decreasing flood discharge. Land degradation is adversely affecting over 75% of the Earth's land surface and could exceed 90% by 2050. The rate of soil erosion will increase by 66% during the period 2015–2070. Over 85% of the land in Ethiopia is moderately to very severely degraded at an estimated cost of $4.6 billion annually. So far, only 18% of Ethiopia's cropland area is covered by SLM practices for the last 40 years of intensive interventions. Water security, in turn, is a powerful and multidimensional option that includes water availability, accessibility, use, and stability across time. In Ethiopia, though, the interconnections between SLM and water security are intimate and numerous, their linkage is very loose and yet to be understood. Ethiopia is among the 16 LDCs and is critically water‐insecure, implying that abundant natural water availability does not necessarily ensure water security as it might be poor water quality, inaccessible, and unsafe. Hence, national and local level of SLM and water security linkages, impacts, and policy implications must come on board in the least developing countries like Ethiopia. Therefore, SLM‐water security policy is crucial in successful SLM because governments/higher officials are capable of promoting well‐informed water security decisions.

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Assessment of land degradation ‘on the ground’ and from ‘above’

Cited by 32 publications

References 44 publications

Assessment of Land Degradation in Semiarid Tanzania—Using Multiscale Remote Sensing Datasets to Support Sustainable Development Goal 15.3

Assessment of Land Degradation in Semiarid Tanzania—Using Multiscale Remote Sensing Datasets to Support Sustainable Development Goal 15.3

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

Ensuring sustainable water security through sustainable land management: Research evidences for policy

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