Reconstructed Aeolian Surface Erosion in Southern Mongolia by Multi-Temporal InSAR Phase Coherence Analyses

Kim, Jung-Rack; Dorjsuren, Munkhzul; Choi, Yunsoo; Purevjav, Gomboluudev

doi:10.3389/feart.2020.531104

Cited by 10 publications

(14 citation statements)

References 32 publications

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“…The input features for vegetation or other surface features in the corresponding period can then be incorporated as user-defined data- For the validation/assessment of the model/monitoring, the datasets provided by the local collaborators, such as time series measurements of significant drivers and consequences of LD (Kim et al, 2021), could be used as designed in Figure 1. Especially we proposed the employment of Structure from motion (SfM) (Schonberger & Frahm, 2016) using unmanned aerial vehicles would be the optimal tool as proposed by multiple researchers (Grottoli et al, 2020;Kim et al, 2020;Lin et al, 2018;Luo et al, 2020). For the assessment of ML prediction, it should be noted that we used splitting of ground truths by InSAR observations into training and validations, respectively, presented in Figure 4.…”

Section: Discussion and Future Developmentmentioning

confidence: 99%

“…We addressed this problem by decomposing the simulated vegetation effects from InSAR phase coherence, which are modeled by the vegetation index created by Layer 0 (Refer to Kim et al (2021) for the detailed technical description). The additional error components were removed by principal component analysis (PCA) on time series observations, as presented by Kim et al (2020). We organized the InSAR LD monitoring component employing public domain InSAR datasets or the public domain InSAR processors (Lazecký et al, 2020; Rosen et al, 2018) with QGIS add‐on components.…”

Section: Methodsmentioning

confidence: 99%

“…InSAR directory with a 100-m resolution. Then we applied PCA to the time series InSAR pairs in each specific Sentinel-1 frame and orbit and decomposed the vegetation effect using yearly average MODIS EVI based on the algorithms developed by Kim et al (2020). The output of the InSAR monitoring component constituted a ground truth and was feed-forwarded to the training component together with the input features described in Table 2.…”

Section: Test On the Thar Desertmentioning

confidence: 99%

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Implementation of a multi‐layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands

et al. 2023

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Various earth observation (EO) data and related spatial technologies are employed to monitor land degradation (LD) and its impacts on the drylands. However, a comprehensive framework that integrates multiple data sources and metrics to generate a consistent output still needs to be explored. Furthermore, the lack of consistency and the limited reliabilities of existing approaches make it challenging to identify proper countermeasures (avoid, reduce, reverse LD) and support decisions to combat LD in line with the Land Degradation Neutrality (LDN) Framework. The “Multi‐Layered Land Degradation Tracer (ML‐LDT)” is a framework that addresses the above research gaps. It is based on a comprehensive framework that integrates data extraction, LD monitoring, and forecasting capacities and consists of three functional layers, that is, a base data processor, monitoring/modeling components, and a forecasting layer employing machine learning (ML) algorithms. ML‐LDT has a state‐of‐art capacity to model wind erosion together with sand and dust storms (SDS), making it particularly suited to the arid lands most prone to these processes. It is implemented over cloud processors with open‐source components based on industry standards. Therefore, the routines are easily adaptable to user requirements in LDN planning. We evaluated the ML‐LDT framework in the Indian Thar Desert and Inner Mongolian drylands. The results showed the overall robustness of the procedures and that ML‐based models provide good insights to forecast future LD/SDS patterns that can be used to simulate LDN scenarios and targets. We validated the forecasting results against LD maps and remote sensing observations.

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Section: Discussion and Future Developmentmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Test On the Thar Desertmentioning

confidence: 99%

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Implementation of a multi‐layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands

et al. 2023

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“…Direct measurement of ongoing aeolian erosion as a high‐resolution product is seldom available; thus, we employed interferometric synthetic aperture RADAR (InSAR) phase coherence (Phase Coh.) time‐series product (Kim et al, 2020) as an indicator of ongoing land erosion, especially induced by aeolian process (details on phase Coh. product in S. Table S1 of the supplement).…”

Section: Methodsmentioning

confidence: 99%

Mapping land degradation and sand and dust generation hotspots by spatiotemporal data fusion analysis: A case‐study in the southern Gobi (Mongolia)

Kim

Dorjsuren²,

Zucca

et al. 2022

Land Degrad Dev

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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.

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“…Currently, desertification and land degradation in Mongolia have severely affected 90% of the territory, and more than 30% of the territory is occupied by the deserts or semi-deserts [ 16 ]. The Gobi Desert in Southern Mongolia has been recognized as the strongest dust storm hot spot [ 17 ]. Extremely dry climate, sparse vegetation, intensive ultraviolet radiation, barren soils with poor humus and moisture content make large areas of Gobi Desert in Mongolia pristine and undisturbed [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Diversity and Antibacterial Potentiality of Cultivable Actinobacteria from the Soil of the Saxaul Forest in Southern Gobi Desert in Mongolia

Osterman

Sun

2022

Microorganisms

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Saxaul (Haloxylon ammodendron) is the most widespread plant community in the Gobi Desert in Mongolia, which plays important roles in wind control, sand fixation and water conservation. Investigations of soil-derived actinobacteria inhabiting in the saxaul forest in Gobi Desert in Mongolia have been scarce. In this study, biodiversity of culturable actinobacteria isolated from soil of the saxaul forest in Southern Gobi Aimak (Southern Gobi Province) of Mongolia was characterized and their potential to produce compounds with antibacterial activities was assessed. A total of 172 actinobacterial strains were recovered by culture-based approaches and were phylogenetically affiliated into 22 genera in 13 families of seven orders. Forty-nine actinobacterial isolates were selected to evaluate the antibacterial activities and their underlying mechanism of action was screened by means of a dual-fluorescent reporter assay (pDualrep2). Twenty-three isolates exhibited antagonistic activity against at least one of the tested pathogens, of which two Streptomyces strains can attenuate protein translation by ribosome stalling. Combinational strategies based on modern metabolomics, including bioassay-guided thin-layer chromatography (TLC), UPLC-QTOF-MS/MS based structural annotation and enhanced molecular networking successfully annotated chloramphenicol, althiomycin and granaticin and their derivatives as the antibacterial compounds from extracts in three Streptomyces strains, respectively. This work demonstrates that UPLC-MS/MS-based structural identification and enhanced molecular networking are effective strategies to rapidly illuminate the bioactive chemicals in the microbial extracts. Meanwhile, our results show that the saxaul forest in Mongolia Gobi Desert is a prospective source for discovering novel actinobacteria and biologically active compounds.

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Reconstructed Aeolian Surface Erosion in Southern Mongolia by Multi-Temporal InSAR Phase Coherence Analyses

Cited by 10 publications

References 32 publications

Implementation of a multi‐layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands

Implementation of a multi‐layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands

Mapping land degradation and sand and dust generation hotspots by spatiotemporal data fusion analysis: A case‐study in the southern Gobi (Mongolia)

Exploring the Diversity and Antibacterial Potentiality of Cultivable Actinobacteria from the Soil of the Saxaul Forest in Southern Gobi Desert in Mongolia

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