Frequency Analysis of Modis Ndvi Time Series for Determining Hotspot of Land Degradation in Mongolia

Nasanbat, Elbegjargal; Sharav, S.; Sanjaa, T.; Lkhamjav, O.; Magsar, E.; Tuvdendorj, Battsetseg

doi:10.5194/isprs-archives-xlii-3-1299-2018

Cited by 5 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remote sensing data have often been used to study the spatiotemporal characteristics of desertification in Mongolia [20,23,30,32,57,67] and the determination of desertification hotspots [33]. The indices derived from remote sensing data have been used to study the relationship between desertification and influencing factors.…”

Section: Research Methods Of Desertificationmentioning

confidence: 99%

“…Remote sensing data have been widely applied in the desertification monitoring in Mongolia. Previous studies on land desertification monitoring were mostly conducted based on the MODIS data [31][32][33], the United States National Oceanic and Atmospheric Administration Advanced Very High-Resolution Radiometer (NOAA-AVHRR) data [34], European Space Agency (ESA) land cover data [29], and Landsat data [35]. The spatial resolution of these data for the whole Mongolia study is generally low, which constrains the accuracy of the analysis results and an in-depth process understanding of desertification.…”

Section: Spatiotemporal Changes Of Desertificationmentioning

confidence: 99%

See 1 more Smart Citation

Research Progress of Desertification and Its Prevention in Mongolia

Liang

Wang

et al. 2021

Sustainability

View full text Add to dashboard Cite

Mongolia is a globally crucial region that has been suffering from land desertification. However, current understanding on Mongolia’s desertification is limited, constraining the desertification control and sustainable development in Mongolia and even other parts of the world. This paper studied spatiotemporal patterns, driving factors, mitigation strategies, and research methods of desertification in Mongolia through an extensive review of literature. Results showed that: (i) remote sensing monitoring of desertification in Mongolia has been subject to a relatively low spatial resolution and considerable time delay, and thus high-resolution and timely data are needed to perform a more precise and timely study; (ii) the contribution of desertification impacting factors has not been quantitatively assessed, and a decoupling analysis is desirable to quantify the contribution of factors in different regions of Mongolia; (iii) existing desertification prevention measures should be strengthened in the future. In particular, the relationship between grassland changes and husbandry development needs to be considered during the development of desertification prevention measures; (iv) the multi-method study (particularly interdisciplinary approaches) and desertification model development should be enhanced to facilitate an in-depth desertification research in Mongolia. This study provides a useful reference for desertification research and control in Mongolia and other regions of the world.

show abstract

Section: Research Methods Of Desertificationmentioning

confidence: 99%

Section: Spatiotemporal Changes Of Desertificationmentioning

confidence: 99%

Research Progress of Desertification and Its Prevention in Mongolia

Liang

Wang

et al. 2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…For instance, it is suggested that a higher value of spatial and spectral detail is appropriate in the case that the classified object is smaller than the spatial resolution of satellite image [35,36]. Therefore, values of spectral and spatial detail were set to 18 for all imagery (possible range: [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The size of objects is defined by the parameter of minimum segment size, which merges segments smaller than this criterion with their best fitting neighboring segment.…”

Section: Segmentationmentioning

confidence: 99%

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

2020

View full text Add to dashboard Cite

Information on the spatial distribution of human disturbance is important for assessing and monitoring land degradation. In the Eastern Mongolian Steppe Ecosystem, one of the major driving factors of human-induced land degradation is the expansion of road networks mainly due to intensifications of oil exploration and exploitation. So far, neither the extents of road networks nor the extent of surrounding grasslands affected by the oil industry are monitored which is generally labor consuming. This causes that no information on the changes in the area which is affected by those disturbance drivers is available. Consequently, the study aim is to provide a cost-effective methodology to classify infrastructure and oil exploitation areas from remotely sensed images using object-based classifications with Random Forest. By combining satellite data with different spatial and spectral resolutions (PlanetScope, RapidEye, and Landsat ETM+), the product delivers data since 2005. For the classification variables, segmentation, spectral characteristics, and indices were extracted from all above mentioned imagery and used as predictors. Results show that overall accuracies of land use maps ranged 73%–93% mainly depending on satellites’ spatial resolution. Since 2005, the area of grassland disturbed by dirt roads and oil exploitation infrastructure increased by 88% with its highest expansion by 47% in the period 2005–2010. Settlements and croplands remained relatively constant throughout the 13 years. Comparison of multiscale classification suggests that, although high spatial resolutions are clearly beneficial, all datasets were useful to delineate linear features such as roads. Consequently, the results of this study provide an effective evaluation for the potential of Random Forest for extracting relatively narrow linear features such as roads from multiscale satellite images and map products that are possible to use for detailed land degradation assessments.

show abstract

“…The expansion of dirt road networks as a main type of linear infrastructure has become more apparent in recent decades, mainly due to the rapid economic development [6]. At the same time, approximately 76.8% of the Mongolian countryside is affected by land degradation while 6.1% of the land is extremely degraded due to human activities [7,8]. Both developments are related: The usage of dirt roads accelerates the land degradation process in Mongolian grasslands through vehicle travel that causes the destruction of the surface vegetation, soil compaction, loss of soil aggregation, changes in soil texture, partial or complete removal of the top layer of soil, and loss of soil organisms [9].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Dashpurev¹,

Bendix²,

Lehnert³

2020

Preprint

View full text Add to dashboard Cite

<p>Rapid changes in land use due to intensifications of oil exploration and exploitation adversely affect the Eastern Mongolian steppe ecosystem. The expansion of supporting infrastructure and dirt road networks for oil production contribute to accelerate the human-induced land degradation process in the grasslands. So far, neither the extents of road networks nor the extent of surrounding grasslands affected by the oil industry are monitored which is generally labor consuming. This causes that no information on the changes in the area which is affected by those disturbance drivers is available. Therefore, the major aim of this study is to provide a cost-effective model to map the supporting infrastructure, sites and dirt roads of oil exploitation through classifying remotely sensed images using object-based classifications with Random Forest. By combining satellite data with different spatial and spectral resolutions (PlanetScope, RapidEye, and Landsat ETM+), the product delivers data since 2005. In the image classification, the variables of segmentation, spectral characteristics, and indices were extracted from all above mentioned imagery and used as predictors. Within this study examined the comparison analysis in order to quantify the uncertainty arising from the combination of data from different sensors in their spectral and spatial configurations. Besides that, this study analyzed the consequence of supporting infrastructure and dirt roads on surrounding ecosystems combining data from field vegetation surveys and drone imagery. Results show that overall accuracies of land use maps ranged 73%&#8211;93% mainly depending on satellites&#8217; spatial resolution. Since 2005, the area of grassland disturbed by dirt roads and oil exploitation infrastructure increased by 88% with its highest expansion by 47% in the period 2005&#8211;2010. Consequently, the comparison of multiscale classification suggests that, although high spatial resolutions are clearly beneficial, all datasets were useful to delineate linear features such as roads. Furthermore, the results of this study provide an effective evaluation for the potential of Random Forest based model for extracting relatively narrow linear features such as roads from multiscale satellite images and map products that are possible to use for detailed land degradation assessments.</p>

show abstract

Frequency Analysis of Modis Ndvi Time Series for Determining Hotspot of Land Degradation in Mongolia

Cited by 5 publications

References 16 publications

Research Progress of Desertification and Its Prevention in Mongolia

Research Progress of Desertification and Its Prevention in Mongolia

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Contact Info

Product

Resources

About