Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8

Urban, Marcel; Berger, Christian; Mudau, Tami E.; Heckel, Kai; Truckenbrodt, J; Odipo, Victor Onyango; Smit, Izak P.J.; Schmullius, Christiane

doi:10.3390/rs10091482

Cited by 68 publications

(49 citation statements)

References 64 publications

(78 reference statements)

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“…In this study, it was found from the study that in 2015, it was the time when drought was at most level equal to 91.54 or equal to 44975.23 km 2 . When the results of the study were used to compare with other similar researches such as research on "Application of remote sensing technology for drought monitoring in Mahasarakham Province, Thailand" by Laosuwan et al, (2016), research on "Drought Detection by Application of Remote Sensing Technology and Vegetation Phenology" by Uttaruk & Laosuwan, (2017), research on "Remote Sensing Based Drought Monitoring In The Middle-Part of Northeast Region of Thailand" by Rotjanakusol & Laosuwan, (2018), research on "Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8" by Urban et al, (2018), it was found that 4 researches had the analysis results in the same direction with that of this study. The result of this study can be used as criteria in making reasonable decision on the Chi watershed area affected by drought, and also can be used to assess the drought area rapidly and reliably.…”

Section: Discussionmentioning

confidence: 99%

An Investigation of Drought Around Chi Watershed During Ten-Year Period Using Terra/Modis Data

Rotjanakusol¹,

Laosuwan²

2019

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To examine the drought affected area is highly challenging because the drought is a natural disaster that starts and expands slowly. The severity of the drought is different when the rainfall is imbalance in the area. The objective of this study was to analyze the drought around Chi watershed with total area of 49,131.920 km 2 by using data from Terra/MODIS satellite during 10 years (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016). On part of method operation, the NDVI data was obtained from Terra/MODIS satellite, and VCI (Vegetation Condition Index) analysis was performed to examine the condition of plants which could identify the condition of drought in the area. According to the study, it was found that in 2015, it was the time when the drought affected area was at most which was equal to 91.54 percent, or 44975.23 km 2 . When the analysis results of these ten years were brought to find relationship with rainfall, it was found that the decision coefficient was R 2 = 0.913. It can be concluded from the results of this study that VCI could be used as indicator and could identify the drought in Chi watershed in terms of area and time reasonably.

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Section: Discussionmentioning

confidence: 99%

An Investigation of Drought Around Chi Watershed During Ten-Year Period Using Terra/Modis Data

Rotjanakusol¹,

Laosuwan²

2019

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“…Further emphasis is on computational approaches taking advantage of and adding value to publicly available satellite imagery such as data from NASA's Landsat missions and ESA's Sentinel (Copernicus) programme (e.g. Cremer et al 2018;Urban et al 2018). The resulting methods are used to derive land surface parameters related to the status and dynamics of South Africa's terrestrial ecosystems (e.g.…”

Section: Leveraging Earth Observation Data To Support Ecosystem Monitmentioning

confidence: 99%

“…fuel biomass, woody cover, vegetation heights, land use) to implement environmental and socio-ecological mapping, monitoring, and management with direct societal benefits (e.g. Urbazaev et al 2015;Odipo et al 2016;Urban et al 2018). An example application is the spatiotemporal characterisation of fuel biomass and fuel moisture content for improved fire management in the Kruger National Park.…”

Section: Leveraging Earth Observation Data To Support Ecosystem Monitmentioning

confidence: 99%

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Linking scales and disciplines: an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management

et al. 2019

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Southern Africa is particularly sensitive to climate change, due to both ecological and socioeconomic factors, with rural land users among the most vulnerable groups. The provision of information to support climate-relevant decision-making requires an understanding of the projected impacts of change and complex feedbacks within the local ecosystems, as well as local demands on ecosystem services. In this paper, we address the limitation of current approaches for developing management relevant socio-ecological information on the projected impacts of climate change and human activities. We emphasise the need for linking disciplines and approaches by expounding the methodology followed in our two consecutive projects. These projects combine disciplines and levels of measurements from the leaf level (ecophysiology) to the local landscape level (flux measurements) and from the local household level (socio-economic surveys) to the regional level (remote sensing), feeding into a variety of models at multiple scales. Interdisciplinary, multi-scaled, and integrated socio-ecological approaches, as proposed here, are needed to compliment reductionist and linear, scalespecific approaches. Decision support systems are used to integrate and communicate the data and models to the local decision-makers.Observed temperature increases over large parts of South Africa during the period 1931-2015 have occurred at rates of about twice the global mean, and this trend is projected to continue into the future (DEA 2017). Other projections across Southern Africa include changes in rainfall amount, variability, intensity and seasonality, and increases in the likelihood of extreme

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“…There are many applications for monitoring the earth's phenomena using optical remote sensing images e.g., agriculture (He et al, 2018), forestry (Townshend et al, 2012;Zhu et al, 2012b), marine (Roelfsema et al, 2013), land use land cover analysis (Nutini et al, 2013;Varade et al, 2018), and disaster: floods (Arnesen et al, 2013;Mason et al, 2014;Schlaffer et al, 2015), droughts (Urban et al, 2018), earthquakes (Fan et al, 2019). Unfortunately, there is a serious issue related to cloud coverage on the optical remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Cloud removal analysis of remote sensing satellite imagery

Candra¹

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Remote sensing satellite images are widely used for many applications, such as land use land cover mapping, agriculture, forestry, marine, disaster monitoring, etc. Unfortunately, there is an obstacle regarding cloud cover. The existence of clouds interferes remote sensing activities. Moreover, about two third of the earth's surface is covered by cloud at any one time. This issue increases the difficulty to support operational remote sensing monitoring applications.This thesis developed, tested and applied methods for cloud masking and removal that work in tropical regions. Four specific objectives were addressed: (1) developing a cloud and cloud shadow masking method for Landsat 8 images in Indonesia, ensuring accuracy is assessed over heterogeneous land cover; (2) improving the ability of the proposed method for detecting cloud and cloud shadow for Landsat 8 images with a variety of cloud types in Indonesia; (3) developing a method for filling masked pixels in Landsat 8 images in Indonesia; and (4) extension a cloud and cloud shadow masking method for Sentinel-2 images.After introductory, review and research approach chapters one -three, chapter four developed a method for cloud and cloud shadow masking called Multi-temporal Cloud Masking (MCM) for Landsat 8 images in tropical environments. This method used two kinds of data: (1) target image and(2) reference image. A target image is an image may have cloud and cloud shadow contaminated pixels and a reference image is a cloud-free image. This method utilizes the difference of reflectance values between those images. The MCM algorithm was applied and evaluated in a part of Indonesia which has a heterogeneous land cover. This chapter concluded that the MCM algorithm can be used to accurately detect cloud and cloud shadow for Landsat 8 images from areas with a heterogeneous land cover in tropical environments.Chapter five developed and assessed an approach for cloud and cloud shadow removal called Multitemporal Cloud Removal (MCR) for Landsat 8 images. This approach has three main steps: (1) radiometric correction; (2) cloud and cloud shadow detection; and (3) image reconstruction. In this chapter, the previous MCM was improved for detecting a variety of cloud types. By using images from a sequence acquisition date, the issue of varying land cover changes was minimised. In the image reconstruction step, a method for filling masked pixels was applied. At the end of the process, cloud and cloud shadow free image was generated. To evaluate the results, selected Landsat 8 images over a limited area in Indonesia with heterogeneous land cover and a variety of cloud types were ii tested. As a result, cloud and cloud shadow contaminated pixels on Landsat 8 images were able to be removed in several test cases. The resultant images had high visual and statistical similarities with the reference image. This evaluation provided a preliminary indication of the utility of MCR in removing cloud and cloud shadow for Landsat 8 images.Chapter six expanded the MCM approach designed ...

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Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8

Cited by 68 publications

References 64 publications

An Investigation of Drought Around Chi Watershed During Ten-Year Period Using Terra/Modis Data

An Investigation of Drought Around Chi Watershed During Ten-Year Period Using Terra/Modis Data

Linking scales and disciplines: an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management

Cloud removal analysis of remote sensing satellite imagery

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