Ground subsidence observation of solid waste landfill park using multi-temporal radar interferometry

Baek, Won-Kyung; Jung, Hyung-Sup; Jo, Minjeong; Lee, Won-Jin; Zhang, Lei

doi:10.1080/12265934.2018.1468275

Cited by 17 publications

(18 citation statements)

References 18 publications

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“…The phenomenon is consistent with other studies that have reported localized subsidence attributed to the higher density of buildings (Xu et al, 2012;Yang et al, 2018)). Additionally, the deformation observed at the edges of Dandora landfill(location m) can be attributed to consolidation and the biochemical process, similar observations have been noted in other places like the case of Baek et al (2019), the continued filling of the waste poses danger to the neighbouring properties. Similarly, the recent major developments in the Noth west of Nairobi on the previously undeveloped land seems to have triggered subsidence, for instance, the development of the Two Rivers Mall and the subsequent developments within its environs could be attributed to the subsidence at location g. The subsidence witnessed at the two airports (Wilson Airport and Moi Airbase, Eastleigh airports ) can be attributed to foundation settlement that is tied to the consolidation of the filling material during construction and the loading effect during take-off and landing.…”

Section: Other Causative Agentssupporting

confidence: 74%

“…Similarly, an exponential increase in the waste with no alternative sites for water disposal has put pressure on the existing landfill, Dandora landfill, with tones of waste being disposed of daily (Hudson Hill, 2020;Haregu et al, 2017). Aside from the obvious environmental hazards, the addition of 5000 tonnes of waste daily is a recipe for land subsidence to the adjacent properties mechanical and biochemical processes at the landfill is a recipe for land subsidence for the adjacent properties (Gasperini et al, 2014;Baek et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Determination and Analysis of Land Deformation in Nairobi City County Based ALOS/Palsar and Sentinel-1 InSAR Processing

Kirui¹,

Oiro²,

Waithaka³

et al. 2021

Preprint

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Nairobi city is one of the fastest-growing cities in Africa. The rapid development has put pressure on the existing resources such as water resulting in overexploitation of groundwater. Nairobi had been reported to be susceptible to environmental hazards such as subsidence associated with the overexploitation of groundwater due to depletion of its aquifer. However, the subsidence has not been empirically quantified because Kenya lacks a functioning deformation monitoring framework. Through the use of InSAR, land subsidence in Nairobi was spatially quantified for the first time that confirms fears of subsidence as suggested by previous hydrogeological studies. We observed an increase in the spatial extends and the rates of subsidence, the highest rate of subsidence being approx. 62mm/yr located in the largest deforming area West of Nairobi. The deformation can majorly be linked to overexploitation of groundwater as it matches with regions with the highest rate of decline of groundwater levels. However, there exist also subsidence linked to rapid development. Given the significance of Nairobi to the Kenyan economy, the ripple effects of subsidence could be devastating and measures need to be taken to mitigate them. The deformation map provides an important tool for planning mitigation measures to prevent further deformation such as regulating the drilling of boreholes, planning of construction of buildings and transport networks, and locations for the observation wells for monitoring groundwater levels. It also reinforces the need for updating the geodetic network that has been rendered unreliable due to land subsidence.

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Section: Other Causative Agentssupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Determination and Analysis of Land Deformation in Nairobi City County Based ALOS/Palsar and Sentinel-1 InSAR Processing

Kirui¹,

Oiro²,

Waithaka³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The former refers to the compression of air-filled pores within the waste products, a continuous and long-term process caused by the compression of waste products, whereas the latter (biochemical processes) refer to the decomposition of organic matter [51]. Similar observations (subsidence over landfills) were reported from many landfills [52,53] worldwide.…”

Section: Subsidence In Umm Al-afai Landfillsupporting

confidence: 55%

Countrywide Monitoring of Ground Deformation Using InSAR Time Series: A Case Study from Qatar

et al. 2021

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Over the past few decades the country of Qatar has been one of the fastest growing economies in the Middle East; it has witnessed a rapid increase in its population, growth of its urban centers, and development of its natural resources. These anthropogenic activities compounded with natural forcings (e.g., climate change) will most likely introduce environmental effects that should be assessed. In this manuscript, we identify and assess one of these effects, namely, ground deformation over the entire country of Qatar. We use the Small Baseline Subset (SBAS) InSAR time series approach in conjunction with ALOS Palsar-1 (Jan. 2007 to Mar. 2011) and Sentinel-1 (Mar. 2017 to Dec. 2019) synthetic aperture radar (SAR) datasets to assess ground deformation and conduct spatial and temporal correlations between the observed deformation with relevant datasets to identify the controlling factors. The findings indicate: (1) the deformation products revealed areas of subsidence and uplift with high vertical velocities of up to 35 mm/yr; (2) the deformation rates were consistent with those extracted from the continuously operating reference GPS stations of Qatar; (3) many inland and coastal sabkhas (salt flats) showed evidence for uplift (up to 35 mm/yr) due to the continuous evaporation of the saline waters within the sabkhas and the deposition of the evaporites in the surficial and near-surficial sabkha sediments; (4) the increased precipitation during Sentinel-1 period compared to the ALOS Palsar-1 period led to a rise in groundwater levels and an increase in the areas occupied by surface water within the sabkhas, which in turn increased the rate of deposition of the evaporitic sediments; (5) high subsidence rates (up to 14 mm/yr) were detected over landfills and dumpsites, caused by mechanical compaction and biochemical processes; and (6) the deformation rates over areas surrounding known sinkhole locations were low (+/−2 mm/yr). We suggest that this study can pave the way to similar countrywide studies over the remaining Arabian Peninsula countries and to the development of a ground motion monitoring system for the entire Arabian Peninsula.

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“…The temporal and perpendicular baselines of the interferometric pairs are summarized in Table 2. An L-band ALOS PALSAR-1 image has a higher transmittance to objects due to a longer wavelength, so that it is able to preserve a higher coherence in forest areas than the X and C-band images [32]. Therefore, the L-band image is more suitable to classify the forest vertical structure rather than the X and C-band images.…”

Section: Study Area and Datasetmentioning

confidence: 99%

Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network

Lee

Baek

et al. 2020

Remote Sensing

Self Cite

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Recently, due to the acceleration of global warming, an accurate understanding and management of forest carbon stocks, such as forest aboveground biomass, has become very important. The vertical structure of the forest, which is the internal structure of the forest, was mainly investigated by field surveys that are labor intensive. Recently, remote sensing techniques have been actively used to explore large and inaccessible areas. In addition, machine learning techniques that could classify and analyze large amounts of data are being used in various fields. Thus, this study aims to analyze the forest vertical structure (number of tree layers) to estimate forest aboveground biomass in Jeju Island from optical and radar satellite images using artificial neural networks (ANN). For this purpose, the eight input neurons of the forest related layers, based on remote sensing data, were prepared: normalized difference vegetation index (NDVI), normalized difference water index (NDWI), NDVI texture, NDWI texture, average canopy height, standard deviation canopy height and two types of coherence maps were created using the Kompsat-3 optical image, L-band ALOS PALSAR-1 radar images, digital surface model (DSM), and digital terrain model (DTM). The forest vertical structure data, based on field surveys, was divided into the training/validation and test data and the hyper-parameters of ANN were trained using the training/validation data. The forest vertical classification result from ANN was evaluated by comparison to the test data. It showed about a 65.7% overall accuracy based on the error matrix. This result shows that the forest vertical structure map can be effectively generated from optical and radar satellite images and existing DEM and DTM using the ANN approach, especially for national scale mapping.

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Ground subsidence observation of solid waste landfill park using multi-temporal radar interferometry

Cited by 17 publications

References 18 publications

Determination and Analysis of Land Deformation in Nairobi City County Based ALOS/Palsar and Sentinel-1 InSAR Processing

Determination and Analysis of Land Deformation in Nairobi City County Based ALOS/Palsar and Sentinel-1 InSAR Processing

Countrywide Monitoring of Ground Deformation Using InSAR Time Series: A Case Study from Qatar

Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network

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