Land subsidence rebound detected via multi-temporal InSAR and ground truth data in Kalochori and Sindos regions, Northern Greece

Abstract. Land subsidence caused by overexploitation of aquifers manifests with an increasing frequency in several regions of Greece. The first signs of land subsidence have been identified since 1965 at the west side of Thessaloniki (broader Kalochori village region), in the form of a progressive marine invasion. Since then several areas have been investigated and proved to be affected mainly by the overexploitation of the aquifers. The Multidisciplinary nature of the subsidence mechanism combining the geological, hydrogeological and morphological setting of the areas with the human activities and the land use data makes their study complicated and require the intervention of multiple scientific specialties. Furthermore, at several sites, beside ground truth data, the land subsidence trends and extends have been also identified via multi-temporal InSAR techniques, such as PSI and SBAS techniques. Among the sites to be presented in the current paper is the wider area of Kalochori village in the eastern part of Thessaloniki plain, the east and west Thessaly plain in central Greece and the region extending to the west–southwest of the Amyntaio opencast coalmine in West Macedonia.

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Multi-Frequency and Multi-Temporal InSAR Analysis for Monitoring Land Subsidence, Aswan City, Egypt

10.5194/egusphere-egu21-292 ◽

2021 ◽

Author(s):

Noha Medhat ◽

Masa-yuki Yamamoto ◽

Gad El-Qady

Keyword(s):

Land Subsidence ◽

Urban Areas ◽

Ground Deformation ◽

Anthropogenic Impacts ◽

Ground Truth ◽

Subsurface Water ◽

Ground Truth Data ◽

Time Period ◽

Management Plans ◽

Multi Temporal

Land deformation due to natural and anthropogenic impacts considered to be one of the challenging environmental problems in the Aswan area located in the southern part of Egypt. Specifically, we applied multi-sensor analysis in order to record the slow rate of subsidence with a high spatial resolution of COSMO-SkyMed (X-band) and Sentinel-1 TOPSAR (C-band) scenes. We proposed multi-temporal DInSAR data analysis by means of ascending and descending orbit tracks during the recent time period of 2012-2017. The stacked DInSAR results reported the occurrence of land subsidence of active urban areas. A strong correlation between the ground truth data, ground leveling, and the estimated Line of Sight (LOS) displacement time series values are reached, assuming the ground deformation controlled by seasonal surface water loading, lithological units, and subsurface water activity. The detection of short-term displacement highlights the priority of groundwater management plans in the affected urban areas.

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Multi-Temporal Arable Land Monitoring in Arid Region of Northwest China Using a New Extraction Index

Sustainability ◽

10.3390/su13095274 ◽

2021 ◽

Vol 13 (9) ◽

pp. 5274

Author(s):

Xinyang Yu ◽

Younggu Her ◽

Xicun Zhu ◽

Changhe Lu ◽

Xuefei Li

Keyword(s):

Arable Land ◽

Ground Truth ◽

Northwest China ◽

Hexi Corridor ◽

Ground Truth Data ◽

Land Protection ◽

Promising Tool ◽

Study Results ◽

Multi Temporal ◽

The Mean

Development of a high-accuracy method to extract arable land using effective data sources is crucial to detect and monitor arable land dynamics, servicing land protection and sustainable development. In this study, a new arable land extraction index (ALEI) based on spectral analysis was proposed, examined by ground truth data, and then applied to the Hexi Corridor in northwest China. The arable land and its change patterns during 1990–2020 were extracted and identified using 40 Landsat TM/OLI images acquired in 1990, 2000, 2010, and 2020. The results demonstrated that the proposed method can distinguish arable land areas accurately, with the User’s (Producer’s) accuracy and overall accuracy (kappa coefficient) exceeding 0.90 (0.88) and 0.89 (0.87), respectively. The mean relative error calculated using field survey data obtained in 2012 and 2020 was 0.169 and 0.191, respectively, indicating the feasibility of the ALEI method in arable land extracting. The study found that arable land area in the Hexi Corridor was 13217.58 km2 in 2020, significantly increased by 25.33% compared to that in 1990. At 10-year intervals, the arable land experienced different change patterns. The study results indicate that ALEI index is a promising tool used to effectively extract arable land in the arid area.

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Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery

Remote Sensing ◽

10.3390/rs10121907 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1907 ◽

Cited By ~ 18

Author(s):

Luís Pádua ◽

Pedro Marques ◽

Jonáš Hruška ◽

Telmo Adão ◽

Emanuel Peres ◽

...

Keyword(s):

Accuracy Assessment ◽

Low Cost ◽

Ground Truth ◽

Volume Estimation ◽

Height Range ◽

Ground Truth Data ◽

Operational Technique ◽

Digital Elevation ◽

Multi Temporal ◽

Aerial Vehicle

This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.

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Vegetation Cover Change Detection and Assessment in Arid Environment Using Multi-temporal Remote Sensing images and Ecosystem Management Approach

10.5194/se-2016-31 ◽

2016 ◽

Cited By ~ 1

Author(s):

Anwar Abdelrahman Aly ◽

Abdulrasoul Mosa Al-Omran ◽

Abdulazeam Shahwan Sallam ◽

Mohammad Ibrahim Al-Wabel ◽

Mohammad Shayaa Al-Shayaa

Keyword(s):

Remote Sensing ◽

Ecosystem Management ◽

Vegetation Cover ◽

Secondary Data ◽

Ground Truth ◽

Arid Environment ◽

Management Approach ◽

Landsat Images ◽

Ground Truth Data ◽

Multi Temporal

Abstract. Vegetation cover (VC) changes detection is essential for a better understanding of the interactions and interrelationships between humans and their ecosystem. Remote sensing (RS) technology is one of the most beneficial tools to study spatial and temporal changes of VC. A case study has been conducted in the agro-ecosystem (AE) of Al-Kharj, in the centre of Saudi Arabia. Characteristics and dynamics of VC changes during a period of 26 years (1987–2013) were investigated. A multi-temporal set of images was processed using Landsat images; Landsat4 TM 1987, Landsat7 ETM+ 2000, and Landsat8 2013. The VC pattern and changes were linked to both natural and social processes to investigate the drivers responsible for the change. The analyses of the three satellite images concluded that the surface area of the VC increased by 107.4 % between 1987 and 2000, it was decreased by 27.5 % between years 2000 and 2013. The field study, review of secondary data and community problem diagnosis using the participatory rural appraisal (PRA) method suggested that the drivers for this change are the deterioration and salinization of both soil and water resources. Ground truth data indicated that the deteriorated soils in the eastern part of the Al-Kharj AE are frequently subjected to sand dune encroachment; while the south-western part is frequently subjected to soil and groundwater salinization. The groundwater in the western part of the ecosystem is highly saline, with a salinity ≥ 6 dS m−1. The ecosystem management approach applied in this study can be used to alike AE worldwide.

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Soil salinity assessment using temporal series of Sentinel2 satellite images in irrigated paddy fields of Western Africa

10.5194/egusphere-egu2020-11855 ◽

2020 ◽

Author(s):

Moussa Issaka ◽

Walter Christian ◽

Michot Didier ◽

Pichelin Pascal ◽

Nicolas Hervé ◽

...

Keyword(s):

Remote Sensing ◽

Soil Salinity ◽

Ground Truth ◽

Growing Season ◽

Bare Soil ◽

Atmospheric Effects ◽

Ground Truth Data ◽

Multi Temporal ◽

Salinity Index ◽

Niger River

Salinization and alkalinization are worldwide among the soil degradation threats in irrigated schemes affecting soil productivity. Niger River basin irrigated schemes in the Sahel arid zone are no exception (ONAHA, 2011). The use of remote sensing for identifying and evaluating the level of these phenomena is an interesting tool. The launching of the Sentinel2 satellite constellation (2015) brings new perspectives with high spectral and temporal resolutions images. The aim of this study was to develop a methodology for detection of salt-affected soils in this climatic condition.To achieve our goal, we used two types of data: remote sensing and ground truth data.Two complementary approaches were used: one by observing salinity on bare soil by the use of salinity index (SI) and the other by observing the indirect effects of salinity on the vegetation during eight (8) rice growth phases &#160;using vegetation index NDVI.Remote sensing data were acquired from multi temporal sentinel2 images over 4 years (from 11/12/2015 to 30/11/2019). One hundred and fifty seven (157) images were downloaded (one image each 5 days) and corrected from atmospheric effects and some bands resampled to 5 m using python software. The salinity and vegetation indices were calculated. NDVI index was calculated and NDVI integral between NDVI curve and the threshold of 0.21 NDVI calculated for the eight growing cycles.Ground truth data were collected in 2019 during the dry growing season (January &#8211; may 2019) from 24 calibration plots and 40 validation plots. One hundred and twenty (120) soil samples collected and analyzed for pH and electrical conductivity and finally forty six (46) biomass samples were collected, air dried and weighed for biomass yield and 46 grains samples collected for grain yield.NDVI integral proved to be good indicator for yield variations and could distinguish crops behavior according to the growing period. It also makes it possible to distinguish plots which were not cultivated or with weak growth due to strong constraints of which the main one is salinity. It showed also that the effect of salinity on growth differs according to the growing season and the possibility of managing irrigation. Bare soil analysis distinguishes fields with different salinity indexes despite the low number of dates for which bare soil can be observed.Ascending Hierarchical Classification (AHC) enabled to identify four classes of NDVI dynamics over time and bare soil salinity index. High saline soils according to direct soil measurements were related to the class characterized by high frequency of no-cultivation during the dry season and low NDVI integral during the wet season. Multi-temporal Sentinel2 images analysis enabled therefore to detect rice crop fields affected by salinity through its influence on crop behavior. This approach will be tested over the whole paddy schemes of the Niger River valley.

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Simple Method for Land-Cover Mapping by Combining Multi-Temporal Landsat ETM+ Images and Systematically Sampled Ground Truth Data : A Case Study in Japan

Journal of Forest Planning ◽

10.20659/jfp.18.1_77 ◽

2012 ◽

Vol 18 (1) ◽

pp. 77-85

Author(s):

Shinya Tanaka ◽

Tomoaki Takahashi ◽

Hideki Saito ◽

Yoshio Awaya ◽

Toshiro Iehara ◽

...

Keyword(s):

Land Cover ◽

Ground Truth ◽

Land Cover Mapping ◽

Simple Method ◽

Ground Truth Data ◽

Multi Temporal

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Monitoring of Chestnut Trees Using Machine Learning Techniques Applied to UAV-Based Multispectral Data

Remote Sensing ◽

10.3390/rs12183032 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3032

Author(s):

Luís Pádua ◽

Pedro Marques ◽

Luís Martins ◽

António Sousa ◽

Emanuel Peres ◽

...

Keyword(s):

Management Practices ◽

Abiotic Factors ◽

Vegetation Indices ◽

Ground Truth ◽

Temporal Analysis ◽

Machine Learning Techniques ◽

Ground Truth Data ◽

Multispectral Data ◽

Spectral Bands ◽

Multi Temporal

Phytosanitary conditions can hamper the normal development of trees and significantly impact their yield. The phytosanitary condition of chestnut stands is usually evaluated by sampling trees followed by a statistical extrapolation process, making it a challenging task, as it is labor-intensive and requires skill. In this study, a novel methodology that enables multi-temporal analysis of chestnut stands using multispectral imagery acquired from unmanned aerial vehicles is presented. Data were collected in different flight campaigns along with field surveys to identify the phytosanitary issues affecting each tree. A random forest classifier was trained with sections of each tree crown using vegetation indices and spectral bands. These were first categorized into two classes: (i) absence or (ii) presence of phytosanitary issues. Subsequently, the class with phytosanitary issues was used to identify and classify either biotic or abiotic factors. The comparison between the classification results, obtained by the presented methodology, with ground-truth data, allowed us to conclude that phytosanitary problems were detected with an accuracy rate between 86% and 91%. As for determining the specific phytosanitary issue, rates between 80% and 85% were achieved. Higher accuracy rates were attained in the last flight campaigns, the stage when symptoms are more prevalent. The proposed methodology proved to be effective in automatically detecting and classifying phytosanitary issues in chestnut trees throughout the growing season. Moreover, it is also able to identify decline or expansion situations. It may be of help as part of decision support systems that further improve on the efficient and sustainable management practices of chestnut stands.

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Forest and Industrial Plantation Mapping with Multi-Temporal Dual Polarization by ALOS PALSAR Data In Riau Province

International Journal of Environment and Geosciences ◽

10.24843/ijeg.2018.v02.i01.p04 ◽

2018 ◽

Vol 2 (1) ◽

pp. 35

Author(s):

Elis Molidena ◽

Takahiro Osawa ◽

Putu Gede Ardhana ◽

Abd. Rahman As-syakur

Keyword(s):

Land Use ◽

Ground Truth ◽

Temporal Analysis ◽

Alos Palsar ◽

Ground Truth Data ◽

Multi Temporal ◽

Industrial Plantation ◽

Changes Detection ◽

Accuracy Result ◽

Color Composite

Backscattering characteristics of land use has been analyzed using ALOS PALSAR data. The purpose of this research are mapping of land use by five categories such as forest, acacia, oil palm, open area and water, and to identify the changes of environmental. Analysis Pixel-by-pixel average of ALOS PALSAR level 1.5 backscattering used from five of category land use was to estimate the spectral characteristic of each object in difference HH and HV polarization. Ground truth data was taken from 169 locations which used for classification, 119 locations and 50 locations used for validation. Two different times of ALOS PALSAR level 1.0 2009 and 2010 data, was used for changes detection by multi temporal color composite combination. The accuracy result for classification map shows 62% of ground truth database, and multi temporal analysis showed the possibility of changes.

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Sentinel 1A SAR Backscattering Signature of Maize and Cotton Crops

Madras Agricultural Journal ◽

10.29321/maj.01.000398 ◽

2017 ◽

Vol 104 (.1-.4) ◽

Author(s):

Kumaraperumal R ◽

◽

Shama M ◽

Balaji Kannan ◽

Ragunath K P ◽

...

Keyword(s):

Tamil Nadu ◽

Ground Truth ◽

Backscattering Coefficient ◽

Ground Truth Data ◽

Crop Monitoring ◽

Multi Temporal ◽

The Mean ◽

Sar Data ◽

Remote Sensing Techniques ◽

Crop Discrimination

Crop discrimination is a key issue for agricultural monitoring using remote sensing techniques. Synthetic Aperture Radar (SAR) data are advantageous for crop monitoring and classification because of their all-weather imaging capabilities. The multi-temporal Sentinel 1A SAR data was acquired from 08th August, 2015 to 23rd January, 2016 at 12 days interval covering the extent of Perambalur district of Tamil Nadu. Both the Vertical - Vertical (VV) and Vertical-Horizontal (VH) polarized data are compared. The ground truth data collection was performed for cotton and maize during the vegetative, flowering and harvesting stages. The temporal backscattering coefficient (σ0 ) for cotton and maize are extracted using the training datasets. The mean backscattering values for cotton during the entire cropping period ranges from -10.58 dB to -6.28 dB and -20.59 dB to -14.53 dB for VV and VH polarized data respectively, and for maize it ranges from -11.08 dB to -7.07 dB and -19.85 dB to -14.14 dB for VV and VH polarized data respectively.

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