Analysis of shoreline changes along the coast of Tiruvallur district, Tamil Nadu, India

Shore line change is considered as one of the most dynamic processes, which were mapped along the coast of Tiruvallur district by using topographic maps of 1976 and multi-temporal satellite images. The satellite images pertaining to 1988, 1991, 2006, 2010, 2013 and 2016 were used to extract the shorelines. It is important to map and monitor the HTL (High Tide Line) at frequent time intervals as the shoreline was demarcated by using visual interpretation technique from satellite images and topographic maps. Followed by this, an overlay analysis was performed to calculate areas of erosion and accretion in the study area. The results revealed that the coast of Tiruvallur district lost 603 ha and gained 630 ha due to erosion and accretion respectively. It was confirmed after the ground truth survey carried out in the study area. The high accretion of 178 ha was found nearby Pulicat Lake and low accretion of 19 ha was seen between Pulicat Lake and Kattupali Port. The high erosion area was found along the Pulicat Lake, Kattupali and Ennore ports, and Ennore creek mouth and southern Ennore such as Periya Kuppam, Chinna Kuppam, Kasi Koil Kuppam, and Thyagarajapuram. It may be concluded that the coastal erosion and accretion in the study area were mainly caused by anthropogenic and natural factors, which altered the coastal environment.

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Analysis of shoreline changes along the coast of Tiruvallur district, Tamil Nadu, India

Journal of Geography and Cartography ◽

10.24294/jgc.v1i3.764 ◽

2021 ◽

Vol 4 (2) ◽

Author(s):

K. Jayakumar

Keyword(s):

Tamil Nadu ◽

Satellite Images ◽

High Tide ◽

Ground Truth ◽

Shoreline Change ◽

Topographic Maps ◽

Time Interval ◽

Visual Interpretation ◽

Pulicat Lake ◽

Erosion And Accretion

Shoreline change is considered as one of the most dynamic process, which was mapped along the coast of Tiruvallur district by using topographic maps of 1976 and multi-temporal satellite images. The satellite images were pertaining to 1988, 1991, 2006, 2010, 2013 and 2016 which were used to extract the shorelines. It is important to map and monitor the HTL (High Tide line) at frequent time interval as it considered as shoreline, which was demarcated by using visual interpretation technique from satellite images and topographic maps. Followed by this, an overlay analysis was performed to calculate areas of erosion and accretion in the study area. The results revealed that the coast of Tiruvallur district lost 603 ha and gained 630 ha due to erosion and accretion respectively. It was confirmed after the ground truth survey carried out in the study area. The high accretion of 178 ha was found nearby Pulicat Lake and low accretion of 19 ha seen between Pulicat lake to Kattupali port. The high erosion area was found along the Pulicat lake, Kattupali and Ennore ports, and Ennore creek mouth and south of Ennore such as Periya Kuppam, Chinna Kuppam, Kasi Koil Kuppam, and Thyagarajapuram. It may be concluded that the main causes of coastal erosion and accretion in the study area are anthropogenic and natural factors, which alter the coastal environment.

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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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Genus-Physiognomy-Ecosystem Map with 88 Legends Produced at 10m-resolution First-time in a Country Scale through Machine Learning of Multi-temporal Satellite Images

10.20944/preprints202201.0123.v1 ◽

2022 ◽

Author(s):

Ram C. Sharma ◽

Keitarou Hara

Keyword(s):

Machine Learning ◽

Satellite Images ◽

Graphics Processing Unit ◽

Ground Truth ◽

Training Data ◽

Gradient Boosting ◽

Processing Unit ◽

Spectral Bands ◽

Multi Temporal ◽

First Time

This research introduces Genus-Physiognomy-Ecosystem (GPE) mapping at a prefecture level through machine learning of multi-spectral and multi-temporal satellite images at 10m spatial resolution, and later integration of prefecture wise maps into country scale for dealing with 88 GPE types to be classified from a large size of training data involved in the research effectively. This research was made possible by harnessing entire archives of Level-2A product, Bottom of Atmosphere reflectance images collected by MultiSpectral Instruments onboard a constellation of two polar-orbiting Sentinel-2 mission satellites. The satellite images were pre-processed for cloud masking and monthly median composite images consisting of 10 multi-spectral bands and 7 spectral indexes were generated. The ground truth labels were extracted from extant vegetation survey maps by implementing systematic stratified sampling approach and noisy labels were dropped out for preparing a reliable ground truth database. Graphics Processing Unit (GPU) implementation of Gradient Boosting Decision Trees (GBDT) classifier was employed for classification of 88 GPE types from 204 satellite features. The classification accuracy computed with 25% test data varied from 65-81% in terms of F1-score across 48 prefectural regions. This research produced seamless maps of 88 GPE types first time at a country scale with an average 72% F1-score.

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Criteria for the optimal selection of remote sensing images to map event landslides

10.5194/nhess-2017-111 ◽

2017 ◽

Author(s):

Federica Fiorucci ◽

Daniele Giordan ◽

Michele Santangelo ◽

Furio Dutto ◽

Mauro Rossi ◽

...

Keyword(s):

Satellite Image ◽

Central Italy ◽

Ground Truth ◽

Visual Interpretation ◽

Differential Gps ◽

Gps Survey ◽

Erdas Imagine ◽

Multi Temporal ◽

Colour Composite ◽

Error Index

Abstract. We executed an experiment to determine the effects of image characteristics on event landslide mapping. In the experiment, we compared eight maps of the same landslide, the Assignano landslide, in Umbria, central Italy. Six maps were obtained through the expert visual interpretation of monoscopic and pseudo-stereoscopic (2.5D), ultra-resolution (3 × 3 cm) images taken on 14 April 2014 by a Canon EOS M photographic camera flown by an CarbonCore 950 hexacopter over the landslide, and of monoscopic and stereoscopic, true-colour and false-colour-composite, 1.84 × 1.84 m resolution images taken by the WorldView-2 satellite also on 14 April 2014. The seventh map was prepared through a reconnaissance field survey aided by a pre-event satellite image taken on 8 July 2013, available on Goggle EarthTM, and by colour photographs taken in the field with a hand-held camera. The images were interpreted visually by an expert geomorphologist using the StereoMirrorTM hardware technology combined with the ERDAS IMAGINE® and Leica Photogrammetry Suite (LPS) software. The eighth map, which we considered our reference showing the ground truth, was obtained through a Real Time Kinematic differential GPS survey conducted by walking a GPS receiver along the landslide perimeter to capture geographic coordinates every about 5 m, with centimetre accuracy. The eight maps of the Assignano landslide were stored in a GIS, and compared adopting a pairwise approach. Results of the comparisons, quantified by the error index E, revealed that where the landslide signature was primarily photographical (in the landslide source and transport area) the best mapping results were obtained using the higher resolution images, and where the landslide signature was mainly morphometric (in the landslide deposit) the best results were obtained using the stereoscopic images. The ultra-resolution image proved very effective to map the landslide, with results comparable to those obtained using the stereoscopic satellite image. Conversely, the field-based reconnaissance mapping provided the poorest results, measured by large mapping errors, and confirmed the difficulty in preparing accurate landslide maps in the field. Albeit conducted on a single landslide, we maintain that our results are general, and provide useful information to decide on the optimal imagery for the production of event, seasonal and multi-temporal landslide inventory maps.

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Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images

Remote Sensing ◽

10.3390/rs11101155 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1155 ◽

Cited By ~ 27

Author(s):

Tatsuyuki Sagawa ◽

Yuta Yamashita ◽

Toshio Okumura ◽

Tsutomu Yamanokuchi

Keyword(s):

Machine Learning ◽

Shallow Water ◽

Satellite Images ◽

High Tide ◽

Depth Estimation ◽

Mapping Method ◽

Google Earth ◽

Landsat 8 ◽

Estimation Model ◽

Multi Temporal

Shallow water bathymetry is important for nautical navigation to avoid stranding, as well as for the scientific simulation of high tide and high waves in coastal areas. Although many studies have been conducted on satellite derived bathymetry (SDB), previously used methods basically require supervised data for analysis, and cannot be used to analyze areas that are unreachable by boat or airplane. In this study, a mapping method for shallow water bathymetry was developed, using random forest machine learning and multi-temporal satellite images to create a generalized depth estimation model. A total of 135 Landsat-8 images, and a large amount of training bathymetry data for five areas were analyzed with the Google Earth Engine. The accuracy of SDB was evaluated by comparison with reference bathymetry data. The root mean square error in the final estimated water depth in the five test areas was 1.41 m for depths of 0 to 20 m. The SDB creation system developed in this study is expected to be applicable in various shallow water regions under highly transparent conditions.

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ESTIMATION OF COTTON AND MAIZE CROP AREA IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI-DATE SENTINEL-1A SAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-67-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 67-71

Author(s):

M. Ashmitha Nihar ◽

J. Mohammed Ahamed ◽

S. Pazhanivelan ◽

R. Kumaraperumal ◽

K. Ganesha Raj

Keyword(s):

Decision Tree ◽

Tamil Nadu ◽

Ground Truth ◽

Decision Tree Classifier ◽

Maize Crop ◽

Ground Truth Data ◽

Tree Classifier ◽

Multi Temporal ◽

Crop Classification ◽

Sar Data

<p><strong>Abstract.</strong> Crop classification is a key issue for agricultural monitoring using remote sensing techniques. Synthetic Aperture Radar (SAR) data has an advantage in crop classification because of its all-weather imaging capabilities. The objective of this study was to investigate the capability of SAR data for estimation of cotton and maize area in Perambalur district of Tamil Nadu. The multi-temporal Sentinel-1 SAR data was acquired from 2nd September, 2017 to 24th January, 2018. Both the Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarized data was used. Ground truth data collection was performed for cotton and maize during the vegetative, flowering and harvesting stages. Sixty per cent of the ground truth data were used for training and remaining forty per cent were utilized for validation. The temporal backscattering coefficient (&sigma;0) for cotton and maize were extracted using the training datasets.. The mean backscattering values for cotton crop during the entire cropping period had a range from &minus;11.729&thinsp;dB to &minus;8.827&thinsp;dB and from &minus;19.167&thinsp;dB to &minus;14.186 dB for VV and VH polarization respectively. For maize crop it ranged from &minus;11.248&thinsp;dB to &minus;8.878&thinsp;dB and from &minus;19.043 dB to &minus;14.753&thinsp;dB for VV and VH polarized data respectively. The Spectral Angle Mapper (SAM) and Decision Tree classifier (DT) methods were adopted for cotton and maize area estimation. SAM classified 73259 and 51489 hectares (ha) as cotton and maize respectively in VV polarization. DT classified the area of 61501 and 64530&thinsp;ha for cotton and maize respectively in VH polarization. The accuracy measures, such as overall accuracy, producer’s accuracy and user’s accuracy and kappa coefficient were estimated. SAM classifier exhibits the overall accuracy of 73.3% for VV Decision tree classifier reported the overall accuracy of 75.0% for VH. It is evident from the present study, that the multi-temporal Sentinel-1 SAR sensor can be well used for the discrimination of cotton and maize crops because of its high temporal resolution which captures the complete phenology of the crops during the cropping period.</p>

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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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