scholarly journals Analysis of Shoreline Shift using Satellite Imagery near Makassar City

2020 ◽  
Vol 5 (3) ◽  
pp. 133-138
Author(s):  
Rian Amukti ◽  
Arif Seno Adji ◽  
Syamsuri Ruslan
Keyword(s):  
Remote Sensing ◽  
Central Government ◽  
Landsat Imagery ◽  
Coastal Region ◽  
Anthropogenic Factors ◽  
Temporal Data ◽  
City Region ◽  
Sensing Technology ◽  
Multi Temporal ◽  
Remote Sensing Techniques

Shoreline shift have occurred in the Coastal region of Makassar City in recent years due to abrasion and accretion. Spatial temporal feature extraction of the Makassar City Region has been carried out using remote sensing techniques  withRadiometri,  Geometric Corrections and Composite Imagein the Landsat image dataset in 2009 and 2019. This study aims to analyze shoreline shift near Makassar City with remote sensing technology using Landsat imagery data, based on multi-temporal data with visual and digital analysis techniques between 2009 and 2019. This research contributes to local and central government as baseline data (data base) in making decisions for handling coastal areas. The results showed that the length of the Makassar City coastline without including the coastline length of the islands separated from land in a row that is equal to 37.79 km in 2009. While in 2019 there was a significant change that is 49.82 km. This shows the addition of a coastline of 12.03 km in the span of 10 years. These changes are mainly caused by anthropogenic factors, namely the construction of the pier / port and the reclamation and hydro-oceanographic factors, namely waves, currents and tides.

scholarly journals Remote Sensing in Predicting Development Patterns of Built-up Land

2021 ◽  
Vol 2 (2) ◽  
pp. 56-64
Author(s):  
Iqbal Eko Noviandi ◽  
Ramadhan Alvien Hanif ◽  
Hasanah Rahma Nur ◽  
Nandi
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Population Growth Rate ◽  
Landsat Imagery ◽  
Temporal Analysis ◽  
West Java ◽  
Development Patterns ◽  
Human Needs ◽  
Multi Temporal ◽  
Total Growth

Indonesia is a developing country whose construction and development are centered on the island of Java, especially in West Java Province. Sukabumi City is one of the areas in West Java. The development of urban areas is expanding due to various human needs to carry out the construction of buildings. Remote sensing that can be used to store developments with multi-temporal analysis with materials is Landsat imagery from 2001 to 2020. The method used is the Normalized Difference Built-up Index (NDBI). The purpose of this study is to map the development of the built-up land from year to year and predict the following years. The results of the research on the significant changes in built-up land occurred between 2013-2020, while from 2001 to 2013 there was not much change. Based on the research results, the total growth of built-up land was 1.539% per year with a population growth rate of 1.4% per year. The results of the analysis show that the area of ​​land built in Sukabumi City in 2028 is 186,7194 km2 or has increased by 21,2808 km2 since 2020.

Remote sensing of Australian wetlands: An evaluation of Landsat TM data for inventory and classification

1993 ◽  
Vol 44 (2) ◽  
pp. 235 ◽  
Author(s):  
RM Johnston ◽  
MM Barson
Keyword(s):  
Remote Sensing ◽  
Water Regime ◽  
Landsat Tm ◽  
Classification Methods ◽  
Vegetation Types ◽  
Infrared Band ◽  
South Wales ◽  
Multi Temporal ◽  
Density Slicing ◽  
Remote Sensing Techniques

This study aimed to develop simple remote-sensing techniques suitable for mapping and monitoring wetlands, using Landsat TM imagery of inland wetland sites in Victoria and New South Wales. A range of classification methods was examined in attempts to map the location and extent of wetlands and their vegetation types. Multi-temporal imagery (winter/spring and summer) was used to display seasonal variability in water regime and vegetation status. Simple density slicing of the mid-infrared band (TM5) from imagery taken during wet conditions was useful for mapping the location and extent of inundated areas. None of the classification methods tested reproduced field maps of dominant vegetation species; however, density slicing of multi-temporal imagery produced classes based on seasonal variation in water regime and vegetation status that are useful for reconnaissance mapping and for examining variability in previously mapped units. Satellite imagery is unlikely to replace aerial photography for detailed mapping of wetland vegetation types, particularly where ecological gradients are steep, as in many riverine systems. However, it has much to offer in monitoring changes in water regime and in reconnaissance mapping at regional scales.

Quantifying Urban Sprawl With Spatial Autocorrelation Techniques Using Multi-Temporal Satellite Data

2019 ◽  
pp. 1624-1644
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Data Sets ◽  
Temporal Data ◽  
Landsat Images ◽  
Urban Settlements ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

Integration of Satellite Remote Sensing Techniques and Landscape Metrics to Characterize Land Cover Change and Dynamics

2013 ◽  
pp. 228-244
Author(s):  
Carmelo Riccardo Fichera ◽  
Giuseppe Modica ◽  
Maurizio Pollino
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Landscape Metrics ◽  
Landsat Images ◽  
Aerial Photos ◽  
Multi Temporal ◽  
Urban Rural ◽  
Remote Sensing Techniques

One of the most relevant applications of Remote Sensing (RS) techniques is related to the analysis and the characterization of Land Cover (LC) and its change, very useful to efficiently undertake land planning and management policies. Here, a case study is described, conducted in the area of Avellino (Southern Italy) by means of RS in combination with GIS and landscape metrics. A multi-temporal dataset of RS imagery has been used: aerial photos (1954, 1974, 1990), Landsat images (MSS 1975, TM 1985 and 1993, ETM+ 2004), and digital orthophotos (1994 and 2006). To characterize the dynamics of changes during a fifty year period (1954-2004), the approach has integrated temporal trend analysis and landscape metrics, focusing on the urban-rural gradient. Aerial photos and satellite images have been classified to obtain maps of LC changes, for fixed intervals: 1954-1985 and 1985-2004. LC pattern and its change are linked to both natural and social processes, whose driving role has been clearly demonstrated in the case analysed. In fact, after the disastrous Irpinia earthquake (1980), the local specific zoning laws and urban plans have significantly addressed landscape changes.

scholarly journals Mapping the Recovery Process of Vegetation Growth in the Copper Basin, Tennessee Using Remote Sensing Technology

GeoHazards ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 31-43
Author(s):  
Richard Blanton ◽  
A.K.M. Azad Hossain
Keyword(s):  
Remote Sensing ◽  
Landsat Imagery ◽  
Recovery Process ◽  
Mining District ◽  
Vegetation Growth ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Soil Destruction ◽  
Ducktown Mining District ◽  
Mining And Smelting

The Copper Basin (CB) of southeastern Tennessee, known as the Ducktown Mining District, is a classic example of forest and soil destruction due to extensive mining and smelting operations from the mid-1800s until 1987. The smelting operation released a sulfur dioxide by-product that formed sulfuric acid precipitation which, in combination with heavy logging, led to the complete denudation of all vegetation covering 130 km2 in CB. The area has since been successfully revegetated. This study used remote sensing technology to map the different episodes of this vegetation recovery process. A time series of Landsat imagery acquired from 1977 through 2017 at 10-year intervals was used to map and analyze the changes in vegetation cover in CB. These maps were used to generate a single thematic map indicating in which 10-year period each parcel of land was revegetated. Analysis shows that the extent of non-vegetated areas continuously decreased from about 38.5 to 2.5 km2 between 1977 and 2017. The greatest increase in vegetation regrowth occurred between 1987 and 1997, which was the period when all mining and smelting activities ceased. This research could be very useful to better understand the recovery process of areas affected by mining and smelting processes.

scholarly journals Mapping Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability

2020 ◽  
Vol 12 (10) ◽  
pp. 1620 ◽  
Author(s):  
Weichun Zhang ◽  
Hongbin Liu ◽  
Wei Wu ◽  
Linqing Zhan ◽  
Jing Wei
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Rice Paddy ◽  
Temporal Data ◽  
Model Transferability ◽  
Local Classification ◽  
Multi Temporal ◽  
Sentinel 2

Rice is an important agricultural crop in the Southwest Hilly Area, China, but there has been a lack of efficient and accurate monitoring methods in the region. Recently, convolutional neural networks (CNNs) have obtained considerable achievements in the remote sensing community. However, it has not been widely used in mapping a rice paddy, and most studies lack the comparison of classification effectiveness and efficiency between CNNs and other classic machine learning models and their transferability. This study aims to develop various machine learning classification models with remote sensing data for comparing the local accuracy of classifiers and evaluating the transferability of pretrained classifiers. Therefore, two types of experiments were designed: local classification experiments and model transferability experiments. These experiments were conducted using cloud-free Sentinel-2 multi-temporal data in Banan District and Zhongxian County, typical hilly areas of Southwestern China. A pure pixel extraction algorithm was designed based on land-use vector data and a Google Earth Online image. Four convolutional neural network (CNN) algorithms (one-dimensional (Conv-1D), two-dimensional (Conv-2D) and three-dimensional (Conv-3D_1 and Conv-3D_2) convolutional neural networks) were developed and compared with four widely used classifiers (random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP)). Recall, precision, overall accuracy (OA) and F1 score were applied to evaluate classification accuracy. The results showed that Conv-2D performed best in local classification experiments with OA of 93.14% and F1 score of 0.8552 in Banan District, OA of 92.53% and F1 score of 0.8399 in Zhongxian County. CNN-based models except Conv-1D provided more desirable performance than non-CNN classifiers. Besides, among the non-CNN classifiers, XGBoost received the best result with OA of 89.73% and F1 score of 0.7742 in Banan District, SVM received the best result with OA of 88.57% and F1 score of 0.7538 in Zhongxian County. In model transferability experiments, almost all CNN classifiers had low transferability. RF and XGBoost models have achieved acceptable F1 scores for transfer (RF = 0.6673 and 0.6469, XGBoost = 0.7171 and 0.6709, respectively).

scholarly journals Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3652 ◽  
Author(s):  
Chris Hacking ◽  
Nitesh Poona ◽  
Nicola Manzan ◽  
Carlos Poblete-Echeverría
Keyword(s):  
Remote Sensing ◽  
Volume Estimation ◽  
High Yield ◽  
Yield Estimation ◽  
Laboratory Conditions ◽  
Managerial Decisions ◽  
Sensing Technology ◽  
Maximum Quantity ◽  
Plant Level ◽  
Remote Sensing Techniques

Vineyard yield estimation provides the winegrower with insightful information regarding the expected yield, facilitating managerial decisions to achieve maximum quantity and quality and assisting the winery with logistics. The use of proximal remote sensing technology and techniques for yield estimation has produced limited success within viticulture. In this study, 2-D RGB and 3-D RGB-D (Kinect sensor) imagery were investigated for yield estimation in a vertical shoot positioned (VSP) vineyard. Three experiments were implemented, including two measurement levels and two canopy treatments. The RGB imagery (bunch- and plant-level) underwent image segmentation before the fruit area was estimated using a calibrated pixel area. RGB-D imagery captured at bunch-level (mesh) and plant-level (point cloud) was reconstructed for fruit volume estimation. The RGB and RGB-D measurements utilised cross-validation to determine fruit mass, which was subsequently used for yield estimation. Experiment one’s (laboratory conditions) bunch-level results achieved a high yield estimation agreement with RGB-D imagery (r2 = 0.950), which outperformed RGB imagery (r2 = 0.889). Both RGB and RGB-D performed similarly in experiment two (bunch-level), while RGB outperformed RGB-D in experiment three (plant-level). The RGB-D sensor (Kinect) is suited to ideal laboratory conditions, while the robust RGB methodology is suitable for both laboratory and in-situ yield estimation.

Remote sensing of crop residue cover using multi-temporal Landsat imagery

2012 ◽  
Vol 117 ◽  
pp. 177-183 ◽  
Author(s):  
Baojuan Zheng ◽  
James B. Campbell ◽  
Kirsten M. de Beurs
Keyword(s):  
Remote Sensing ◽  
Crop Residue ◽  
Landsat Imagery ◽  
Multi Temporal

scholarly journals Bridge monitoring and deformation time-series analysis by high-resolution Multi-Temporal SAR Interferometry (MT-InSAR) 

2021 ◽  
Author(s):  
Valerio Gagliardi ◽  
Luca Bianchini Ciampoli ◽  
Amir Alani ◽  
Fabio Tosti ◽  
Andrea Benedetto
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
High Resolution ◽  
Health Monitoring ◽  
Interferometric Synthetic Aperture Radar ◽  
Space Agency ◽  
Multi Temporal ◽  
Italian Space Agency ◽  
Remote Sensing Techniques ◽  
Non Destructive

<p>Multi-temporal Interferometric Synthetic Aperture Radar (InSAR) is a space-borne monitoring technique capable of detecting cumulative surface displacements with millimeter accuracy in the Line of Sight (LOS) of the radar sensor [1-3]. Several developments in the processing methods and the increasing availability of SAR datasets from different satellite missions, have proven the viability of this technique in the near-real-time assessment of bridges and the health monitoring of transport infrastructures [2-4].</p><p>This research aims to demonstrate the potential of satellite-based remote sensing techniques as an innovative health-monitoring method for structural assessment of bridges and the prevention of damages by structural subsidence, using high-resolution SAR datasets integrated with complementary Ground-Based (GB) Non-Destructive Testing (NDT) techniques. To this purpose, high-resolution COSMO‐SkyMed (CSK) products provided by the Italian Space Agency (ASI) were acquired and processed.</p><p>In particular, a multi-temporal InSAR analysis was developed to identify and monitor the structural displacements of the Rochester Bridge, located in Rochester, Kent, UK. To this extent, a clustering operation is realised to collect the identified Persistent Scatterers (PSs) over the structural elements of the bridge (i.e., bridge piers and arcs). Furthermore, several sub-clusters with a comparable deformation trend were identified and located over the bridge elements. This operation paves the way for an automatisation of the process through a Machine Learning (ML) clustering algorithms to assign each PS data-point to specific groups, based on the structural element type and the trend of seasonal deformation time-series.</p><p>The outcomes of this study demonstrate how multi-temporal InSAR remote sensing techniques can be synergistically applied to complement non-destructive ground-based analyses, paving the way for future integrated methodologies in the monitoring of infrastructure assets.</p><p><strong>Acknowledgments: </strong>The authors want to acknowledge the Italian Space Agency (ASI) for providing the COSMO-SkyMed Products® (©ASI, 2017-2019),  in the framework of the ASI-Open Call Project “MoTIB, ID 742” accepted by ASI. In addition, the authors would like to acknowledge the Rochester Bridge Trust for facilitating and supporting this research. This research is supported by the Italian Ministry of Education, University and Research under the National Project “EXTRA TN”, PRIN 2017, Prot. 20179BP4SM.</p><p><strong>References</strong></p><p>[1] Alani A. M., Tosti F., Bianchini Ciampoli L., Gagliardi V., Benedetto A., Integration of GPR and InSAR methods for the health monitoring of masonry arch bridges. NDT&E International. (2020)</p><p>[2] Gagliardi V., Bianchini Ciampoli L., D'Amico F., Alani A. M., Tosti F., Battagliere M. L., Benedetto A., Bridge monitoring and assessment by high-resolution satellite remote sensing technologies, Proc. SPIE 11525, SPIE Future Sensing Technologies. 2020. doi: 10.1117/12.2579700</p><p>[3] Selvakumaran, S., Plank, S., Geiß, C., Rossi, C., Middleton, C. (2018). Remote monitoring to predict bridge scour failure using Interferometric Synthetic Aperture Radar (InSAR) stacking techniques, Int. J. .Appl. Earth Obs. and Geoinf. 73, 463-470.</p><p>[4] Qin X, Liao M., Zhang L., & Yang M., Structural Health and Stability Assessment of High-Speed Railways via Thermal Dilation Mapping with Time-Series InSAR Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</p>

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