scholarly journals Characterization and Mapping of Fuel Types for the Mediterranean Ecosystems of Pollino National Park in Southern Italy by Using Hyperspectral MIVIS Data

2006 ◽  
Vol 10 (13) ◽  
pp. 1-11 ◽  
Author(s):  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Stefano Pignatti
Keyword(s):  
Remote Sensing ◽  
National Park ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Mediterranean Ecosystems ◽  
Remotely Sensed ◽  
Hyperspectral Data ◽  
Test Area ◽  
Sensing Data

Abstract The characterization and mapping of fuel types is one of the most important factors that should be taken into consideration for wildland fire prevention and prefire planning. This research aims to investigate the usefulness of hyperspectral data to recognize and map fuel types in order to ascertain how well remote sensing data can provide an exhaustive classification of fuel properties. For this purpose airborne hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired in November 1998 have been analyzed for a test area of 60 km2 selected inside Pollino National Park in the south of Italy. Fieldwork fuel-type recognitions, performed at the same time as remote sensing data acquisition, were used as a ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three steps: 1) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; 2) model construction for the spectral characterization and mapping of fuel types based on a maximum likelihood (ML) classification algorithm; and 3) accuracy assessment for the performance evaluation based on the comparison of MIVIS-based results with ground truth. Results from our analysis showed that the use of remotely sensed data at high spatial and spectral resolution provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 90%.

scholarly journals Classification of Remote Sensing Data With Morphological Attributes Profiles: A decade of advances

2021 ◽  
pp. 0-0
Author(s):  
Deise Santana Maia ◽  
Minh-Tan Pham ◽  
Erchan Aptoula ◽  
Florent Guiotte ◽  
Sebastien Lefevre
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Morphological Attributes ◽  
Sensing Data

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

scholarly journals Groundwater Potential Zones in Relation to Catchment Condition in Orenburg, Russia

2019 ◽  
pp. 60-65
Author(s):  
K Choudhary ◽  
M S Boori ◽  
A Kupriyanov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Groundwater Potential ◽  
Groundwater Potential Zone ◽  
Potential Zone ◽  
Ground Truth Data ◽  
Groundwater Availability ◽  
Sensing Data

The main objective of this study was to detect groundwater availability for agriculture in the Orenburg, Russia. Remote sensing data (RS) and geographic information system (GIS) were used to locate potential zones for groundwater in Orenburg. Diverse maps such as a base map, geomorphological, geological structural, lithology, drainage, slope, land use/cover and groundwater potential zone were prepared using the satellite remote sensing data, ground truth data, and secondary data. ArcGIS software was utilized to manipulate these data sets. The groundwater availability of the study was classified into different classes such as very high, high, moderate, low and very low based on its hydro-geomorphological conditions. The land use/cover map was prepared using a digital classification technique with the limited ground truth for mapping irrigated areas in the Orenburg, Russia.

scholarly journals Remote Sensing Approaches and Related Techniques to Map and Study Landslides

2020 ◽  
Author(s):  
Ram L. Ray ◽  
Maurizio Lazzari ◽  
Tolulope Olutimehin
Keyword(s):  
Remote Sensing ◽  
Hazard Assessment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Landslide Monitoring ◽  
In Situ Observation ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Stability Model

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales.

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