scholarly journals Forest Fire Mapping using Normalized Burned Ratio and Cloud Computing to Calculate the Losses Incurred in Mount Lawu, Magetan Regency

2021 ◽  
Vol 936 (1) ◽  
pp. 012002
Author(s):  
Bangun Muljo Sukojo ◽  
Adinda Sitaresmi Putri Arimurti
Keyword(s):  
Cloud Computing ◽  
Vegetation Index ◽  
Google Earth ◽  
Total Loss ◽  
Data Sets ◽  
High Data ◽  
Sensing Technology ◽  
Calculation Results ◽  
The Government ◽  
Applications Of Machine Learning

Abstract Forest and land fires in Indonesia are one of the most common disasters. Over time, Indonesia’s forests have experienced a decline, one of which is the result of forest and land fires. Forest and land fires can occur due to temperatures in the dry season that continue to increase. Utilization of remote sensing technology can be used for mapping forest and fire areas, and can also be used as a reference for the government to carry out reforestation or reforestation processes in fire areas. The use of Multispectral Sensors in Sentinel 2 satellite imagery can be used to produce this research. This research takes a case study on Mount Lawu, Magetan Regency. Mount Lawu is one of the climbing tourist destinations that almost every year experiences forest and land fires. And the use of the Google Earth Engine platform that supports cloud computing to facilitate this research in accessing high data sources to process large geospatial data sets. For processing satellite images using the Normalized Burned Ratio (NBR) vegetation index processing algorithm. This research was conducted in 2018 and 2019. By using the Random Forest method, which is one of the applications of Machine Learning, it will produce several accurate results from the calculation results. The results of the calculation of the area of fire in 2018 were 284.88 hectares with a total loss of Rp 42,732,000.00 and in 2019 it was 146.03 hectares with a total loss of Rp 21,904,500. The results of this study are displayed on GEE Apps so that users can directly access the resulting data.

scholarly journals Multi-Year Mapping of Disturbance and Reclamation Patterns over Tronox’s Hillendale Mine, South Africa with DBEST and Google Earth Engine

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 760
Author(s):  
Sifiso Xulu ◽  
Philani T. Phungula ◽  
Nkanyiso Mbatha ◽  
Inocent Moyo
Keyword(s):  
South Africa ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Abandoned Mines ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Petroleum Resources ◽  
Google Earth Engine ◽  
The Government

This study was devised to examine the pattern of disturbance and reclamation by Tronox, which instigated a closure process for its Hillendale mine site in South Africa, where they recovered zirconium- and titanium-bearing minerals from 2001 to 2013. Restoring mined-out areas is of great importance in South Africa, with its ominous record of almost 6000 abandoned mines since the 1860s. In 2002, the government enacted the Mineral and Petroleum Resources Development Act (No. 28 of 2002) to enforce extracting companies to restore mined-out areas before pursuing closure permits. Thus, the trajectory of the Hillendale mine remains unstudied despite advances in the satellite remote sensing technology that is widely used in this field. Here, we retrieved a collection of Landsat-derived normalized difference vegetation index (NDVI) within the Google Earth Engine and applied the Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to examine the progress of vegetation transformation over the Hillendale mine between 2001 and 2019. Our results showed key breakpoints in NDVI, a drop from 2001, reaching the lowest point in 2009–2011, with a marked recovery pattern after 2013 when the restoration program started. We also validated our results using a random forests strategy that separated vegetated and non-vegetated areas with an accuracy exceeding 78%. Overall, our findings are expected to encourage users to replicate this affordable application, particularly in emerging countries with similar cases.

scholarly journals Multi-temporal Crop Type and Field Boundary Classification with Google Earth Engine

2020 ◽  
Author(s):  
Michael Marszalek ◽  
Maximilian Lösch ◽  
Marco Körner ◽  
Urs Schmidhalter
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Federal State ◽  
Support Vector ◽  
Data Sets ◽  
Field Boundary ◽  
Data Set ◽  
Crop Type ◽  
Normalised Difference Vegetation Index ◽  
Boundary Classification

Crop type and field boundary mapping enable cost-efficient crop management on the field scale and serve as the basis for yield forecasts. Our study uses a data set with crop types and corresponding field borders from the federal state of Bavaria, Germany, as documented by farmers from 2016 to 2018. The study classified corn, winter wheat, barley, sugar beet, potato, and rapeseed as the main crops grown in Upper Bavaria. Corresponding Sentinel-2 data sets include the normalised difference vegetation index (NDVI) and raw band data from 2016 to 2018 for each selected field. The influences of clouds, raw bands, and NDVI on crop type classification are analysed, and the classification algorithms, i.e., support vector machine (SVM) and random forest (RF), are compared. Field boundary detection and extraction are based on non-iterative clustering and a newly developed procedure based on Canny edge detection. The results emphasise the application of Sentinel’s raw bands (B1–B12) and RF, which outperforms SVM with an accuracy of up to 94%. Furthermore, we forecast data for an unknown year, which slightly reduces the classification accuracy. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

Developing City-Wide Hurricane Impact Maps using Real-Life Data on Infrastructure, Vegetation and Weather

2020 ◽  
pp. 036119812097271
Author(s):  
Mingyang Chen ◽  
Alican Karaer ◽  
Eren Erman Ozguven ◽  
Tarek Abichou ◽  
Reza Arghandeh ◽  
...  
Keyword(s):  
Geographical Information Systems ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Real Life ◽  
Google Earth ◽  
Geographical Information ◽  
Data Sets ◽  
Hurricane Impact ◽  
The City

Hurricanes affect thousands of people annually, with devastating consequences such as loss of life, vegetation and infrastructure. Vegetation losses such as downed trees and infrastructure disruptions such as toppled power lines often lead to roadway closures. These disruptions can be life threatening for the victims. Emergency officials, therefore, have been trying to find ways to alleviate such problems by identifying those locations that pose high risk in the aftermath of hurricanes. This paper proposes an integrated methodology that utilizes both Google Earth Engine (GEE) and geographical information systems (GIS). First, GEE is used to access Sentinel-2 satellite images and calculate the Normalized Difference Vegetation Index (NDVI) to investigate the vegetation change as a result of Hurricane Michael in the City of Tallahassee. Second, through the use of ArcGIS, data on wind speed, debris, roadway density and demographics are incorporated into the methodology in addition to the NDVI indices to assess the overall impact of the hurricane. As a result, city-wide hurricane impact maps are created using weighted indices created based on all these data sets. Findings indicate that the northeast side of the city was the worst affected because of the hurricane. This is a region where more seniors live, and such disruptions can lead to dramatic consequences because of the fragility of these seniors. Officials can pinpoint the identified critical locations for future improvements such as roadway geometry modification and landscaping justification.

Mapping plastic greenhouses with satellite imagery in Valparaiso, Chile: development of a new methodology through data cloud platform

2021 ◽  
Author(s):  
Ignacio Aguirre ◽  
Javier Lozano-Parra
Keyword(s):  
Spatial Distribution ◽  
Rural Areas ◽  
Urban Areas ◽  
Vegetation Index ◽  
Central Valley ◽  
Google Earth ◽  
Strong Increase ◽  
Landsat 8 ◽  
Adverse Weather ◽  
The Government

<p>During the last decades, there has been a strong increase around the globe in the use of plastic greenhouses (PGs) which respond to the need to provide better water security, overcome adverse weather events, or elude pests. The central valley of Chile has not been an exception and the surface covered by greenhouses has also experienced an increase over the years. In the Valparaiso region, the surface increased from 1122 ha to 1180 ha throughout the decade 1997-2007. However, on one hand, there has not been a new PGs census since 2007 and, on the other hand, its spatial distribution has not ever been mapped. Considering that agriculture in this region employs more than 60000 people and moves 4% of regional GPD, this information should be available to be included in land planning and to be incorporated into hydrological, economic, and food security models. To overcome this, we propose a new method for monitoring the variations of the surface covered by PGs based on the intersection of normalized difference indexes and areas excludes by masks. For this, free Landsat 8 multi-temporal cloud-free images were used, from which five indexes were obtained (Modified Soil-adjusted Vegetation Index, Temperature Brightness Index, Normalized Difference Vegetation Index - Green, Normalized Difference Built-up Index, and Plastic Surface Index). These indexes were then reclassified in binary form and added up. Finally, urban areas and high slope zones were excluded to obtain the final output. This procedure was run in Google Earth Engine, which allowed easy replication and automation for longer time series or in other study sites. Results proved this methodology was able to successfully discriminate the 86% of PG, which suppose 1410 ha. This surface is consistent with the agricultural census developed in 2007 and with the increase of more than 900 subsidies granted by the government for installing PGs. Its performance also supports our confidence to discriminate PGs in areas with different land covers such as reservoirs, rural areas, open crops, bare soil, and roads. Future studies will allow us to estimate the surface of plastic greenhouses in Chile, mapping its spatial distribution in all the country, and monitor changes over time.</p>

scholarly journals Google Earth Engine Applications

2019 ◽  
Vol 11 (5) ◽  
pp. 591 ◽  
Author(s):  
Onisimo Mutanga ◽  
Lalit Kumar
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Google Earth ◽  
Data Sets ◽  
Computing Platform ◽  
Huge Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

The Google Earth Engine (GEE) is a cloud computing platform designed to store and process huge data sets (at petabyte-scale) for analysis and ultimate decision making [...]

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Data Sets ◽  
Visual Words ◽  
Fusion Systems ◽  
Kaze Feature ◽  
Applications Of Machine Learning ◽  
Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

scholarly journals Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

2017 ◽  
Vol 126 ◽  
pp. 225-244 ◽  
Author(s):  
Jun Xiong ◽  
Prasad S. Thenkabail ◽  
Murali K. Gumma ◽  
Pardhasaradhi Teluguntla ◽  
Justin Poehnelt ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Google Earth ◽  
Google Earth Engine ◽  
Cropland Mapping

scholarly journals Environmental Vulnerability by Floods and Occupation of Permanent Preservation Area: An Approach about Three Cities of Midwest of Brazil

2017 ◽  
Vol 10 (2) ◽  
pp. 45
Author(s):  
Greyce Bernardes de Mello Rezende ◽  
Telma Lucia Bezerra Alves
Keyword(s):  
Urban Areas ◽  
Local Level ◽  
Urban Expansion ◽  
Google Earth ◽  
Satellite Imaging ◽  
Environmental Vulnerability ◽  
Industrial Buildings ◽  
Araguaia River ◽  
The Government ◽  
Very High

The purpose of this article is to identify the areas of environmental vulnerability by flooding in urban areas of the municipalities of Barra dos Garças - MT, Pontal do Araguaia - MT and Aragarças - GO; and demarcate the occupations in permanent preservation areas (PPAs) in the study area. The methodology uses variables such as time series of maximum quotas of the Araguaia River, from 1968 to 2014, the frequency of those floods, as well as the local level curves. From the junction of these data, it was stipulated the levels of environmental vulnerability by floods in five levels: very high, high, medium, low and very low. The results indicate that areas with very high vulnerability correspond to approximately 1,58 square kilometers which equals to 0.5% of the total area studied; the high vulnerability areas, have only 3.19 square kilometers, corresponding to 1% of the area; the medium vulnerability areas have 7.66 square kilometers, which corresponds to 2.41% of the area; low vulnerability areas, have 11.18 square kilometers of extension relating to 3.52% of the area; and finally the remainder of the study area was characterized as very low vulnerability. After this mapping, it was found by satellite imaging from Google earth software dated 2014, the main occupations in PPAs. The main uses and occupations refer to human activities related to tourism, as well as commercial, residential and industrial buildings. It was found that it is of salutary importance that the Government enforces the fulfillment of the restrictions set out in the Forest Code, preventing that more occupations occur in PPAs and areas subject to flooding. Moreover, the mapping of areas of flooding is also a tool for future public policies that aim to guide the recommended areas to urban expansion, as well as ordering the use and occupation of land by developing zoning.

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