scholarly journals Burnt area mapping in insular Southeast Asia using medium resolution satellite imagery

2007 ◽  
Vol 2007 (45) ◽  
Author(s):  
Jukka Miettinen
Keyword(s):  
Southeast Asia ◽  
Satellite Imagery ◽  
Burnt Area ◽  
Medium Resolution

Burnt area estimation for the year 2005 in Borneo using multi-resolution satellite imagery

2007 ◽  
Vol 16 (1) ◽  
pp. 45 ◽  
Author(s):  
Jukka Miettinen ◽  
Andreas Langner ◽  
Florian Siegert
Keyword(s):  
Satellite Imagery ◽  
Large Scale ◽  
Agricultural Land ◽  
Fire Detection ◽  
Small Scale ◽  
Burnt Area ◽  
Active Fire ◽  
Medium Resolution ◽  
Significant Difference ◽  
Degraded Ecosystems

Humid tropical South-East Asia suffers significant yearly biomass burning. This paper evaluates and compares the results of medium-resolution (MODIS) burnt area mapping and hotspot-based assessment of fire affected areas in Borneo in 2005, using field observations and high resolution Landsat ETM+ data as reference. Based on burnt area mapping, over 600 000 ha burnt in large-scale vegetation fires. Approximately 90% of this burning took place in degraded ecosystems and was related to agricultural land clearing activities or logged over forests. The estimation based on active fire detection (hotspots) resulted in a total burnt area of more than 1.1 million hectares. The reason for this significant difference was that small scale shifting cultivation fires could not be detected in MODIS images. These results indicate that a combination of both methods is required to reliably assess burnt areas in Borneo using medium-resolution MODIS satellite imagery.

scholarly journals A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2233
Author(s):  
Rasa Janušaitė ◽  
Laurynas Jukna ◽  
Darius Jarmalavičius ◽  
Donatas Pupienis ◽  
Gintautas Žilinskas
Keyword(s):  
Satellite Imagery ◽  
Morphological Characteristics ◽  
Unsupervised Classification ◽  
Landsat 8 ◽  
Multi Scale ◽  
Medium Resolution ◽  
Wide Range ◽  
Position Index ◽  
Local Relief ◽  
Length Width

Satellite remote sensing is a valuable tool for coastal management, enabling the possibility to repeatedly observe nearshore sandbars. However, a lack of methodological approaches for sandbar detection prevents the wider use of satellite data in sandbar studies. In this paper, a novel fully automated approach to extract nearshore sandbars in high–medium-resolution satellite imagery using a GIS-based algorithm is proposed. The method is composed of a multi-step workflow providing a wide range of data with morphological nearshore characteristics, which include nearshore local relief, extracted sandbars, their crests and shoreline. The proposed processing chain involves a combination of spectral indices, ISODATA unsupervised classification, multi-scale Relative Bathymetric Position Index (RBPI), criteria-based selection operations, spatial statistics and filtering. The algorithm has been tested with 145 dates of PlanetScope and RapidEye imagery using a case study of the complex multiple sandbar system on the Curonian Spit coast, Baltic Sea. The comparison of results against 4 years of in situ bathymetric surveys shows a strong agreement between measured and derived sandbar crest positions (R2 = 0.999 and 0.997) with an average RMSE of 5.8 and 7 m for PlanetScope and RapidEye sensors, respectively. The accuracy of the proposed approach implies its feasibility to study inter-annual and seasonal sandbar behaviour and short-term changes related to high-impact events. Algorithm-provided outputs enable the possibility to evaluate a range of sandbar characteristics such as distance from shoreline, length, width, count or shape at a relevant spatiotemporal scale. The design of the method determines its compatibility with most sandbar morphologies and suitability to other sandy nearshores. Tests of the described technique with Sentinel-2 MSI and Landsat-8 OLI data show that it can be applied to publicly available medium resolution satellite imagery of other sensors.

Olive Tree Classification and Inventory with Medium Resolution Multi-spectral Satellite Imagery

2019 ◽  
pp. 13-30
Author(s):  
Ranya Mezzi ◽  
Mitchel Alioscha-Perez ◽  
Mohamed Allani ◽  
Fatma Guedri ◽  
Adel Zouabi ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Olive Tree ◽  
Medium Resolution

scholarly journals CLASSIFICATION OF THE STRUCTURE OF CITIES THROUGH MID-RESOLUTION SATELLITE IMAGERY AND PATCH BASED NEURAL NETWORKS

2018 ◽  
Vol XLII-5 ◽  
pp. 713-717
Author(s):  
D. Verma ◽  
A. Jana ◽  
K. Ramamritham
Keyword(s):  
Satellite Imagery ◽  
Urban Areas ◽  
Visual Exploration ◽  
Urban Spatial Structure ◽  
Urban Forms ◽  
Medium Resolution ◽  
Convolutional Autoencoder ◽  
Energy Consumption Patterns ◽  
Advanced Computer

<p><strong>Abstract.</strong> The studies in the classification of the urban spatial structure have been essential in deriving insights into the land cover and the built typology which helped in the estimation of energy consumption patterns, urban density, compactness, and hierarchy of settlements. However, the analysis and comparison of the physical forms of the cities have been attempted in a piecemeal fashion where the requirement of datasets and the computation power for analysis has been a major hindrance. With the advancement in machine learning based techniques, large datasets such as satellite imagery can be studied with advanced computer vision methods. These solutions may help in studying the intricate nature of human habitats in large extents of geographical areas including various urban areas. This study utilizes smaller patches of medium resolution Sentinel-2B Imagery of ten different cities in India to explore the urban forms present in these cities. This study uses Stacked Convolutional Autoencoder (CAE) to reduce the dimensionality of satellite imagery patches and unsupervised clustering techniques such as t-SNE and K-means to study the characteristics of similar patches. On analyzing the clusters through visual exploration, similar patches are delineated and provided with corresponding labels representing urban forms. Individual clusters are then studied with respect to each city. The motive of the study is to gain insights into the different types of morphological patterns present within and among cities.</p>

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