scholarly journals MAPPING BURNT AREAS USING THE SEMI-AUTOMATIC OBJECT-BASED IMAGE ANALYSIS METHOD

2020 ◽  
Vol 17 (1) ◽  
pp. 57
Author(s):  
Hana Listi Fitriana ◽  
Suwarsono Suwarsono ◽  
Eko Kusratmoko ◽  
Supriatna Supriatna
Keyword(s):  
Image Analysis ◽  
Geographical Area ◽  
Remote Sensing Data ◽  
Environmental Damage ◽  
Landsat 8 ◽  
Fast Method ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Burnt Areas ◽  
The Impact

Forest and land fires in Indonesia take place almost every year, particularly in the dry season and in Sumatra and Kalimantan. Such fires damage the ecosystem, and lower the quality of life of the community, especially in health, social and economic terms. To establish the location of forest and land fires, it is necessary to identify and analyse burnt areas. Information on these is necessary to determine the environmental damage caused, the impact on the environment, the carbon emissions produced, and the rehabilitation process needed. Identification methods of burnt land was made both visually and digitally by utilising satellite remote sensing data technology. Such data were chosen because they can identify objects quickly and precisely. Landsat 8 image data have many advantages: they can be easily obtained, the archives are long and they are visible to thermal wavelengths. By using a combination of visible, infrared and thermal channels through the semi-automatic object-based image analysis (OBIA) approach, the study aims to identify burnt areas in the geographical area of Indonesia. The research concludes that the semi-automatic OBIA approach based on the red, infrared and thermal spectral bands is a reliable and fast method for identifying burnt areas in regions of Sumatra and Kalimantan.

scholarly journals ASSESSMENT OF LANDSLIDE-INDUCED MORPHOLOGY CHANGES USING AN OBJECT-BASED IMAGE ANALYSIS APPROACH: A CASE STUDY OF HÍTARDALUR, ICELAND

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 109-114
Author(s):  
Z. Dabiri ◽  
D. Hölbling ◽  
L. Abad ◽  
D. Tiede
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Remote Sensing Data ◽  
Optical Data ◽  
Sensing Data ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Spatio Temporal ◽  
The Impact ◽  
Morphology Changes

<p><strong>Abstract.</strong> On July 7, 2018, a large landslide occurred at the eastern slope of the Fagraskógarfjall Mountain in Hítardalur valley in West Iceland. The landslide dammed the river, led to the formation of a lake and, consequently, to a change in the river course. The main focus of this research is to develop a knowledge-based expert system using an object-based image analysis (OBIA) approach for identifying morphology changes caused by the Hítardalur landslide. We use synthetic aperture radar (SAR) and optical remote sensing data, in particular from Sentinel-1/2 for detection of the landslide and its effects on the river system. We extracted and classified the landslide area, the landslide-dammed lake, other lakes and the river course using intensity information from S1 and spectral information from S2 in the object-based framework. Future research will focus on further developing this approach to support mapping and monitoring of the spatio-temporal dynamics of surface morphology in an object-based framework by combining SAR and optical data. The results can reveal details on the applicability of different remote sensing data for the spatio-temporal investigation of landslides, landslide-induced river course changes and lake formation and lead to a better understanding of the impact of large landslides on river systems.</p>

Object-based image analysis of suburban landscapes using Landsat-8 imagery

2018 ◽  
Vol 12 (6) ◽  
pp. 720-736 ◽  
Author(s):  
Ming Shang ◽  
Shixin Wang ◽  
Yi Zhou ◽  
Cong Du ◽  
Wenliang Liu
Keyword(s):  
Image Analysis ◽  
Landsat 8 ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

2019 ◽  
Vol 11 (21) ◽  
pp. 2583 ◽  
Author(s):  
Payam Najafi ◽  
Hossein Navid ◽  
Bakhtiar Feizizadeh ◽  
Iraj Eskandari ◽  
Thomas Blaschke
Keyword(s):  
Image Analysis ◽  
Agricultural Research ◽  
Soil Surface ◽  
Control Measures ◽  
Landsat 8 ◽  
Surface Residue ◽  
Ground Truth Data ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2A

Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques.

scholarly journals Object-based image analysis of remote sensing data

2011 ◽  
Vol 55 (04) ◽  
pp. 641-664 ◽  
Author(s):  
Tatjana Veljanovski ◽  
Urša Kanjir ◽  
Krištof Oštir
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals Object-based image analysis for the assessment of mineral extraction in conflict regions – a case study in the Democratic Republic of the Congo

2016 ◽  
Vol XLI-B7 ◽  
pp. 891-896
Author(s):  
Olaf Kranz ◽  
Elisabeth Schoepfer ◽  
Kristin Spröhnle ◽  
Stefan Lang
Keyword(s):  
Image Analysis ◽  
Democratic Republic ◽  
Mining Industry ◽  
Remote Sensing Data ◽  
Mining Activities ◽  
Mineral Extraction ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Starting Point ◽  
Republic Of The Congo

In this study object-based image analysis (OBIA) techniques were applied to assess land cover changes related to mineral extraction in a conflict-affected area of the eastern Democratic Republic of the Congo (DRC) over a period of five years based on very high resolution (VHR) satellite data of different sensors. Object-based approaches explicitly consider spatio-temporal aspects which allow extracting important information to document mining activities. The use of remote sensing data as an independent, up-to-date and reliable data source provided hints on the general development of the mining sector in relation to socio-economic and political decisions. While in early 2010, the situation was still characterised by an intensification of mineral extraction, a mining ban between autumn 2010 and spring 2011 marked the starting point for a continuous decrease of mining activities. The latter can be substantiated through a decrease in the extend of the mining area as well as of the number of dwellings in the nearby settlement. A following demilitarisation and the mentioned need for accountability with respect to the origin of certain minerals led to organised, more industrialized exploitation. This development is likewise visible on satellite imagery as typical clearings within forested areas. The results of the continuous monitoring in turn facilitate non-governmental organisations (NGOs) to further foster the mentioned establishment of responsible supply chains by the mining industry throughout the entire period of investigation.

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