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 DIFFERENT SPATIAL RESOLUTION SATELLITE IMAGERIES IN URBAN AND SUBURBAN ENVIRONMENT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 105-112
Author(s):  
I. Kotaridis ◽  
M. Lazaridou
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Land Cover ◽  
Spatial Resolution ◽  
Support Vector ◽  
Landsat 8 ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Very High Spatial Resolution ◽  
Sentinel 2A

Abstract. Monitoring urban and suburban land cover has become a particularly researched investigation field in remote sensing community, since there is a large amount of professionals interested in gathering useful information, regarding urban sprawl and its side effects in natural vegetation, urban parks and water bodies. This paper focuses on studying the implementation of an object-based image analysis methodological framework, in Orfeo ToolBox. Moderate, high and very high spatial resolution satellite images were utilized in order to generate thematic land cover maps of the study area located in Thessaloniki, Greece. Taking into consideration that there is not a relevant research in literature concerning the selection of segmentation parameters values, the optimal values are presented for MeanShift segmentation algorithm. Classifications were conducted with the use of Support Vector Machines algorithm and the final outputs are presented, accompanied by the evaluation of accuracy assessments which is a mandatory step in every remote sensing project. The analysis showed that OBIA, in this case, works well with Landsat-8 and QuickBird data and exceptionally well with Sentinel-2A data with over 90% overall accuracy. Critical considerations on the aforementioned are also included.

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 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.

Winter Wheat Mapping Using Landsat 8 Images and Geographic Object-Based Image Analysis

2017 ◽  
Vol 60 (3) ◽  
pp. 625-633
Author(s):  
Tengfei Su ◽  
Shengwei Zhang
Keyword(s):  
Image Analysis ◽  
Winter Wheat ◽  
Feature Reduction ◽  
Classification Model ◽  
Feature Analysis ◽  
Landsat 8 ◽  
Scale Selection ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

Abstract. Winter wheat is a major food source in many areas, so it is necessary to construct an effective approach for its monitoring based on satellite data. By taking advantage of geographic object-based image analysis (GEOBIA), a winter wheat classification framework was established. Two stages, which included scale selection and feature analysis, were incorporated into the new approach. The scale selection stage was implemented based on an unsupervised method, so human intervention for tuning the scale parameter of image segmentation can be largely saved. The feature analysis stage was performed on the basis of a random forest classification model, and in the experiment this step allowed for feature reduction, which was validated to be beneficial to the classification performance. Keywords: Feature analysis, GEOBIA, Scale selection, Winter wheat.

Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery

2017 ◽  
Vol 33 (10) ◽  
pp. 1064-1083 ◽  
Author(s):  
A. Stefanidou ◽  
E. Dragozi ◽  
D. Stavrakoudis ◽  
I. Z. Gitas
Keyword(s):  
Image Analysis ◽  
Landsat 8 ◽  
Fuel Type ◽  
Landsat 8 Oli ◽  
Object Based Image Analysis ◽  
Type Mapping ◽  
Object Based
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