scholarly journals Evaluation Methods of Change Detection of Seagrass Beds in the Waters of Pajenekang and Gusung Selayar

2021 ◽  
Vol 18 (23) ◽  
pp. 677
Author(s):  
Pragunanti Turissa ◽  
Nababan Bisman ◽  
Siregar Vincentius ◽  
Kushardono Dony ◽  
Madduppa Hawis
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
High Accuracy ◽  
Small Island ◽  
Marine Resources ◽  
Classification Methods ◽  
Sensing Data ◽  
South Sulawesi ◽  
Object Based ◽  
Seagrass Ecosystems

Knowledge about coastal and small island ecosystems is increasing for the monitoring of marine resources based on remote sensing. Remote sensing data provides up-to-date information with various resolutions when detecting changes in ecosystems. Studies have defined a shift in marine resources but were limited only to pixel or object classification in changes of seagrass area. In the present study, two classification method analysis approaches were compared to obtain optimum results in detecting changes in seagrass extent. It aimed to determine the dynamics of a seagrass ecosystem by comparing two classification methods in the waters of Gusung Island and Pajenekang, South Sulawesi, these methods being pixel-based and object-based classification methods. This research used SPOT-7 satellite imagery with 6 m2 of spatial resolution. Accuracy assessment using the confusion matrix showed optimum accuracy in object-based classification with an accuracy value of 87 %. Meanwhile, pixel-based classification showed an accuracy value of 78 % around Gusung Island. Pajenekang Island had accuracy values of 69 % with object-based classification and 65 % with pixel-based classification. A comparison of both classification methods revealed statistically high accuracy in mapping the benthic habitats of seagrass ecosystems. The results of the classifications showed a decline in the area of seagrass populations around Gusung Island from 2016 - 2018 and around Pajenekang Island from 2013 - 2017, with a change rate of 11.8 % around the island of Gusung and 7.6 % around the island of Pajenekang. This can explain the reason for the temporal method of object-based research classification having the best potential to process data changes in areas of seagrass in South Sulawesi waters and remote sensing information for the mapping of coastal area ecosystems. HIGHLIGHTS Information on coastal ecosystems globally with remote sensing data is currently very easy to access, but information related to ecosystem management and seagrass ecology in certain areas is still limited Analysis of seagrass benthic changes in shallow water requires data processing methods with high accuracy The OBIA (Object Based Image Analysis) method is one of the analytical methods that can provide optimal results in observing changes in seagrass ecosystems in the waters of South Sulawesi, Indonesia GRAPHICAL ABSTRACT

Object-based speckle filtering using multisensoral remote sensing data

2005 ◽  
Author(s):  
Björn Waske ◽  
Vanessa Heinzel ◽  
Matthias Braun ◽  
Gunter Menz
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Object Based ◽  
Speckle Filtering

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 Hierarchical Geographic Object-Based Vegetation Type Extraction Based on Multi-Source Remote Sensing Data

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1271
Author(s):  
Xuegang Mao ◽  
Yueqing Deng ◽  
Liang Zhu ◽  
Yao Yao
Keyword(s):  
Remote Sensing ◽  
Forest Type ◽  
Vegetation Type ◽  
Remote Sensing Data ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Sensing Data ◽  
Object Based ◽  
Four Levels ◽  
Airborne Lidar Data

Providing vegetation type information with accurate surface distribution is one of the important tasks of remote sensing of the ecological environment. Many studies have explored ecosystem structure information at specific spatial scales based on specific remote sensing data, but it is still rare to extract vegetation information at various landscape levels from a variety of remote sensing data. Based on Gaofen-1 satellite (GF-1) Wide-Field-View (WFV) data (16 m), Ziyuan-3 satellite (ZY-3) and airborne LiDAR data, this study comparatively analyzed the four levels of vegetation information by using the geographic object-based image analysis method (GEOBIA) on the typical natural secondary forest in Northeast China. The four levels of vegetation information include vegetation/non-vegetation (L1), vegetation type (L2), forest type (L3) and canopy and canopy gap (L4). The results showed that vegetation height and density provided by airborne LiDAR data could extract vegetation features and categories more effectively than the spectral information provided by GF-1 and ZY-3 images. Only 0.5 m LiDAR data can extract four levels of vegetation information (L1–L4); and from L1 to L4, the total accuracy of the classification decreased orderly 98%, 93%, 80% and 69%. Comparing with 2.1 m ZY-3, the total classification accuracy of L1, L2 and L3 extracted by 2.1 m LiDAR data increased by 3%, 17% and 43%, respectively. At the vegetation/non-vegetation level, the spatial resolution of data plays a leading role, and the data types used at the vegetation type and forest type level become the main influencing factors. This study will provide reference for data selection and mapping strategies for hierarchical multi-scale vegetation type extraction.

Proof of concept of a novel cloud computing approach for object-based remote sensing data analysis and classification

2018 ◽  
Vol 56 (4) ◽  
pp. 536-553 ◽  
Author(s):  
R. R. Antunes ◽  
T. Blaschke ◽  
D. Tiede ◽  
E. S. Bias ◽  
G. A. O. P. Costa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Data Analysis ◽  
Remote Sensing Data ◽  
Proof Of Concept ◽  
Sensing Data ◽  
Object Based ◽  
Computing Approach

scholarly journals AUTOMATIC BUILDING EXTRACTION USING A DECISION TREE OBJECT-BASED CLASSIFICATION ON JOINT USE OF AERIAL AND LIDAR DATA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 429-434
Author(s):  
R. Ghasemi Nejad ◽  
P. Pahlavani ◽  
B. Bigdeli
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Spatial Databases ◽  
Remote Sensing Data ◽  
Predictive Performance ◽  
Single Step ◽  
Building Extraction ◽  
Digital Maps ◽  
Sensing Data ◽  
Object Based

Abstract. Updating digital maps is a challenging task that has been considered for many years and the requirement of up-to-date urban maps is universal. One of the main procedures used in updating digital maps and spatial databases is building extraction which is an active research topic in remote sensing and object-based image analysis (OBIA). Since in building extraction field a full automatic system is not yet operational and cannot be implemented in a single step, experts are used to define classification rules based on a complex and subjective “trial-and-error” process. In this paper, a decision tree classification method called, C4.5, was adopted to construct an automatic model for building extraction based on the remote sensing data. In this method, a set of rules was derived automatically then a rule-based classification is applied to the remote sensing data include aerial and lidar images. The results of experiments showed that the obtained rules have exceptional predictive performance.

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>

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