Glacier Mapping with Object based Image Analysis Method, Case Study of Mount Everest Region

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Object-based image analysis (OBIA) method has been proven to improve the accuracy value on benthic habitat mapping. The purpose of this study was to assess the accuracy of several classification algorithms on benthic habitat mapping based on OBIA method with and without input feature of Hue Saturation Intensity (HSI) in the Pajenekang island waters, South Sulawesi, Indonesia. Sentinel-2A satellite imagery with 10 m2 spatial resolution acquired on 3 September 2018 was used in this study. During OBIA analyses, we segmented the object into 5, 10, and 15 classes and treated each of them with input features of mean+ratio vs mean+ratio+hue saturation intensity (HSI). We later classified the benthic habitat by applying several classification algorithms such as the Bayes, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The results showed that the Bayes algorithm produced highest accuracy of 78.35% within 10 segmentation classes and input features of mean+ratio+HSI followed by the KNN of 71.13% with 5 segmentation classes and input features of mean+ration+HSI. The addition of HSI input features into OBIA analyses increased the accuracy of benthic habitat classification mapping of 4.13% with the Bayes classification algorithm.

Shallow sea habitat has a very important role as a place to live and shelter for various types of organisms. Mapping this area needs to be done regularly considering the many benefits it has and the tendency for this area to change, one of them is by using remote sensing technology with the object-based image analysis (OBIA) method. OBIA classification includes the process of image segmentation and classification of all segmentation results. In the segmentation process using scale 100 at level 1 and scale 10 at level 2. Furthermore, at the classification process, 6 (six) habitat classes are obtained, namely sand, coral, macroalgae, sea grass, rubble and dead coral algae. The overall accuracy value in 2018 showed a value of 70%, and in 2022, it showed a value of 70.77%. The kappa coefficient value is 0.617 in the 2018 image acquisition and 0.612 in the 2022 image acquisition. The analysis carried out showed that there was a change in each habitat with the habitat that experienced the greatest changes are sand and dead coral algae class.

Forest inventory, monitoring, and assessment requires accurate tree species identification and mapping. Recent experiences with multispectral data from small fixed-wing and rotary blade unmanned aerial vehicles (UAVs) suggest a role for this technology in the emerging paradigm of enhanced forest inventory (EFI). In this paper, pixel-based and object-based image analysis (OBIA) methods were compared in UAV-based tree species classification of nine commercial tree species in mature eastern Ontario mixedwood forests. Unsupervised clustering and supervised classification of tree crown pixels yielded approximately 50%–60% classification accuracy overall; OBIA with image segmentation to delineate tree crowns and machine learning yielded up to 80% classification accuracy overall. Spectral response patterns and tree crown shape and geometric differences were interpreted in context of their ability to separate tree species of interest with these classification methods. Accuracy assessment was based on field-based forest inventory tree species identification. The paper provides a brief summary of future research issues that will influence the growth of this geomatics innovation in forest tree species classification and forest inventory.

Abstract. Agroforestry is an integrated self-sustainable land use management system that is not only capable of producing food from marginal agricultural land but also capable of maintaining and improving the quality of environment. Accurate assessment of trees on farmlands i.e. agroforestry would help in determining their contribution in meeting timber demand and also in carbon sequestration vis-a-vis climate change mitigation. In the present, high resolution multispectral satellite imagery (LISS-IV) has been used for mapping and estimating agroforestry area in Koraput district of Odisha. Both supervised and Object based Image Analysis (OBIA) classifications methods have been applied. In case of supervised maximum likelihood method, those pixels are fully captured where trees exist, whereas in OBIA captures trees according to their crown shapes. This proved OBIA method to be better in identification of trees on farmlands (scattered trees, boundary, and block plantations) than supervised method. This can lead to accurate estimation of area under trees in scattered form, in linear form and also in patch form. Improved results were obtained in case of OBIA classification with more than 90% accuracy. This research implies that remote sensing provide promising tools for evaluating and mapping of agroforestry at district level. Hence, the proposed approach of using high resolution remote sensing data in conjunction with OBIA method would be promising for mapping agroforestry area.

Monitoring of glaciers is crucial as they are an important source of freshwater, an indicator of global warming, and a contributor to sea level rise. Accurate delineation of glaciers plays a crucial role in glacier monitoring and remote sensing is the most appropriate tool to map glaciers.Existing glacier inventories have shortcomings such as unavailability in recent years and data quality. Traditional glacier mapping methods using remote sensing often rely on spectral band ratio techniques or manual digitizing. However, glacier boundaries achieved from manual digitizing are highly affected by human errors. Moreover, in the band ratio technique challenges arise in mapping debris-covered glaciers as traditional optical methods fail to distinguish debris-covered ice from surrounding rock due to their spectral similarities. Therefore, automatic mapping of glaciers is still challenging.Advanced deep learning methods have demonstrated significant advancements in automatic glacier mapping. However, the potential of state-of-the-art deep learning methods in glacier mapping has not yet been fully explored. When it comes to deep learning, one of the challenges is the amount of training data. With the low amount of training data, the results won't be of the desired accuracy. However, it is still possible to obtain good results using a lower amount of training data and the transfer learning technique.This study focuses on glacier mapping in Poiqu Basin (Central Himalaya), using U-Net and transfer learning. To this purpose, Sentinel-2 images and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model are deployed.The results indicate that transfer learning leads to considerably better results than training the deep learning network from scratch. Moreover, trying different backbones does not considerably affect the results. This study highlights the efficiency of the transfer learning technique, emphasizing its potential and effectiveness in regions with limited training data.

Sentinel-2A remote sensing satellite system was recently launched, providing free global remote sensing data similar to Landsat systems. Although the mission enables the acquisition of 10 m spatial resolution global data, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban areas in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets and using Wavelet transform (WT), Brovey transform (BT) and principal component analysis. Pixel-based and object-based image analysis (OBIA) classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentinel-2A data provided superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77 % by SVM, followed by 65.27 % using DT. BT produced the highest OA for the fusion images of 78.40 % with SVM and 52.21 % with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33 % by SVM, followed by 76.38 % using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35 % with SVM, followed by 65.66 % with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and useful for future long-term mapping applications. HIGHLIGHTS An accurate mapping of urban land is still challenging and requires high image quality of spectral and spatial aspects to identify features Single and fusion image analysis conducted in order to investigate and assess the most performing interpretation result by grouping out the features classes Statistical performance and image classification comparison is relevant to prove the most effective result among the images GRAPHICAL ABSTRACT

The traditionally human-visual or manual interpretation is generally chosen method for spatial feature extraction on map producing process in Indonesia, although the pixel-based feature extraction is used in particular step. The main problem of human-visual method is the dependency to operator capacity. Subjectivity may occur because each operator has different ability. In early 20th century, the image segmentation method was still developing and now Geographic Object-based Image Analysis (GEOBIA) method is introduced to mapping process. Researchers are still developing and applying GEOBIA for many mapping purposes. The advantage of GEOBIA is object-based segmentation which contextually influenced by spatial feature characteristics. GEOBIA provides more meaningful information than traditional pixel-based image analysis by allowing less well-defined edges or borders between different classes. On the resulting maps, there are divisions between different types of object classification, for example where some species of mangroves meet are not generally represented by a single object. The developing of remotely sensed imagery data quality requires the developing of extraction method as well. This article discusses the implementation of GEOBIA for coastal mapping in Indonesia. Some research combined pixel-based and GEOBIA method, and some get the overall accuracy > 70% of GEOBIA method. By developing a new segmentation accuracy measure, we evaluated that segmentation accuracies decrease with increasing segmentation scales and the negative impact of under-segmentation errors become significantly rise at large scale. The use of GEOBIA for coastal mapping in Indonesia are still lacking, locally applicable and still developing the transferability rule sets. The conclusion is that the innovation is needed to make GEOBIA method applicable for mass production of Indonesian coastal map.

Gully erosion and its complex and variable characteristics have led to challenging and many problems for creation of erosion maps with the most commonly used methods of satellite imagery analysis. The fuzzy algorithm-based object-based image analysis (OBIA) method is one of the most effective methods for classifying satellite imagery, which aims to integrate the spectral satellite imagery and provides the necessary facilities for the use of environmental and spatial information as well as a geometric feature of land surface phenomena. The purpose of this study was to apply and evaluate the implementation of different classification algorithms and fuzzy functions membership detecting and mapping of gully erosion. For this purpose, satellite images of Sentinel-2 were employed and accordingly the soil erosion map of Lighvan watershed was developed within the semi-automated approach by applying fuzzy-OBIA features and techniques. The obtained results from the accuracy assessment of the methods indicated that the accuracy of gully maps obtained by fuzzy-OBIA features including: length/width, asymmetry, density, and shape index algorithms with the respective kappa coefficient of 0.78, 0.91, 0.85, and 0.89 is higher than other algorithms. Also, the results obtained from the study of the degree of membership of fuzzy functions indicate the high role of this factor in the accuracy of the results, so that the highest classification accuracy (kappa coefficient greater than 0.91) was related to the length/width algorithm, in which the fuzzy functions B, D, H and L with the highest degree of membership (0.685, 1, 0.972 and 1, respectively) were employed. Results of current research are great of important for decision makers and authorities by means of providing detailed information regarding the soil erosion in the study area as well as GIS sciences society for applying and introducing the most efficient methods and techniques.

Interpreting ocean remote sensing images is still a challenge is worth studying because they can carry valuable information for various important applications. Due to the absence of labeled datasets, unsupervised object-based image analysis (OBIA) methods provide an effective solution to understand remote sensing images with its advantage of grouping local similar pixels into a homogeneous area. However, ocean remote sensing images usually have the characteristics of large size, large background, and coexisting of large and small objects, which result in previous OBIA methods easily falling into the difficulty of accurately segmenting the large and small objects at the same time, and the dilemma of time-consuming computation. To solve this problem, a novel multi-scale and multi-subgraph (MSMSG)-based image segmentation method is presented in this paper. First, a coarse-to-fine superpixel generation method is designed to generate optimal superpixels, which can not only solve the problem of coexisting large objects and small objects but also the problem of manually setting the initial segmentation number. Second, the proposed background removal strategy helps to eliminate the trouble of large background areas in ocean remote sensing images. Third, a multi-subgraph is constructed with the help of background removal. Finally, the multi-scale and multi-subgraph merging strategy is addressed to group all similar superpixels into a same cluster, which not only reduces the useless computation of nonadjacent superpixels but also avoids segmentation errors with the same scale. Experiments conducted on three different datasets show that the proposed segmentation method is high-performance and high-efficiency.

This manual provides detailed information on a customized methodology for glacier mapping using a remote sensing based semi-automatic technique for quick delivery. Based on this methodology, studies on the status of the glaciers of the Hindu Kush Himalaya and decadal glacier change since the 1980s have been carried out in selected areas and basins. The data and results derived from this methodology have been published in several journals, book chapters, and reports. A summary of the results and publications is presented here, and in global level glacier mapping initiatives. Reviews of the methodologies adopted by global initiatives like World Glacier Monitoring Service (WGMS), Global Land Ice Measurement from Space (GLIMS), and GlobGlacier are also presented in this manual. The methodology can be applied with little knowledge of remote sensing and geographic information systems. This is true not only for glacier mapping, but also for mapping the earth’s physical features. The methodology relies on the “eCognition” software, and post processing database management is done in an ArcGIS environment. A short introductory tour of the software is included here to facilitate the beginner’s understanding of image processing and data handling.

This paper concerns the study of the seafloor through digital seabed elevation models, using object based image analysis methods. The goal of this research was the automated extraction of geomorphological features from the seabed in regions presenting intense volcanic activity. The study area is located around the submarine volcano of the Kolοumbo (in the submarine area northeast of the Santorini island, Greece). For this purpose, a Digital Elevation Model (DEM) of the seabed with a spatial resolution of 50m was used. Derivatives of the DEM, such us Slope, Topographic Position Index (TPI) and Terrain Ruggedness Index (TRI) were created in the open source software "QGIS 2.4". The implementation of the object based image analysis approach was performed in eCognition 8.7 software. Nine segmentation and classification levels were created in order to produce the final level segmentation "level 5", where the final geomorphological features were classified. The results of the method were evaluated using classification stability measures and qualitative and quantitative comparison of the results with existing map.

The development of forest and land cover classification methods was currently increasing rapidly, one of which is by utilizing remote sensing applications, namely UAV technology. One method with high accuracy that can be used to classify forest and land cover is the object-based image analysis (OBIA) classification method. There is an assumption that the results of orthophoto shooting will look the same at a glance, but each orthophoto result also has a different spatial resolution and affects the detail of each OBIA segmentation. This makes this research important to do how to classify forest and land cover in the Ghimbo Pomuan customary forest, Kenegerian Kampa, Kampar Riau at different flying heights of the UAV. This study aims to map forest and land cover classes based on the UAV flight height and analyze the UAV flight altitude on the resulting land cover classification. The method used is exploration and processing of UAV imagery. Starting with the preparation and determination of observation points, forest and land cover classification using the OBIA method at several UAV flight heights and data analysis. In the Ghimbo Pomuan customary forest area, several land cover classifications were produced using OBIA. The land cover classes include water bodies, forests, oil palm plantations and shrubs. Classification of land cover in the water body class has a tendency of under segmentation at UAV flight height of 120 m. Object details in the oil palm and shrub land cover class were higher at UAV flight heights of 100 m than at 120 m and 130 m, whereas in the forest class there was no difference.

Exchange means change: An unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images.