MAPPING TREES ON FARMLANDS USING OBIA METHOD AND HIGH RESOLUTION SATELLITE DATA: A CASE STUDY OF KORAPUT DISTRICT, ODISHA

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.

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.

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.

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.

Substantial progress in Geoinformatics System in recent years leads to the research in monitoring and mapping of glaciers. Monitoring glacier in the mountain region with traditional manual method is very crucial and time-consuming. Glaciers are melting because of global warming. Melting of glaciers can causes calamities like rising in sea level, glacial lake outburst, avalanches etc. Glacier monitoring using multi-temporal data for objects on the surface of the glacier is hard to classify. This paper gives an insight into the importance of Geo-spatial data and object-based image analysis method for satellite image processing. The object-based image analysis benefits more compared to traditional pixel-based image analysis as it is more robust and noise removing more image features etc. Spectral data with multiple bands is the backbone of surveying and monitoring of glacier. In this paper case study of Mount Everest region (27 48° 32N, 86 54° 47E) is represented. The remotely sensed image needs to be taken in a cloud-free environment. Object-based image classification is done in recognition tool. Also, the step by step methodology of object-based classification, segmentation and post-classification possibilities are discussed. Finally, the paper presents several representations of indexes. The integration of indexes is useful for accurately classifying the different part of terrain, lake, vegetation and glacier.

Regional lineament detection for mapping of geological structure can provide crucial information for mineral exploration. Manual methods of lineament detection are time consuming, subjective and unreliable. The use of semi-automated methods reduces the subjectivity through applying a standardised method of searching. Object-Based Image Analysis (OBIA) has become a mainstream technique for landcover classification, however, the use of OBIA methods for lineament detection is still relatively under-utilised. The Southwest England region is covered by high-resolution airborne geophysics and LiDAR data that provide an excellent opportunity to demonstrate the power of OBIA methods for lineament detection. Herein, two complementary but stand-alone OBIA methods for lineament detection are presented which both enable semi-automatic regional lineament mapping. Furthermore, these methods have been developed to integrate multiple datasets to create a composite lineament network. The top-down method uses threshold segmentation and sub-levels to create objects, whereas the bottom-up method segments the whole image before merging objects and refining these through a border assessment. Overall lineament lengths are longest when using the top-down method which also provides detailed metadata on the source dataset of the lineament. The bottom-up method is more objective and computationally efficient and only requires user knowledge to classify lineaments into major and minor groups. Both OBIA methods create a similar network of lineaments indicating that semi-automatic techniques are robust and consistent. The integration of multiple datasets from different types of spatial data to create a comprehensive, composite lineament network is an important development and demonstrates the suitability of OBIA methods for enhancing lineament detection.

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

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.

Urbanization poses significant challenges to ecosystems, resources, and human well-being, necessitating sustainable planning. Urban vegetation, particularly trees, provides critical ecosystem services such as carbon sequestration, air quality improvement, and biodiversity conservation. Traditional tree assessments are resource-intensive and time-consuming. Recent advances in remote sensing, especially high-resolution multispectral imagery and object-based image analysis (OBIA), offer efficient alternatives for mapping urban vegetation. This study evaluates and compares the efficacy of Sentinel-2 and Pléiades satellite imagery in classifying tree species within historic urban parks in Rome—Villa Borghese, Villa Ada Savoia, and Villa Doria Pamphilj. Pléiades imagery demonstrated superior classification accuracy, achieving an overall accuracy (OA) of 89% and a Kappa index of 0.84 in Villa Ada Savoia, compared to Sentinel-2’s OA of 66% and Kappa index of 0.47. Specific tree species, such as Pinus pinea (Stone Pine), reached a user accuracy (UA) of 84% with Pléiades versus 53% with Sentinel-2. These insights underscore the potential of integrating high-resolution remote sensing data into urban forestry practices to support sustainable urban management and planning.

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

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.

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.

Cobia fish cage is the most popular marine culture species raised in Phu Quoc Island, Vietnam. For its sustainable development, there is a need to determine the carrying capacity to avoid negative marine environmental impact in the future. This study was carried out to collect water samples each two months at the lowest and highest tides at four points around the farming area in Rach Vem, Phu Quoc Island, Kien Giang Province from February to October 2011. Water quality in cobia cage culture was surveyed to assess the environmental status of coastal aquaculture areas including seven parameters such as DO, COD, BOD, TSS, TN, TP and Chlorophyll-a. These parameters are suitable to rear cobia fish cage in this area. Nitrogen and phosphorus are considered as the principal nutrients produced by the cobia fish farm and affecting water environment. This study found that the carrying capacity for fish cage farming in the area is 290.96 to 727.81 tons (based on total nitrogen) and 428.64 to 1,383.88 tons (based on total phosphorus) from February to Au-gust 2011. The maximum number of cobia cages should be, based on total nitrogen, from 64 to 266 and, based on total phosphorus, from 94 to 253. Moreover, this study examined the possibility of remote sensing and geographic information system (GIS) technique based on Object-based Image Analysis (OBIA) method by THEOS imagery for mapping of cage culture facilities and detect the location for cobia cage culture in study area. Cá bớp nuôi lồng bè là một trong những loài cá nuôi phổ biến khu vực ven biển Phú Quốc, Việt nam. Nhằm phát triển bền vững vùng ven biển, đề tài thực hiện đã xác định và đánh giá hiện trạng môi trường nước và sức tải môi trường của nghề nuôi cá bớp lồng bè ở đảo Phú Quốc. Nghiên cứu này được thực hiện thông qua việc thu mẫu môi trường nước mỗi 2 tháng theo mức nước triều cao nhất và thấp nhất tại 4 điểm quanh khu vực nuôi tại ấp Rạch Vẹm, huyện Phú Quốc, tỉnh Kiên Giang từ tháng 02-10/2011. Chất lượng nước khu vực nuôi thủy sản cũng được khảo sát với 7 chỉ tiêu gồm DO, COD, BOD, TSS, TN, TP, Chlorophyll-a. Các thông số chất lượng môi trường này phù hợp nuôi cá bớp tại khu vực nghiên cứu dựa trên tiêu chuẩn chất lượng môi trường. Trong nghiên cứu này, đạm và lân là 2 thông số được sử dụng để tính toán sức tải môi trường. Sức tải môi trường được tính toán cho khu vực nuôi cá bớp dao động khoảng từ 290.96 tấn đến 727.81 tấn (tính trên hàm lượng đạm tổng số) và từ 428,64 tấn đến 1.383,88 tấn (tính trên hàm lượng lân tổng số) trong thời gian từ tháng 2 đến tháng 10 năm 2011. Số lượng tối đa lồng bè nuôi cá bớp khoảng từ 64 đến 266 (đạm tổng số) và từ 94 đến 253 (lân tổng số) dựa vào phân tích hồi quy tương quan. Bên cạnh đó, công nghệ viễn thám và hệ thống thông tin địa lý (GIS) đã được ứng dụng bằng cách sử dụng ảnh THEOS để xác định vị trí và phân bố không gian khu vực nuôi cá lồng bè dựa trên phương pháp phân tích đối tượng theo hướng (OBIA).

The ability to extract biophysical and structural properties of features in digital images is an important goal in remote sensing. Delineating the boundaries of image features, and building meaningful numerical descriptors of them based on their geometric shape, are both challenging tasks that are necessary to identify shape pattern in image data. This study seeks to evaluate and compare the performance of Fourier harmonic descriptors against dimensionless ratios of image-object shape for characterizing objects in image classification training samples. We took 150 random samples of three different object types – forest canopies, residential houses, and buildings (50 samples each) – from a high spatial-resolution satellite image (WorldView-2) covering a portion of the southern end of California’s Great Central Valley. To identify patterns in object shapes and perform object classification, we followed three steps. First, we performed image segmentation through an object-based image analysis (OBIA) method based on the Watershed Transform. We then carried out shape characterization using Fourier harmonics to measure variation in the silhouette of different object types. Finally, we compared and evaluated the performance of dimensionless ratios with Fourier descriptors by classifying different object shapes and assessing the accuracy of each method. Multinomial regression models were fitted to compare the accuracy and error of the two methods. Classification accuracy assessment was addressed utilizing hypothesis tests and significance of the likelihood ratio (LR) test and Akaike’s Information Criterion (AIC). Dimensionless ratios and Fourier harmonics had similar, moderate accuracy of 70% and 73.33%, respectively, but only four harmonics were required to achieve the best model fit, and six were needed for dimensionless ratios. Harmonic analysis provides quantitative descriptions of object shapes allowing pattern characterizations that can improve a supervised classification.