Object-based Image Analysis Techniques Research Articles

Assessment of Ecosystem Services Value Based on Land Use and Land Cover Changes in the Transboundary Karnali River Basin, Central Himalayas

Land use and land cover change (LUCC) and its spatio-temporal characteristics are essential for natural resource management and sustainable development. LUCC is one of the major factors that affect the ecosystem and the services it provides. In this study, we used remote sensing techniques and a geographical information system to extract the land cover categories based on the Object-Based Image Analysis (OBIA) technique from Landsat TM/ETM/OLI satellite images in the transboundary Karnali River Basin (KRB, China and Nepal) of central Himalayas from 2000 to 2017. Spatio-temporal integrated methodology—Tupu was used to spatially show the LUCC as well as spatial characteristics of the arisen Tupu and shrunken Tupu. In addition, the ecosystem services value (ESV) were obtained and analyzed for each land cover category. In 2017, forest covered the highest area (33.45%) followed by bare area (30.3%), shrub/grassland (18.49%), agriculture (13.12%), snow/ice (4.32%), waterbody (0.3%) and built-up area (0.04%) in the KRB. From 2000 to 2017, the areas of forest, waterbody and snow/ice have decreased by 0.59, 6.14, and 1072.1 km2, respectively. Meanwhile, the areas of shrub/grassland, agriculture, barren land, and built-up categories have increased by 82.21, 1.44, 991.97, and 3.11 km2, respectively. These changes in the land cover have led to an increase in the ESV of the basin, especially the increase in shrub/grassland, agriculture, and water bodies (in the higher elevation). The total ESV of the basin was increased by $1.59 × 106 from 2000 to 2017. Anthropogenic factors together with natural phenomena drive LUCC in the basin and thus the ESV. The findings of this study could facilitate the basin-level policy formulation to guide future conservation and development management interventions.

Determination and accuracy analysis of individual tree crown parameters using UAV based imagery and OBIA techniques

In the process of producing information from images with very or ultra-high spatial resolution (VHR and UHR), the most accurate results are achieved by using object-based image analysis (OBIA) techniques. The most economical method to obtain UHR images is to use sensor systems that are integrated into unmanned aerial vehicles (UAV). In this study, which combines UHR-UAV-based images and OBIA-based analyzes, individual tree crown parameters were obtained, and the results were examined using various accuracy analysis techniques. For this purpose, the UAV data acquisition was performed at the altitude of 40 m above ground level, and a ground sample distance (GSD) of 1.28 cm was obtained. Photogrammetric processes were performed using the structure-from-motion (SfM) techniques, and orthomosaic and photogrammetric point cloud data were generated with 2.46 cm RMSE. OBIA-based techniques were applied to these data, and the individual tree heights, crown borders and related parameters were derived. For the accuracy analysis, actual tree heights were collected with terrestrial measurements. The reference tree crown borders were stereoscopically interpreted from UAV-based images. In this study, the accuracy of the tree crown borders and tree heights were tested over 31 parameters. Recommendations were presented by interpreting the ultimate accuracy values to determine the accuracy of the data obtained using OBIA techniques. As a result, OBIA techniques will increase the effectiveness and preciseness forest inventory applications, such as determination of the stand structural characteristics (canopy cover, canopy gaps, stand height etc.).

City-wide canopy cover decline due to residential property redevelopment in Christchurch, New Zealand

Urban redevelopment influences urban forests, with consequences for ecosystem service provision. Better understanding the effect of redevelopment on trees in cities can improve management and inform policy, thus having positive effects on ecosystem service provision and human wellbeing. This study quantified the effect of residential property redevelopment on canopy cover change in Christchurch, New Zealand. By applying an object-based image analysis (OBIA) technique to aerial imagery and LiDAR data, this study delineated tree canopy cover city-wide in 2011 and again in 2015 and then spatially quantified changes in city-wide canopy cover between 2011 and 2015. Changes in tree canopy cover were also determined at a finer scale, that of the meshblock, a geographic boundary used for census purposes. The results show a small absolute magnitude of city-wide tree canopy cover decline, from 10.84% to 10.28% between 2011 and 2015, but a statistically significant decrease in meshblock-scale mean tree canopy cover. Tree canopy cover losses were more likely to occur in meshblocks containing properties that underwent redevelopment, but the loss was insensitive to the density of redeveloped properties within meshblocks. These findings show that property redevelopment is an important influencer of urban forest dynamics.

An Object-Based Classification Method to Detect Methane Ebullition Bubbles in Early Winter Lake Ice

Thermokarst lakes in the Arctic and Subarctic release carbon from thawing permafrost in the form of methane and carbon dioxide with important implications for regional and global carbon cycles. Lake ice impedes the release of gas during the winter. For instance, bubbles released from lake sediments become trapped in downward growing lake ice, resulting in vertically-oriented bubble columns in the ice that are visible on the lake surface. We here describe a classification technique using an object-based image analysis (OBIA) framework to successfully map ebullition bubbles in airborne imagery of early winter ice on an interior Alaska thermokarst lake. Ebullition bubbles appear as white patches in high-resolution optical remote sensing images of snow-free lake ice acquired in early winter and, thus, can be mapped across whole lake areas. We used high-resolution (9–11 cm) aerial images acquired two and four days following freeze-up in the years 2011 and 2012, respectively. The design of multiresolution segmentation and region-specific classification rulesets allowed the identification of bubble features and separation from other confounding factors such as snow, submerged and floating vegetation, shadows, and open water. The OBIA technique had an accuracy of >95% for mapping ebullition bubble patches in early winter lake ice. Overall, we mapped 1195 and 1860 ebullition bubble patches in the 2011 and 2012 images, respectively. The percent surface area of lake ice covered with ebullition bubble patches for 2011 was 2.14% and for 2012 was 2.67%, representing a conservative whole lake estimate of bubble patches compared to ground surveys usually conducted on thicker ice 10 or more days after freeze-up. Our findings suggest that the information derived from high-resolution optical images of lake ice can supplement spatially limited field sampling methods to better estimate methane flux from individual lakes. The method can also be used to improve estimates of methane ebullition from numerous lakes within larger regions.

Landscape dynamics in the northeast part of Andhikhola watershed, Middle hills of Nepal

Depopulation and increasing greenery due to agriculture land abandonment is general scenario in many highlands of Nepal in recent decades. High resolution remote sensing image is used in land use change analysis. Recently, object based image analysis technique has helped to improve the land use classification accuracies using object based image analysis. Thus, this study was carried out with high resolution image data sources and innovative technique of land use classification in the northeast part of Andhikhola watershed, in the Middle Hill of Nepal. Increasing greenery due to agriculture land abandonment in the hill slope is the major land use change. Secondly, increasing built-up area in lowland along the highway is another. Decreasing hill farmers is the major drivers of converting cultivated land into vegetated area and increasing built-up area is due to urbanization and shift of rural people from hill slope to lowland and accessible area. Converting cultivated land into forest, shrubs and grassland is at marginal land and remote areas which is mostly controlled by altitude, slope gradient and slope aspect. Additionally, land suitability and accessibility are also other important controlling factors.

Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective

Image segmentation is a critical and important step in (GEographic) Object-Based Image Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly dependent on the quality of image segmentation. Segmentation has been used in remote sensing image processing since the advent of the Landsat-1 satellite. However, after the launch of the high-resolution IKONOS satellite in 1999, the paradigm of image analysis moved from pixel-based to object-based. As a result, the purpose of segmentation has been changed from helping pixel labeling to object identification. Although several articles have reviewed segmentation algorithms, it is unclear if some segmentation algorithms are generally more suited for (GE)OBIA than others. This article has conducted an extensive state-of-the-art survey on OBIA techniques, discussed different segmentation techniques and their applicability to OBIA. Conceptual details of those techniques are explained along with the strengths and weaknesses. The available tools and software packages for segmentation are also summarized. The key challenge in image segmentation is to select optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects. Recent research indicates an apparent movement towards the improvement of segmentation algorithms, aiming at more accurate, automated, and computationally efficient techniques.

Classification of impervious land-use features using object-based image analysis and data fusion

The proportion of impervious area within a watershed is a key indicator of the impacts of urbanization on water quality and stream health. Research has shown that object-based image analysis (OBIA) techniques are more effective for urban land-cover classification than pixel-based classifiers and are better suited to the increased complexity of high-resolution imagery. Focusing on five 2-km2 study areas within the Black Creek sub-watershed of the Humber River, this research uses eCognition® software to develop a rule-based OBIA workflow for semi-automatic classification of impervious land-use features (e.g., roads, buildings, Parking Lots, driveways). The overall classification accuracy ranges from 88.7 to 94.3%, indicating the effectiveness of using an OBIA approach and developing a sequential system for data fusion and automated impervious feature extraction. Similar accuracy results between the calibrating and validating sites demonstrates the strong potential for the transferability of the rule-set from pilot study sites to a larger area.

Mapping invasive plant with UAV-derived 3D mesh model in mountain area—A case study in Shenzhen Coast, China

Invasive plants constitute one of the major causes of biodiversity loss, and its monitoring plays an important role in the management of coastal ecological systems. This study aimed to apply high precision 3D mesh-model and digital orthophoto map (DOM) derived from unmanned aerial vehicle (UAV) multi view images to monitor the invasive plants over coastal mountain region in Shenzhen, China. To overcome the limitations of RGB images, the Gray-level Co-occurrence Matrix (GLCM) features of images were analyzed and combined with spectral features to obtain the 2-dimentional distribution of invasive plants first, using an object-based image analysis technique. A fine analysis was then introduced to obtain a more accurate 3-dimentional distribution of invasive plant by combining 2-dimentional distribution of invasive plant and 3D mesh model. the results have shown that: (1) Although the UAV RGB image has limited spectral information, the low-altitude makes the spatial resolution very high, which can effectively enhance the effectiveness of the texture in mapping invasive plants, and finally achieved an overall accuracy of 93.25%. (2) The use of 3D mesh model, on the one hand, could significantly alleviate the impact of undulatory terrain over mountain area and improve the classification result; on the other hand, it could better visualize the final results, helping us more intuitively understanding the distribution of invasive plants. This study demonstrated the great potential of UAV-derived 3D mesh model in accurate natural resource management over mountain areas.

Geospatial Analysis of Indus River Meandering and Flow Pattern from Chachran to Guddu Barrage, Pakistan

Natural and anthropogenic influence affects directly ecologic equilibrium and hydro morphologic symmetry of riverine surroundings. The current research intends to study the hydro morphologic features (meanders, shape, and size) of Indus River, Pakistan by using remote sensing (RS) and geographical information science (GIS) techniques to calculate the temporal changes. Landsat satellite imagery was used for qualitative and analytical study. Satellite imagery was acquired from Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI). Temporal satellite imagery of study area was used to identify the variations of river morphology for the years 1988,1995,2002,2009 and 2017. Research was based upon the spatial and temporal change of river pattern with respect to meandering and flow pattern observations for 30 years’ temporal data with almost 7 years’ interval. Image preprocessing was applied on the imagery of the study area for the better visualization and identification of variations among the objects. Object-based image analysis technique was performed for better results of a feature on the earth surface. Model builder (Arc GIS) was used for calculation of temporal variation of the river. In observation many natural factor involves for pattern changes such as; floods and rain fall. The object-oriented classification was applied for land use/land cover (LULC) features of the study area for the years 1988 and 2017 and abrupt change observed. Overall, 1988 to 2017 the Indus River in the study area has changed its path and pattern.

Monitoring Land-Use/Land-Cover Changes at a Provincial Large Scale Using an Object-Oriented Technique and Medium-Resolution Remote-Sensing Images

An object-based image analysis (OBIA) technique is replacing traditional pixel-based methods and setting a new standard for monitoring land-use/land-cover changes (LUCC). To date, however, studies have focused mainly on small-scale exploratory experiments and high-resolution remote-sensing images. Therefore, this study used OBIA techniques and medium-resolution Chinese HJ-CCD images to monitor LUCC at the provincial scale. The results showed that while woodland was mainly distributed in the west, south, and east mountain areas of Hunan Province, the west had the largest area and most continuous distribution. Wetland was distributed mainly in the northern plain area, and cultivated land was distributed mainly in the central and northern plains and mountain valleys. The largest impervious surface was the Changzhutan urban agglomerate in the northeast plain area. The spatial distribution of land cover in Hunan Province was closely related to topography, government policy, and economic development. For the period 2000–2010, the areas of cultivated land transformed into woodland, grassland, and wetland were 183.87 km2, 5.57 km2, and 70.02 km2, respectively, indicating that the government-promoted ecologically engineered construction was yielding some results. The rapid economic growth and urbanization, high resource development intensity, and other natural factors offset the gains made in ecologically engineered construction and in increasing forest and wetland areas, respectively, by 229.82 km2 and 132.12 km2 from 2000 to 2010 in Hunan Province. The results also showed large spatial differences in change amplitude (LUCCA), change speed (LUCCS), and transformation processes in Hunan Province. The Changzhutan urban agglomerate and the surrounding prefectures had the largest LUCCA and LUCCS, where the dominant land cover accounted for the conversion of some 189.76 km2 of cultivated land, 129.30 km2 of woodland, and 6.12 km2 of wetland into impervious surfaces in 2000–2010. This conversion was attributed to accelerated urbanization and rapid economic growth in this region.

Mapping the 3D structure of almond trees using UAV acquired photogrammetric point clouds and object-based image analysis

The geometric features, such as canopy area, tree height and crown volume, of agricultural trees provide useful information to elucidate plantation status and to design input prescription maps adjusted to real crop needs. This work presents an innovative procedure for computing the 3-dimensional (3D) geometric features of almond trees by applying two phases: 1) generation of photogrammetric point clouds with unmanned aerial vehicle (UAV) technology, and 2) analysis of the point clouds using object-based image analysis (OBIA) techniques. To test this approach, a UAV with a visible-RGB (low-cost) sensor was flown over three experimental almond groves at different phenological stages for two years, and the validation field method consisted of registering the height of a total of 325 trees in the two fields. The OBIA algorithm developed in this study achieved successful results: i) the individual and overall similarity measures between manually delineated and automatically detected almond tree crowns were above 0.9, and ii) the validation assessment conducted to estimate tree height from the UAV-derived algorithm produced an R2 = 0.94, an overall root mean square error (RMSE) of 0.39 m. The information derived from the OBIA algorithm was used for generating 3D maps for every tree volume and volume growth, which would be useful to understand the linkages between tree and crop management operations in the context of precision agriculture, with relevant agro-environmental implications. Our findings show that an RGB, low-cost sensor on-board a UAV provided dense 3D point clouds can be used for accurately characterising almond tree architecture.

Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery

Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained.

Object Based Image Analysis (OBIA) Methods for Mapping Poultry Farm Locations

Background: Livestock production is a major industry in the United States. Though livestock farms are associated with community exposures to pathogens and environmental pollutants, conducting exposure assessments and epidemiological analyses are often hindered by the difficulty of ascertaining their geographic locations. While county and watershed level counts of animal operations are available through the US Department of Agriculture’s Agricultural Census, a publicly available dataset of livestock farm locations does not exist. Objective: The presentation will explore a novel method employing object based image analysis (OBIA) to map farm locations, highlighting a case study of poultry farms in the state of Delaware (DE), USA.Methods: Briefly, a rule-based expert system to identify poultry houses was designed in eCognition Developer and deployed using eCognition Server (Trimble, Inc. Westminster, CO) that employs OBIA techniques to conduct a feature extraction of poultry farms from a combination of high-resolution imagery, LIDAR (light detection and ranging), and ancillary vector datasets, yielding a proposed geospatial database of poultry farm locations. Results were imported into ArcGIS (ESRI Corp, Redlands, CA) for subsequent analysis. Accuracy of poultry operation locations was assessed visually and in comparison to an animal operation facilities dataset from the DE’s publicly available land use / land cover dataset and the Delaware Environmental Navigator.Results: Producer’s accuracy for identifying poultry farms was 90%. While user’s accuracy was 50%, this was improved significantly by utilizing ancillary datasets. 3% of operations identified as poultry operations were non-poultry animal operations.Conclusion: Object based image analysis, especially when used in conjunction with ancillary data, is a promising tool for mapping poultry operations. Further study is needed to determine its generalizability to other animal operation types and geographic areas.

Supervised and Adaptive Feature Weighting for Object-Based Classification on Satellite Images

Object-based image analysis (OBIA) technique has been representing an evolving paradigm of remote sensing application, along with more high-resolution satellite images available. However, too many derived features from segmented objects also present a new challenge to OBIA applications. In this paper, we present a supervised and adaptive method for ranking and weighting features for object-based classification. The core of this method is the feature weight maps for each land type resulted from prior thematic maps and their corresponding satellite images of study areas. Specifically, first, satellite images to be classified are segmented using an adaptive multiscale algorithm, and the multiple (spectral, shape, and texture) features of segmented objects are calculated. Second, we extract distance maps and feature weight vectors for each land type from the prior thematic maps and corresponding satellite images, to generate feature weight maps. Third, a feature-weighted classifier with the feature weight maps, is applied on the segmented objects to generate classification maps. Finally, the classification result is evaluated. This approach is applied on a Sentinel-2 multispectral satellite image and a Google Map image to produce objected-based classification maps, compared with the traditional feature selection algorithms. The experimental results illustrate that the proposed method is practically efficient to select important features and improve classification performance.

Automated identification of intensive animal production locations from aerial photography.

Successful control of an emergency animal disease outbreak requires the timely and accurate identification of properties of interest. The identification of commercial piggeries within study areas in the Goulburn-Murray Irrigation District in Victoria, Australia, is used to demonstrate the innovative application of object-based image analysis (OBIA) techniques for the identification of intensive animal production land uses, to improve the accuracy of existing datasets. Characteristics of infrastructure and landscape features were combined to form a commercial piggery identification algorithm. These criteria were applied to recent aerial photography that had been classified using OBIA techniques. The results were then compared with three datasets containing known commercial piggery locations and visually checked by roadside surveys. The OBIA technique identified 21 potential piggery locations across three study areas, 14 of which were identified in existing databases. Of the 7 additional sites, 4 were dairy properties, 1 was a cropping and sheep property and 2 were previously undocumented piggery locations. The OBIA approach has potential of OBIA for identifying the locations of commercial piggeries. Further development and testing will determine how generic this approach is in terms of industry type and operation size. The method described is cost-effective, automated and repeatable, and could be used to regularly update existing databases by analysing newly acquired aerial imagery to identify possible land use changes. This would improve the reliability of currently available data and increase the effectiveness of a biosecurity response during an emergency animal disease outbreak.

A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal

Forests account for 80% of the total carbon exchange between the atmosphere and terrestrial ecosystems. Thus, to better manage our responses to global warming, it is important to monitor and assess forest aboveground carbon and forest aboveground biomass (FAGB). Different levels of detail are needed to estimate FAGB at local, regional and national scales. Multi-scale remote sensing analysis from high, medium and coarse spatial resolution data, along with field sampling, is one approach often used. However, the methods developed are still time consuming, expensive, and inconvenient for systematic monitoring, especially for developing countries, as they require vast numbers of field samples for upscaling. Here, we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites. The study was conducted in the Chitwan district of Nepal using GeoEye-1 (0.5 m), Landsat (30 m) and Google Earth very high resolution (GEVHR) Quickbird (0.65 m) images. For the local scale (Kayerkhola watershed), tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images. An overall accuracy of 83% was obtained in the delineation of tree canopy cover (TCC) per plot. A TCC vs. FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots. A coefficient of determination (R2) of 0.76 was obtained in the modelling, and a value of 0.83 was obtained in the validation of the model. To upscale FAGB to the entire district, open source GEVHR images were used as virtual field plots. We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model. Using the multivariate adaptive regression splines machine learning algorithm, we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices. The model was then used to extrapolate FAGB to the entire district. This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution (30 m) and accuracy (R2 = 0.76 and 0.7) with minimal error (RMSE = 64 and 38 tons ha−1) at local and regional scales. This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time. The method is especially applicable for developing countries that have low budgets for carbon estimations, and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation (REDD +) monitoring reporting and verification processes.

Comparison of pixel-based and object-based image classification techniques in extracting information from UAV imagery data

As the rapid development is being focused in the urban area, there is a need for the utilisation of a rapid system for updating this profile immediately. One of the current technologies being applied in recent years is the use of unmanned aerial vehicle (UAV) for mapping purposes. The use of UAV is widespread in various fields because it is low cost, has high resolution and is able to fly at low altitude without the constraints of cloudy weather. Typically, the method of data extraction for UAV in Malaysia is still very limited and the traditional methods are still being implemented by some industries. The features from aerial photo orthomosaic are manually detected and digitised from visual interpretation for the mapping purposes. Unfortunately, these methods are tedious, expensive, consume much time, and may involve much fieldwork, to acquire only a limited information. Pixel-based technique is often used to extract low level features where the image is classified according to the spectral information where the pixels in the overlapping region will be misclassified due to the confusion among the classes. The supervised object-based image analysis (OBIA) classification technique is widely used nowadays for automatic data extraction. Therefore, the general objective of this study is to assess the capability of UAV with high resolution data for image classifications. The pixel-based and OBIA classifications were compared using the Support Vector Machine (SVM) classifier. The classifications were assessed using different numbers of sample size. The result shows that OBIA gives a better result of Overall Accuracy (OA) than pixel-based. The consequences of this study accommodate further understanding and additional insight of utilising OBIA technique with different classifiers for the extended study.

Photovoltaic panel extraction from very high-resolution aerial imagery using region–line primitive association analysis and template matching

In object-based image analysis (OBIA), object classification performance is jointly determined by image segmentation, sample or rule setting, and classifiers. Typically, as a crucial step to obtain object primitives, image segmentation quality significantly influences subsequent feature extraction and analyses. By contrast, template matching extracts specific objects from images and prevents shape defects caused by image segmentation. However, creating or editing templates is tedious and sometimes results in incomplete or inaccurate templates. In this study, we combine OBIA and template matching techniques to address these problems and aim for accurate photovoltaic panel (PVP) extraction from very high-resolution (VHR) aerial imagery. The proposed method is based on the previously proposed region–line primitive association framework, in which complementary information between region (segment) and line (straight line) primitives is utilized to achieve a more powerful performance than routine OBIA. Several novel concepts, including the mutual fitting ratio and best-fitting template based on region–line primitive association analyses, are proposed. Automatic template generation and matching method for PVP extraction from VHR imagery are designed for concept and model validation. Results show that the proposed method can successfully extract PVPs without any user-specified matching template or training sample. High user independency and accuracy are the main characteristics of the proposed method in comparison with routine OBIA and template matching techniques.

Synergistic object-based multi-class feature extraction in urban landscape using airborne LiDAR data

This study addresses the potential of geospatial information extraction by using light detection and ranging (LiDAR) data and aerial optical images in an urban landscape. We have adopted an advanced geographic object-based image analysis (GEOBIA) technique consisting of rule-based procedures relying upon the integration of spectral, textural, and spatial characteristics of aerial imagery and roughness of point cloud of LiDAR to fuse aerial imagery and airborne LiDAR for effective urban geospatial information extraction. This study is focused on the extraction of four tangible geospatial features, e.g., buildings, trees, marine vessels, and cars. LiDAR-derived normalized digital surface model (nDSM) was insufficient in delineating the polygon features because of the sparse point cloud density at the edges of features, which greatly affected the accuracy of extracting the polygon features. Therefore aerial imagery was supplemented in order to enhance the quality of extraction. The final feature extraction accuracy was assessed against manual digitization by visual interpretation, statistical analysis, and confusion matrix. The accuracy of feature extraction was found to be ranging from 90 to 95%. The accuracy of buildings class was improved using intensity image generated from LiDAR data and Hough image along with morphological operations. In a nutshell, this study highlights robust improvements in the geospatial extraction of urban features by merging more than one dataset synergistically.

Pre-harvest Sugarcane Yield Estimation Using UAV-Based RGB Images and Ground Observation

Sugarcane supply can vary according to the cultivation area, climatic condition, and disease. Although there are several scientific simulation models for sugarcane yield estimation, they are not widely and efficiently used due to a large number of data requirements. The success of yield estimation using remote sensing and aerial observation was limited due to the insufficient spatial requirement and spectral requirement. This study introduces a technique to use UAV-acquired RGB images coupled with ground information for reliable and fast estimation of sugarcane yield for two popular varieties (KK3 and UT12) in Thailand. The first challenge was to discriminate sugarcane and non-sugarcane pixels in the images. For this purpose, both object-based image analysis (OBIA) and pixel-based image analysis techniques were investigated. The results revealed that OBIA technique (GLCM) could determine sugarcane pixels with 92 and 96% accuracy while pixel-based ExG method had accuracy of 84 and 88% for both the varieties. After identification of sugarcane pixels, the numbers of stalks, average height, and weight data were collected from 30 random sample points (size of 2 m × 2 m) from each variety. Using natural break method sampling, classes were created based on pixel value and number of stalks. The yield was finally estimated from sugarcane pixels using ground data and compared with harvested yield of both varieties. The object-based method produced the best result followed by pixel-based and traditional technique to estimate the yield. The very high spatial resolution of UAV image and advanced image classification of OBIA demonstrate significant potential for the farmers and related industries to predict yield before harvest.