Object-based Image Classification Research Articles

A Comparison of Pixel Based and Object Based Image Classification for Cropland Area Estimation

Pixel-based (PB) and object-based (OB) Land Use–Land Cover (LULC) classification techniques can be applied in Google Earth Engine (GEE), a flexible cloud-based platform, because of its numerous state-of-the-art functions that comprise several Machine Learning (ML) methods. Adding some texture measure, any measure to a classification usually improves the accuracy. Object segmentation and object textural analysis are two OB methods that are still uncommon in the GEE environment. Object based image classification is difficult because it can be challenging to concatenate the correct functions and adjust various parameters in order to get past the computational limitations of GEE. The goal of this work is to develop and test an OB classification approach that combines the ML algorithm Random Forest (RF) to perform the final classification, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices and the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters. The primary seven GLCM indices are subjected to a Principal Components Analysis (PCA) in order to combine the textural data needed for the OB classification into a single band. The proposed methodology was broadly tested in a 304 km2 study area, located in the Telangana state (India), using Sentinel 2 (S2).

Automated extraction of mining‐induced ground fissures using deep learning and object‐based image classification

AbstractAccurate extraction of ground fissures caused by intense coal mining has the potential to significantly improve the efficiency of environmental monitoring in mining areas. However, the extraction results using previous methods have often exhibited issues of discontinuity and substantial deviation from ground truth data, resulting in low extraction accuracy. In this study, a novel approach, ENVINet5‐OBIC, for extracting ground fissures in mining areas is proposed, which integrates object‐based image classification (OBIC) with the pixel‐based deep learning model ENVINet5. ENVINet5‐OBIC uses OBIC to segment high‐resolution unmanned aerial vehicle (UAV) images across different scales, effectively considering shape, texture and correlative information between adjacent pixels. Furthermore, by utilizing homogeneous objects as building blocks, it establishes a deep learning model for the automated extraction of ground fissures. Experimental results show that ENVINet5‐OBIC performs better when compared with OBIC, U‐Net, PSPNet and ENVINet5 methods in terms of continuity, accuracy and error reduction. In addition, the ground fissure area extracted by ENVINet5‐OBIC closely aligns with ground truth data. This study provides a more effective method for automatic extraction of ground fissures, which improves the efficiency of environmental monitoring in mining areas.

Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz)

Köyceğiz Lake is one of our country’s most critical coastal barrier lakes, rich in sulfur, located at the western end of the Mediterranean Region. Köyceğiz Lake, connected to the Mediterranean via the Dalyan Strait, is one of the 7 lakes in the world with this feature. In this study, water change analysis of Köyceğiz Lake was carried out by integrating the Object-Based Image Classification method with CART (Classification and Regression Tree), RF (Random Forest), and SVM (Support Vector Machine) algorithms, which are machine learning algorithms. SNIC (Simple Non-iterative Clustering) segmentation method was used, which allows a detailed analysis at the object level by dividing the image into super pixels. Sentinel 2 Harmonized images of the study area were obtained from the Google Earth Engine (GEE) platform for 2019, 2020, 2021, and 2022,and all calculations were made in GEE. When the classification accuracies of four years were examined, it was seen that the classification accuracies(OA, UA, PA, and Kappa) of the lake water area were above 92%, F-score was above 0.98 for all methods using the object-based classification method obtained by the combination of the SNIC algorithm and CART, RF, and SVM machine learning algorithms. It has been determined that the SVM algorithm has higher evaluation metrics in determining the lake water area than the CART and RF methods.

Detection and classification of Brandt’s vole burrow clusters utilizing GF-2 satellite imagery and faster R-CNN model

Most small rodent populations worldwide exhibit fascinating population dynamics, capturing the attention of numerous scholars due to their multiyear cyclic fluctuations in population size and the astonishing amplitude of these fluctuations. Hulunbuir steppe stands as a crucial global hub for livestock production, yet in recent decades, the area has faced recurring challenges from steppes rodent invasions, with Brandt’s vole (Lasiopodomys brandtii, BV) being particularly rampant among them. They not only exhibit seasonal reproduction but also strong social behavior, and are generally considered pests, especially during population outbreak years. Prior studies suggest that BV population outbreaks tend to occur across a wider geographic area, and a strong indicator for identifying rodent outbreaks is recognizing their burrow clusters (burrow systems). Hence, this paper conducts target object detection of BV burrow clusters in the typical steppes of Hulunbuir using two GF-2 satellite images from 2021 (the year of the BV outbreak). This task is accomplished by incorporating the Faster R-CNN model in combination with three detection approaches: object-based image classification (OBIC), based on vegetation index classification (BVIC), and based on texture classification (BTC). The results indicate that OBIC demonstrated the highest robustness in BV burrow cluster detection, achieving an average AP of 63.80% and an F1 score of 0.722 across the two images. BTC exhibited the second-highest level of accuracy, achieving an average AP of 55.95% and an F1 score of 0.6660. Moreover, this approach displayed a strong performance in BV burrow clusters localization. In contrast, BVIC achieved the lowest level of accuracy among the three methods, with an average AP of only 29.45% and an F1 score of 0.4370. Overall, this study demonstrates the crucial role of utilizing high-resolution satellite imagery combined with DL-based object detection techniques in effectively monitoring and managing the potential outbreaks of steppe rodent pests across larger spatial extents.

A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring

Aquatic vegetation is an essential component of lake ecosystems, used as a biological indicator for in situ monitoring within the Water Framework Directive. We developed a hierarchical object-based image classification model with multi-seasonal Sentinel-2 imagery and suitable spectral indices in order to map the aquatic vegetation in a Mediterranean oligotrophic/mesotrophic deep lake; we then applied the model to another lake with similar abiotic and biotic characteristics. Field data from a survey of aquatic macrophytes, undertaken on the same dates as EO data, were used within the accuracy assessment. The aquatic vegetation was discerned into three classes: emergent, floating, and submerged aquatic vegetation. Geographic object-based image analysis (GEOBIA) proved to be effective in discriminating the three classes in both study areas. Results showed high effectiveness of the classification model in terms of overall accuracy, particularly for the emergent and floating classes. In the case of submerged aquatic vegetation, challenges in their classification prompted us to establish specific criteria for their accurate detection. Overall results showed that GEOBIA based on spectral indices was suitable for mapping aquatic vegetation in oligotrophic/mesotrophic deep lakes. EO data can contribute to large-scale coverage and high-frequency monitoring requirements, being a complementary tool to in situ monitoring.

Estimation of Rooftop Solar Photovoltaic Potential Based on High-Resolution Images and Digital Surface Models

Buildings are important components of urban areas, and the construction of rooftop photovoltaic systems plays a critical role in the transition to renewable energy generation. With rooftop solar photovoltaics receiving increased attention, the problem of how to estimate rooftop photovoltaics is under discussion; building detection from remote sensing images is one way to address it. In this study, we presented an available approach to estimate a building’s rooftop solar photovoltaic potential. A rapid and accurate rooftop extraction method was developed using object-based image classification combining normalized difference vegetation index (NDVI) and digital surface models (DSMs), and a method for the identification of suitable rooftops for solar panel installation by analysing the geographical restrictions was proposed. The approach was validated using six scenes from Beijing that were taken using Chinese Gaofen-2 (GF-2) satellite imagery and Pleiades imagery. A total of 176 roofs in six scenarios were suitable for PV installation, and the estimated photovoltaic panel area was 205,827 m2. The rooftop photovoltaic potential was estimated to total 22,551 GWh. The results indicated that the rooftop photovoltaic potential estimation method performs well.

Tree Species Mapping in Mangrove Ecosystems Using UAV-RGB Imagery and Object-Based Image Classification

Tree Species Mapping in Mangrove Ecosystems Using UAV-RGB Imagery and Object-Based Image Classification

Refining National Forest Cover Data Based on Fusion Optical Satellite Imageries in Indonesia

Precision mapping towards tropical forest cover data is critical to address the global climate crisis, such as land-based carbon measurement and potential conservation areas identification. In the recent decade, accessibility to open public datasets on forestry is rapidly increased. However, the availability of finer-resolution of forest cover data is still very limited. As a developing country with numerous rainforests, Indonesia suffered multifaceted threats, particularly deforestation. Thus, precise forest cover data can be useful to fulfill Indonesia’s nationally determined contribution to climate change. In this study, we mapped the national forest cover data for Indonesia using a new object-based image classification approach based on combined Planet-NICFI and Sentinel-2 optical imageries. Our findings had relatively high accuracy compared with the other studies, with the F score ranging from 0.67 to 0.99 and can capture the fragmented forest in fine resolution (i.e., ∼5 m). In addition, we found that Planet-NICFI bands had a higher contribution in predicting forest cover than Sentinel-2 imageries. Utilizing forest cover data for further analyses should be performed to help the achievement of national and global agenda, e.g., related to the FOLU net sink in 2030 and the Global Biodiversity Framework.

An Assessment of Landscape and Land Use/Cover Change and Its Implications for Sustainable Landscape Management in the Chittagong Hill Tracts, Bangladesh

Human-caused environmental change has profoundly impacted resource management and land use patterns in Bangladesh’s Chittagong Hill Tracts. This study used multi-temporal Landsat images from 1998, 2008, and 2018 to analyze land use and land cover changes, particularly those associated with forest cover changes, in Bangladesh’s Chittagong Hill Tracts. Using object-based image classification, Landsat images from 1998, 2008, and 2018 were separated into four categories based on their dominant land use and land cover features: forest, grassland, water bodies, and bare land. Post-classification comparison was used to assess the degree and frequency of change, and this method was further developed to evaluate the balance, fluctuation, and adaptation of forests. In addition, the spatial structure of land cover and temporal trajectories related to changes in forest cover were studied. The CA–Markov chain model was also used to anticipate the 2048 LULC map. The image classification of the years 1998, 2008, and 2018 showed that the overall accuracy was 89.65%, 84.44%, and 86.26%; producer accuracy was 90.00%, 68.75%, and 72.22%; and the Kappa coefficient was 85.68, 82.84, and 76.36, respectively. The results showed that between 1998 and 2018, forest cover increased by 58.03%, transforming grassland to forest; grassland increased by 29.50%, converting bare land to grassland; and forest conversion to grassland was 13.34%. In addition, the result of the landscape metric revealed that during the whole study period, class level indicated a fragmentation of forest, bare land, grassland, and water in the CHT, and landscape level indicated by Shannon’s Diversity Index and Shannon’s Evenness Index showed a slight decrease in the land. Based on the CA–Markov model, forest area is predicted to expand to 9129 Km2 in 2048; however, other land uses (bare land and grassland) continue to decrease. This substantial increase in forest cover results from effective forest management based on community forestry practices and the successful execution of Bangladesh’s national forest strategy. However, as Bangladesh’s population rises, so does the country’s need for lumber/timber. Bangladesh’s government should revise its forest policy to meet the local community’s needs without endangering the forest, and policymakers must take climate change seriously. Our strategy for evaluating the critical indicators of changes in forest cover and pathways of change will aid in connecting these patterns to the dynamics of change, such as deforestation and reforestation. It would therefore serve as a framework for developing effective conservation and management plans for the Chittagong Hill regions in Bangladesh.

Integrated Method for Classifying Medium Resolution Satellite Remotely Sensed Imagery into Land Use Map

There are several remotely sensed images of varied resolutions available. As a result, several classification techniques exist, which are roughly classified as pixel-based and object-based classification methods. Based on the foregoing, this study provided an integrated method of deriving land use from a coarse satellite image. Location coordinates of the land uses were acquired with a handheld Global Positioning System (GPS) instrument as primary data. The study classified the image quantitatively (pixel-based) into built-up, water, riparian, cultivated, and uncultivated land cover classes with no mixed pixels, and then qualitatively into educational, commercial, health, residential, and security land use classes that were conflicting due to spectral similarity. The total accuracy and kappa coefficient of the pixel-based land cover classification were 92.5% and 94% respectively. The results showed that residential land use occupied an area of 5500.01ha, followed by educational (2800.69ha); security (411.27ha); health (133.88ha); and commercial (109.01ha) respectively. The integrated method produces a crisp-appearance like the object-based image classification method. It eliminates the "salt and pepper" appearance that a traditional pixel-based classification would have. The output can be a vector or raster model depending on the purpose for which it is created.

Mapping Sea Grass Coverage of Tanjung Benoa Bali Using Medium Resolution Satellite Imagery Sentinel 2B

Seagrass beds are important components of a coastal ecosystem. This ecosystem serves as the primer producers of the water food chain, habitat for marine biota, produces organic carbon, and indirectly contributes to the economic well-being of coastal communities. However, the ecosystem is vulnerable to damage caused by natural factors and human activities. The objectives of this study were, firstly to identify the distribution of seagrass beds in Tanjung Benoa using Sentinel 2B satellite imagery and secondly to compare classification results from two different approaches namely pixel-based image classification and object-based image classification. Accuracy-test was carried out using field data reference of 195 sample points in the form of a 10 m X 10 m transect. The image pre-processing process was conducted with Bottom of Atmosphere (BoA) correction using the Dark Object Subtraction (DOS) method. Furthermore, the water column correction was performed using the Depth Invariant Index (DII) and the Lyzenga algorithm. The mapping results showed that the area of seagrass beds in the shallow waters of Tanjung Benoa reaches 242.99 ha. There were seven seagrass species in the study area, with an average cover of 75%. The accuracy of object-based image classification was higher than that of pixel-based classification with a difference up to 25% for six classes classification and 15% for two classes classification. Excellent results for classifying seagrasses based on cover density can be obtained when high-resolution satellite imagery and OBIA are combined with the SVM or Fuzzy Logic algorithm.

Assessment of Land Use-Land Cover Changes in Samastipur District of Bihar (India) Using Geo-Informatics

The assessment and analysis of Land Use/Land Cover (LULC) changes are required to identify the land use changes from year to year which plays a critical role in planning and implementation of developmental activities. The present study aims to assess LULC changes in Samastipur district of Bihar using Remote Sensing (RS) and Geographical Information System (GIS). The LULC maps were prepared using LANDSAT-5 and LANDSAT-8 images by adopting object based image classification technique. Assessments of LULC changes were done @ 5 years, @ 10 years and @ 20 years during 2000-2020. The results indicated that the agriculture land coverage increased at high rate during 2000-2005 and 2005-2010; and after that it is increasing at slow rate. The natural vegetation coverage is continuously decreasing during years 2000-2020 while settlement is continuously increasing during this period with notable increase during 2000-2005 and 2015-2020. In the time interval of 10 years (2000-2010), the agriculture land area increased by 22.17%; natural vegetation area decreased by 38.04%; the water-bodies decreased by 46.69%; sand and barren land decreased by 61.27% and settlement area increased by 15.62%. Over the next 10 years (2010-2020), area covered by agriculture land, settlement, water-bodies and sand and barren land increased by 8.05%, 38.30%, 26.27% and 44.65% respectively while area covered by natural vegetation decreased by 75.24%. During time interval of 20 years (2000-2020), agriculture land area and settlement area increased by 32% and 59.91% respectively while natural vegetation, sand and barren land and water-bodies decreased by 84.66%, 43.98% and 32.68% respectively. The analysis of the results indicates that the natural vegetation has decreased at fast rate in the recent years. Therefore, proper attention is required towards stopping of cutting of natural vegetation in the district to save the environment.

Ocher deposit prospecting using object-based image analysis of WorldView-3 VNIR data: A case study in Hormuz Island, southern Iran

Mapping lithological units and conducting mineral exploration in a relatively short time and at a reduced cost, requires high-resolution satellite data and state-of-the-art image processing approaches and methods. In this regard, the current paper aims to test an integration of visible-near infrared imagery from WorldView-3 (WV-3) and object-based classification for mapping potential deposits of ocher in Hormuz Island, southern Iran. A combination of field observations, spectroscopy, microscopic mineralogy, and geochemical-based XRD and XRF analyses were conducted on the samples collected throughout the study area. The reflectance and absorption features of the ocher were extracted from spectroscopic measurements of samples from well-known ocher mines at the area, and were then convolved to the WV-3 bands. Results showed that the spectral characteristics of ocher are governed by iron oxides and clay minerals with a presence of distinct strong absorption and high reflectance features in the 510–625 nm and 630–745 nm wavelength regions, respectively. These features are complemented by molecular vibration processes of water O–H intramolecular stretching and H–O–H bending that generate absorption features in the 1440–1940 nm region. Additional absorption features in the 2210–2300 nm are most likely due to the Al-OH and CO3−2 vibrations. The absorptions centered at 480 nm, 540 nm, and 820 nm correspond to bands 2, 3, and 7 of the WV-3, respectively, whereas the high reflectance feature centered near 700 nm corresponds to band 6. These four bands, which were considered as index bands of ocher in this study, were used to assign the segmentation weights and to create the thresholds during image processing. Brightness, density, compactness, and homogeneity features were the primary factors for selecting the training areas in the index bands. The ocher-rich areas were enhanced by using a two-stepapproach of object-based image analysis (OBIA) and image classification, for suitable threshold selection. The information about the feature variables of the ocher within the image and lithological object hierarchy were obtained to evaluate the features of ocher deposits. Information extracted from the index bands provided an important description of the object features, including mean, standard deviation, minimum and maximum pixel values, hue, saturation, and intensity, while the training areas obtained information from the three known ocher mines. Considering the fixed numerical range of ocher mines as a reference, the fixed numerical range of general and specific features of ocher in index bands of WV-3 were achieved. The numerical ranges of pixels were used for creating threshold conditions when applying the “assign class” algorithm in bands 2, 3, and 7 are 815–975 nm, 930–1052 nm, and 1721–1904 nm, respectively. The presented OBIA approach shows a high potential with an overall accuracy of 88 % to discriminate deposits of ocher based on compatibility between ocher mines and identified ocher-bearing pixels.

Associations of residential greenness with unhealthy consumption behaviors: Evidence from high-density Hong Kong using street-view and conventional exposure metrics.

Residential greenness was theoretically associated with health-related consumption behaviors concerning the socio-ecological model and restoration environment theory, but empirical studies were limited, especially in high-density cities. We examined the associations of residential greenness with unhealthy consumption behaviors (infrequent breakfast consumption, infrequent fruit consumption, infrequent vegetable consumption, alcohol drinking, binge drinking, cigarette smoking, moderate-to-heavy smoking, and heavy smoking) using street-view and conventional greenness metrics in high-density Hong Kong. This cross-sectional study employed survey data from 1,977 adults and residence-based objective environmental data in Hong Kong. Street-view greenness (SVG) was extracted from Google Street View images using an object-based image classification algorithm. Two conventional greenness metrics were used, including normalized difference vegetation index (NDVI) derived from Landsat 8 remote-sensing images and park density derived from a geographic information system database. In the main analyses, logistic regression analyses together with interaction and stratified models were performed with environmental metrics measured within a 1000-m buffer of residence. A standard deviation higher SVG and NDVI were significantly associated with fewer odds of infrequent breakfast consumption (OR=0.81, 95% CI 0.71-0.94 for SVG; OR=0.83, 95% CI 0.73-0.95 for NDVI), infrequent fruit consumption (OR=0.85, 95% CI 0.77-0.94 for SVG; OR=0.85, 95% CI 0.77-0.94 for NDVI), and infrequent vegetable consumption (OR=0.78, 95% CI 0.66-0.92 for SVG; OR=0.81, 95% CI 0.69-0.94 for NDVI). The higher SVG was significantly associated with less binge drinking and the higher SVG at a 400-m buffer and a 600-m buffer were significantly associated with less heavy smoking. Park density was not significantly associated with any unhealthy consumption behaviors. Some of the above significant associations were moderated by moderate physical activity, mental and physical health, age, monthly income, and marital status. This study highlights the potential beneficial impact of residential greenness, especially in terms of street greenery, on healthier eating habits, less binge drinking, and less heavy smoking.

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

The potential of precision viticulture has been highlighted since the first studies performed in the context of viticulture, but especially in the last decade there have been excellent results have been achieved in terms of innovation and simple application. The deployment of new sensors for vineyard monitoring is set to increase in the coming years, enabling large amounts of information to be obtained. However, the large number of sensors developed and the great amount of data that can be collected are not always easy to manage, as it requires cross-sectoral expertise. The preliminary section of the review presents the scenario of precision viticulture, highlighting its potential and possible applications. This review illustrates the types of sensors and their operating principles. Remote platforms such as satellites, unmanned aerial vehicles (UAV) and proximal platforms are also presented. Some supervised and unsupervised algorithms used for object-based image segmentation and classification (OBIA) are then discussed, as well as a description of some vegetation indices (VI) used in viticulture. Photogrammetric algorithms for 3D canopy modelling using dense point clouds are illustrated. Finally, some machine learning and deep learning algorithms are illustrated for processing and interpreting big data to understand the vineyard agronomic and physiological status. This review shows that to perform accurate vineyard surveys and evaluations, it is important to select the appropriate sensor or platform, so the algorithms used in post-processing depend on the type of data collected. Several aspects discussed are fundamental to the understanding and implementation of vineyard variability monitoring techniques. However, it is evident that in the future, artificial intelligence and new equipment will become increasingly relevant for the detection and management of spatial variability through an autonomous approach.

XÁC ĐỊNH PHẠM VI CHE PHỦ CỦA CÂY DỪA NƯỚC TẠI KHU DỰ TRỮ SINH QUYỂN CÙ LAO CHÀM-HỘI AN BẰNG DỮ LIỆU SENTINEL-2A

The mangrove palm grows at the Thu Bon river’s mouth. Mangrove palm has contributed to the conservation of natural ecosystems, and the biological diversity of Cu Lao Cham – Hoi An Biosphere Reserve, as well as the socio-economic development of Hoi An city. However, in recent year, its coverage has been gradually reduced due to human socio-economic activities. This study has used not only the raw bands of the Sentinel-2A image but also some spectral indices have been calculated via the band ratio for classifying land covers. The principal component analysis (PCA) was applied to these all bands to eliminate redundant and noise contributions in data. Mangrove palm was separated from other land cover categories with an object-based image classification approach using Support Vector Machines (SVMs) algorithm because of its high efficiency, performance, and flexibility. The obtained map indicates that mangrove palm principally distributes on the left bank of Thu Bon River with a total area of 147 ha by 2019. The validation, was realized by comparing the classification results with random samples through the visual interpretation of Google Earth high resolution image. The error assessment shows that the overall accuracy is about 88.67% and the Kappa coefficient reaches 0.84. Although there are some differences related to the spatial resolution of Sentinel-2 images, such obtained results may support authorities in decision-making by providing maps able to suggest necessary responses for the conservation of mangrove palms this coastal area.

Characterizing Informal Settlement Dynamics Using Google Earth Engine and Intensity Analysis in Durban Metropolitan Area, South Africa: Linking Pattern to Process

The growing population in informal settlements expedites alterations in land use and land cover (LULC) over time. Understanding the patterns and processes of landscape transitions associated with informal settlement dynamics in rapidly urbanizing cities is critical for better understanding of consequences, especially in environmentally vulnerable areas. The study sought to map and systematically analyze informal settlement growth patterns, dynamics and processes, as well as associated LULC transitions in Durban Metropolitan area, from 2015 to 2021. The study applied an object-based image classification on PlanetScope imagery within the Google Earth Engine (GEE) platform. Further, intensity analysis approach was utilized to quantitatively investigate inter-category transitions at category and transition levels. Thus far, no study of land conversion to and from informal settlement areas in South Africa has exploited both GEE and intensity analysis approaches. The results suggest spatial growth of informal settlements with a total net gain of 3%. Intensity analysis results at category level revealed that informal settlements were actively losing and gaining land area within the period, with yearly gain and loss intensity of 72% and 54%, correspondingly, compared to the uniform intensity of 26%. While the growth of informal settlements avoided water bodies over the studied period, there was an observed systematic process of transition between informal settlements and other urban land. Government policy initiatives toward upgrading informal housing could be attributed to the transitions between informal and other urban settlements. This study illustrates the efficacy of intensity analysis in enhancing comprehension of the patterns and processes in land changes, which aids decision making for suitable urban land upgrading plans in the Durban Metropolitan area.

Street-view and traditional greenness metrics with adults' sitting time in high-density living in Hong Kong: Comparing associations, air pollution and noise roles, and population heterogeneity

BackgroundEmerging evidence suggests neighborhood greenness is associated with physical activity; however, the sitting-specific associations with multi-source greenness metrics remain unclear, especially in high-density cities. ObjectivesThis cross-sectional study examined: 1) the associations of street-view greenness (SVG) and traditional greenness metrics (i.e., Normalized Difference Vegetable Index (NDVI) and park density) with sitting time; 2) the potential moderating/mediating roles of objective/perceived air pollution and perceived roadside noise; and 3) how the associations vary by demographics and socioeconomic status. MethodsInterview survey data of 1977 adults in Hong Kong from 2014 and 2015 was linked to environmental data. Using an object-based image classification algorithm, SVG was derived from Google Street View images, capturing human-viewed street-level greenery. NDVI was derived from Landsat 8 satellite images using the normalized difference between the near-infrared and red bands. Park density was calculated by point density. In the main analyses including regressions, parallel mediation, interaction, and stratified models, the environmental metrics were measured within a 1000-m Euclidean buffer of residence. ResultsSVG and park density were negatively associated with sitting time after adjusting for covariates including physical activity while NDVI was not significantly associated with sitting time, and results were robust with 800–1800 m Euclidean and 1400–1800 m network distance. Greenness-sitting associations were not moderated/mediated by perceived air pollution/roadside noise while SVG-sitting associations were moderated by objective NO2, O3, and PM2.5 and mediated by O3. SVG-sitting associations differed by age, having under-school-aged children, birthplace, education, and occupation type while associations between traditional greenness metrics and prolonged sitting showed no significant population heterogeneity. ConclusionsSVG appears to be more accurate in estimating exposure than traditional metrics to reflect greenness-sitting associations, objective air pollution moderating and mediating roles, and population heterogeneity, which emphasizes the importance of street-level greenness planning for health promotion in terms of reducing sitting time.

An Efficient Ontology Based Machine Learning for Classifying High Definition Satellite Images (Chandrayaan-3)

The analysis of satellite images to determine the kind of land, forest cover, plant type, and other aspects of a particular image has become a standard practice. Object-based image classification for land-cover mapping with remote-sensing data has received a lot of interest in recent years. Several researches have looked into a range of sensors, feature extraction, classifiers, and other important elements throughout the last decade. These data, however, have yet to be gathered into a complete reference on the implications of different guided object-based land-cover categorization methods. To solve this issue, we provide an ontology-based conceptual UKNN classification algorithm in this study. We create an 18-field database from the rit18 database's qualitative and quantitative data in this research. Instead of unexplainable characteristics provided by a CNN, this proposed study uses an ontology-based methodology framework to enable picture classification using features extracted from hyper spectral image data. The image is initially given a Gaussian filter. The suggested UNet K nearest neighbours (UKNN) classifier is utilised to translate the ontology components into image-based parameters, which are then used to improve the classification process. When a typical support vector machine (SVM) is compared to UKNN, we find that UKNN outperforms SVM in classification accuracy

Assessment of Land Use-land Cover Change in Irga River Catchment Using Object-based Image Classification Technique

Land use-land cover (LULC) change analysis is essential for understanding the spatial and temporal change of landscape during a known long period for sustainable management of natural resources. The main objective of this study was to assess land use-land cover change using an object-based image classification technique which is a recent image classification technique with better accuracy than traditional pixel based image classification. The study was conducted in the catchment area of the Irga River, a tributary of the Barakar River, which falls in the Giridih district of Jharkhand (India). The catchment of the study area was delineated using SRTM DEM data (30 m spatial resolution). LANDSAT images (TM and OLI-TIRS) were used to develop the land use- land cover maps of 1997, 2007, and 2017 using object-based image analysis (OBIA). The images were classified and analyzed using ArcGIS and eCognition Developer 64 software. The accuracy of the classified images for each year was assessed by preparing the error matrix and calculating the Kappa coefficient. The overall accuracies of classified images were computed to be 88%, 83% and 91% while Kappa coefficients were found to be 0.8455, 0.7706 and 0.8796 for year 1997, 2007 and 2017 respectively. Over the 20 years (1997-2017), agricultural land increased by 12.23%, settlement increased by 76.62%, wasteland decreased by 39.59%, vegetation increased by 14.83%, water-bodies increased by 26.29%, and river area decreased by 16.66%. The analysis indicated an increasing trend in agricultural land, settlement, and vegetation while decreasing trends in wasteland and river areas. However, no definite trend was observed in the extent of the water-bodies.The results indicated that waste land greatly reduced and converted into settlement and agricultural land in the catchment.