Object-based Image Analysis Approach Research Articles

Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-Date Aerial Images and Ortho-Mosaics

Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study’s findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks.

Assessing the contribution of RGB VIs in improving building extraction from RGB-UAV images

Buildings are a fundamental component of the built environment, and accurate information regarding their size, location, and distribution is vital for various purposes. The ever-increasing capabilities of unmanned aerial vehicles (UAVs) have sparked an interest in exploring various techniques to delineate buildings from the very high-resolution images obtained from UAV photogrammetry. However, the limited spectral information in UAV images, particularly the number of bands, can hinder the differentiation between various materials and objects. This setback can affect the ability to distinguish between different materials and objects. To address this limitation, vegetative ındices (VIs) have been employed to enhance the spectral strength of UAV orthophotos, thereby improving building classification. The objective of this study is to evaluate the contribution of four specific VIs: the green leaf index (GLI), red-green-blue vegetation index (RGBVI), visual atmospherically resistant index (VARI), and triangular greenness index (TGI). The significance of this contribution lies in assessing the potential of each VI to enhance building classification. The approach utilized the geographic object-based image analysis (GeoBIA) approach and a random forest classifier. To achieve this aim, five datasets were created, with each dataset comprising the RGB-UAV image and a corresponding RGB VI. The experimental results on the test dataset and a post-classification assessment indicated a general improvement in the classification when the VIs were added to the RGB orthophoto.

Target-based building extraction from high-resolution RGB imagery using GEOBIA framework and tabular deep learning model

Extracting building information from remotely sensed images is essential in various geographic and environmental applications. Specifically, there is a growing demand for distinguishing buildings based on roof colors from low-cost but high-resolution RGB images. However, this task presents significant challenges due to the variability in building color, size, and style, as well as their resemblance to nonbuilding objects. The existing GEographic Object-Based Image Analysis (GEOBIA) approaches often struggle with misclassification, mainly because they do not effectively leverage spatial information during the classification stage. Tabular Deep Learning (DL) models have emerged as promising alternatives to shallow classifiers, demonstrating improved performance in recent years. However, their effectiveness in extracting buildings based on roof color has not been thoroughly examined. To address these challenges and improve the accuracy of building extraction, this study proposed a target-based model that incorporates a sequential extraction process using tabular DL models such as Gated Additive Tree Ensemble (GATE), Neural Oblivious Decision Ensemble (NODE). The proposed method adopted a hybrid segmentation approach to effectively segment the image and extract each segment's spectral, textural, geometrical, and contextual features. Non-building objects were systematically eliminated based on their representation within the image. Subsequently, buildings with varying roof colors—brown, black, and white—were extracted. Spatial information was then meticulously integrated into the classification process. The proposed methodology was tested on a UAV image with a spatial resolution of 0.20 m and three visible bands—red, green, and blue—captured in Kingston, Ontario, Canada. This enhanced approach significantly improved precision and overall classification accuracy, demonstrating the effectiveness of incorporating spatial data into the extraction process. This research introduced a framework for incorporating spatial information into tabular models that would serve as a foundation for further improving building extraction accuracy in future studies.

Upscaling net ecosystem CO2 exchanges in croplands: The application of integrating object-based image analysis and machine learning approaches

Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R2 value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties.

Effects of the Architectural Layout of the Sanctuary of Pachacamac (2nd–16th Century CE, Peru) on the Exposure to Rain, Wind, and Solar Radiation from the Morphometric Analysis of Digital Surface Models

Natural events (floods, earthquakes, landslides, etc.) may significantly damage archaeological sites, and therefore reducing their exposure to such events represents a priority for protective and conservation activities. The archaeological Sanctuary of Pachacamac (SP; 2nd–16th century CE; Peru) covers an area of 465 hectares and includes roads, enclosures, huacas with ramps, temples, and palaces located along the central coast of Peru. This area is affected by heavy rain and winds related to the El Niño–Southern Oscillation and to intense solar radiation. We use a 30 cm resolution Digital Surface Model obtained from orthophotogrammetric data and perform a morphometric analysis using geomorphological, hydrological, and climatic quantitative parameters. Our aim is to identify the zones exposed to water flow or stagnation during rainfall, as well as the exposure to winds and solar radiation. The calculated parameters are subsequently processed with an object-based image analysis approach to identify areas with higher climate exposure. We show that the SP architectural layout controls the exposure to water stagnation or flow in the form of rainfall, whereas exposure to wind and solar radiation mainly depends on the topography of an area (e.g., the presence of hills and plains). The methodological approach proposed here may be applied and extended to other archaeological sites.

Mapping and Characterizing Eelgrass Meadows Using UAV Imagery in Placentia Bay and Trinity Bay, Newfoundland and Labrador, Canada

Sustainable coastal social–ecological systems rely on healthy ecosystems known to provide benefits to both nature and people. A key ecosystem found globally is seagrass, for which maps at a scale relevant to inform conservation and management efforts are often missing. Eelgrass (Zostera marina), a species of seagrass found throughout the northern hemisphere, has been declining in Placentia Bay, an ecologically and biologically significant area of Canada’s east coast subject to an increasing human impact. This research provides baseline information on the distribution of eelgrass meadows and their anthropogenic stressors at seven sites of Placentia Bay and three sites of the adjacent Trinity Bay, on the island of Newfoundland. High-resolution maps of eelgrass meadows were created by combining ground-truth underwater videos with unmanned aerial vehicle imagery classified with an object-based image analysis approach. Visual analyses of the imagery and underwater videos were conducted to characterize sites based on the presence of physical disturbances and the semi-quantitative cover of epiphytes, an indication of nutrient enrichment. A total eelgrass area of ~1 km2 was mapped across the 10 sites, with an overall map accuracy of over 80% for 8 of the 10 sites. Results indicated minimum pressures of physical disturbance and eutrophication affecting eelgrass in the region, likely due to the small population size of the communities near the eelgrass meadows. These baseline data will promote the sustainability of potential future coastal development in the region by facilitating the future monitoring and conservation of eelgrass ecosystems.

Radiometric landscape: a new conceptual framework and operational approach for landscape characterisation and mapping

ABSTRACT Landscape mapping has the potential to address some of the most pressing research issues of our time, including climate change, sustainable development, and human well-being. In this paper, we propose an original method that lays the foundations for landscape mapping and overcomes some of the major limitations of existing biophysical methods. Based on the assumption that the primary components of the landscape can be extracted directly from the radiometric information of satellite image time series, this paper presents a new approach to landscape characterization and mapping based solely on remote sensing data. The approach relies on a conceptual model, which links the description, characteristics, structure and functions of the landscape to a set of Remote Sensing-based Essential Landscape Variables (RS-ELVs). The RS-ELVs are then processed according to geographic object-based image analysis (GEOBIA) approach to produce a radiometric landscape map. The model and the remote sensing data processing chain are tested on a case study in central Madagascar (about 13 000 km2) composed of contrasting landscapes resulting from different climatic conditions and agricultural practices. The RS-ELVs are extracted from MODIS image time series for the temporal and spectral variables, and from MODIS and Sentinel-2 images for the texture variables. The parameterization of the segmentation and clustering algorithms is determined by statistical optimization. The final result is a radiometric landscape map in six classes. The landscape classes are then characterized using an independent set of remote sensing variables, a global land cover map and ground observations. The approach successfully identifies and delineates the gradient and major landscape types of the complex region of central Madagascar, confirming our initial hypothesis. The production of such radiometric landscape maps opens the way for integrated territorial development, including the planning and protection of the living environment and human well-being, and the implementation of sectoral policies.

Cocoa plant mapping in Aceh Tamiang regency, Aceh Indonesia: an object-based image analysis approach

Cocoa is one of the leading commodities of plantations that has a vital role in the Indonesian economy. Due to the unavailability of detailed cocoa area data in Aceh Tamiang Regency, mapping was carried out by utilizing remote sensing technology. This study aims to map and determine the distribution of cocoa plants in Aceh Tamiang Regency using the object-based image analysis (OBIA) method and the accuracy test of the OBIA method to map cocoa plants. The object-based image analysis (OBIA) classification results for cocoa plants covered 946.87 ha and cocoa plants spread across 12 districts in Aceh Tamiang Regency. The widest distribution of cocoa is in Karang Baru District, 139.22 ha with a percentage of 14.70%, and the smallest area in Kota Kualasimpang District, covering an area of 6.30 ha with a rate of 0.67%. The results of the accuracy test to map cocoa plants using the OBIA method obtained an overall accuracy value of 95% and a kappa value of 86%.

Comparação de abordagens baseadas em objetos (OBIA) e em pixels em duas estações para a classificação de áreas úmidas costeiras

ABSTRACT Mapping and classifying Coastal Wetlands is important for their conservation. The study aimed to apply Object-Based Image Analysis (OBIA) and pixel-based approaches to answer the questions: (1) which approach is more accurate for classifying Wetlands; (2) Sentinel 1A images improve the classification of Wetlands compared to Sentinel 2A; (3) dual-station sorting has greater potential for sorting Wetlands compared to single-station sorting. We used Sentinel 1 and 2 in single and double seasons (winter and summer) to classify a coastal UA in Rio Grande do Sul. The results show OBIA with greater potential, with accuracy greater than 80%. Sentinel 2 shows higher ranking importance compared to Sentinel 1. Dual season OBIA increased kappa by up to 7% compared to single season. Furthermore, the pixel-based dual season had lower kappa than the OBIA single season. We conclude that OBIA, even at a single station, has greater potential to map coastal AUs.

Leveraging NAIP Imagery for Accurate Large-Area Land Use/land Cover Mapping: A Case Study in Central Texas

Large-area land use land cover (LULC) mapping using high-resolution imagery remains challenging due to radiometric differences between scenes, the low spectral depth of the imagery, landscape heterogeneity, and computational limitations. Using a random forest (RF)- supervised machine-learning algorithm, we present a geographic object-based image analysis approach to classifying a large mosaic of 220 National Agriculture Imagery Program orthoimagery into lulc categories. The approach was applied in central Texas, USA, covering over 6000 km2. We generated 36 variables for each object and accounted for spatial structures of sample data to determine the distance at which samples were spatially independent. The final rf model produced 94.8% accuracy on independent stratified random samples. In addition, vegetation and water indices, the mean and standard deviation of principal components, and texture features improved classification accuracy. This study demonstrates a cost-effective way of producing an accurate multi-class land use/land cover map using high-spatial/low-spectral resolution orthoimagery.

Using Local Knowledge and Remote Sensing in the Identification of Informal Settlements in Riyadh City, Saudi Arabia

Urban planning within Riyadh, the capital of Saudi Arabia, has been impacted by the presence of informal settlements. An understanding of the spatial distribution of these settlements is essential in developing urban policies. This study used remotely sensed imagery to evaluate and characterize informal settlements within the city, both with and without expert knowledge of the study area (defined as expert knowledge, EK). An informal settlement ontology for four study sites within Riyadh City was developed using an analytical hierarchy process (AHP). Local knowledge was translated into a ruleset to identify and map settlement areas using spatial, spectral, textural, and geometric techniques. These were combined with an object-based image analysis (OBIA) approach. The study demonstrated that combining expert knowledge and remotely sensed data can efficiently and accurately identify informal settlements. Two classified images were produced, one with EK, and one without EK, to investigate how a detailed understanding of local conditions could affect the final image classification. Overall accuracy when using EK was 94%, with a kappa coefficient of 89%, while without EK accuracy was 68% (kappa coefficient of 61%). The final OBIA classes included formal and informal settlements, road networks, vacant blocks, shaded areas, and vegetation. This study demonstrated that local expert knowledge and OBIA helpful in urban mapping. It also indicated the value of integrating a local ontological process during digital image classification. This work provided improved techniques for mapping informal settlements in Middle Eastern cities.

Multi resolution appraisal of Cork Harbour estuary: An object based image analysis approach

Lithic sedimentary benthic habitats are often organised by the overlying hydrodynamic regime, generating seabed sedimentary bedforms (SSBs) whose morphology and sediment composition are controlled by the current flow properties including speed, direction, and duration. Furthermore, SSBs can manifest at a variety of spatial scales reflecting different aspects of the hydrodynamic regime that operate at distinct intensities and temporal scales. Acoustic seabed mapping of SSBs permits the quantification of the complex seafloor hydrodynamics without in situ measurements. As such, seabed mapping efforts must determine the minimum spatial resolution required to effectively capture the complete seabed hydrodynamic picture. Here, we provide a semi-automated, object-based image analysis (OBIA) of SSBs occurring over 0.5, 1.0, 1.5, 2.0, and 2.5 m spatial resolutions of multibeam echosounder (MBES) data acquired in Cork Harbour, Ireland, to ascertain the optimum resolution at which to derive current velocity attributes. Estimations of seabed sediment grain size were completed through the angular range analysis (ARA) of co-located 400 kHz multibeam backscatter data. Results show that current flow estimations derived at 0.5, 1.0 and 1.5 m resolutions of MBES bathymetry agree with previous in situ current speed measurements. Moreover, sediment grain size estimations produced during the ARA of the 400 kHz backscatter data displayed a clear pattern of sediment resuspension at each section of the harbour, and this was effectively depicted at 0.5, 1.0, and 1.5 m resolution of MBES bathymetry data. However, current flow estimates derived from 2.0 and 2.5 m resolution failed to portray the short-term current variation induced by the ebb phase of the tide. Therefore, it is recommended that MBES data with a pixel size of >50 % of the wavelength of the SSB observed is insufficient for inclusion in an OBIA workflow.

The application of ResU-net and OBIA for landslide detection from multi-temporal Sentinel-2 images

ABSTRACT Landslide detection is a hot topic in the remote sensing community, particularly with the current rapid growth in volume (and variety) of Earth observation data and the substantial progress of computer vision. Deep learning algorithms, especially fully convolutional networks (FCNs), and variations like the ResU-Net have been used recently as rapid and automatic landslide detection approaches. Although FCNs have shown cutting-edge results in automatic landslide detection, accuracy can be improved by adding prior knowledge through possible frameworks. This study evaluates a rule-based object-based image analysis (OBIA) approach built on probabilities resulting from the ResU-Net model for landslide detection. We train the ResU-Net model using a landslide dataset comprising landslide inventories from various geographic regions, including our study area and test the testing area not used for training. In the OBIA stage, we first calculate land cover and image difference indices for pre-and post-landslide multi-temporal images. Next, we use the generated indices and the resulting ResU-Net probabilities for image segmentation; the extracted landslide object candidates are then optimized using rule-based classification. In the result validation section, the landslide detection of the proposed integration of the ResU-Net with a rule-based classification of OBIA is compared with that of the ResU-Net alone. Our proposed approach improves the mean intersection-over-union of the resulting map from the ResU-Net by more than 22%.

An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers

Evaluating glacial change and the subsequent water stores in high mountains is becoming increasingly necessary, and in order to do this, models need reliable and consistent glacier data. These often come from global inventories, usually constructed from multi-temporal satellite imagery. However, there are limitations to these datasets. While clean ice can be mapped relatively easily using spectral band ratios, mapping debris-covered ice is more difficult due to the spectral similarity of supraglacial debris to the surrounding terrain. Therefore, analysts often employ manual delineation, a time-consuming and subjective approach to map debris-covered ice extents. Given the increasing prevalence of supraglacial debris in high mountain regions, such as High Mountain Asia, a systematic, objective approach is needed. The current study presents an approach for mapping debris-covered glaciers that integrates a convolutional neural network and object-based image analysis into one seamless classification workflow, applied to freely available and globally applicable Sentinel-2 multispectral, Landsat-8 thermal, Sentinel-1 interferometric coherence, and geomorphometric datasets. The approach is applied to three different domains in the Central Himalayan and the Karakoram ranges of High Mountain Asia that exhibit varying climatic regimes, topographies and debris-covered glacier characteristics. We evaluate the performance of the approach by comparison with a manually delineated glacier inventory, achieving F-score classification accuracies of 89.2%–93.7%. We also tested the performance of this approach on declassified panchromatic 1970 Corona KH-4B satellite imagery in the Manaslu region of Nepal, yielding accuracies of up to 88.4%. We find our approach to be robust, transferable to other regions, and accurate over regional (>4,000 km2) scales. Integrating object-based image analysis with deep-learning within a single workflow overcomes shortcomings associated with convolutional neural network classifications and permits a more flexible and robust approach for mapping debris-covered glaciers. The novel automated processing of panchromatic historical imagery, such as Corona KH-4B, opens the possibility of exploiting a wealth of multi-temporal data to understand past glacier changes.

TOWARDS UAV ASSISTED MONITORING OF AQUATIC VEGETATION WITHIN LARGE RIVERS – THE MIDDLE DANUBE (SERBIA)

UAV technologies provide a time- and cost-efficient framework for a variety of environmental monitoring domains. It also increases data resolution and provides new insights into observed objects and phenomena, especially within the difficult-to-access and complex for monitoring aquatic habitats. The objective of this study was to develop UAV-based acquisition and GIS-based image processing guidelines for aquatic macrophyte detection and monitoring in large temperate rivers. According to the European standard CEN EN -14184:2014, the assessment of aquatic macrophytes should be performed using the transect approach. Large rivers, such as the Danube, represent an exception and should be evaluated using 1km transects. Therefore, seven transects of the Middle Danube in Serbia were simultaneously surveyed using traditional field methods and novel UAV technology. UAV images were acquired using RGB and multispectral cameras carried by a fixed-wing drone. The images were processed and orthomosaics were classified using Object Based Image Analysis (OBIA), to create digital GIS maps of the river transects. During the traditional monitoring approach, the relative abundance of 22 macrophyte species was recorded along the transects. Using the UAV technology and OBIA approach eight macrophyte classes were distinguished based on dominant macrophyte taxa or plant life form traits. Aquatic macrophytes were 'almost perfectly' distinguished from the orthomosaics, achieving a high classification accuracy of 96 % / 88 % / 0.84 for RGB and 94 % / 97 % / 0.95 Producers /Users accuracy/Kappa index for the multispectral approach. Individual macrophyte classes accuracy varied between 0.5 and 1 Kappa and were generally higher for the multispectral imagery approach. Although the resolution of the taxonomic data is lower, UAV monitoring provided the necessary spatial context of macrophytes distribution and absolute area occupied by macrophytes. It also provided information on the diversity and distribution of habitats along the river. Therefore, the UAV-assisted monitoring approach described in this study can be effectively integrated into macrophyte monitoring during large river expeditions such as the JDS.

A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs

Wildland conservation efforts require accurate maps of plant species distribution across large spatial scales. High-resolution species mapping is difficult in diverse, dense plant communities, where extensive ground-based surveys are labor-intensive and risk damaging sensitive flora. High-resolution satellite imagery is available at scales needed for plant community conservation across large areas, but can be cost prohibitive and lack resolution to identify species. Deep learning analysis of drone-based imagery can aid in accurate classification of plant species in these communities across large regions. This study assessed whether drone-based imagery and deep learning modeling approaches could be used to map species in complex chaparral, coastal sage scrub, and oak woodland communities. We tested the effectiveness of random forest, support vector machine, and convolutional neural network (CNN) coupled with object-based image analysis (OBIA) for mapping in diverse shrublands. Our CNN + OBIA approach outperformed random forest and support vector machine methods to accurately identify tree and shrub species, vegetation gaps, and communities, even distinguishing two congeneric shrub species with similar morphological characteristics. Similar accuracies were attained when applied to neighboring sites. This work is key to the accurate species identification and large scale mapping needed for conservation research and monitoring in chaparral and other wildland plant communities. Uncertainty in model application is associated with less common species and intermixed canopies.

Smallholder oil palm plantation sustainability assessment using multi-criteria analysis and unmanned aerial vehicles

Oil palm agriculture has caused extensive land cover and land use changes that have adversely affected tropical landscapes and ecosystems. However, monitoring and assessment of oil palm plantation areas to support sustainable management is costly and labour-intensive. This study used an unmanned aerial vehicles (UAV) to map smallholder farms and applied multi-criteria analysis to data generated from orthomosaics, to provide a set of sustainability indicators for the farms. Images were acquired from a UAV, with structure from motion (SfM) photogrammetry then used to produce orthomosaics and digital elevation models of the farm areas. Some of the inherent problems using high spatial resolution imagery for land cover classification were overcome by using texture analysis and geographic object-based image analysis (OBIA). Six spatially explicit environmental metrics were developed using multi-criteria analysis and used to generate sustainability indicator layers from the UAV data. The SfM and OBIA approach provided an accurate, high-resolution (~5 cm) image-based reconstruction of smallholder farm landscapes, with an overall classification accuracy of 89%. The multi-criteria analysis highlighted areas with lower sustainability values, which should be considered targets for adoption of sustainable management practices. The results of this work suggest that UAVs are a cost-effective tool for sustainability assessments of oil palm plantations, but there remains the need to plan surveys and image processing workflows carefully. Future work can build on our proposed approach, including the use of additional and/or alternative indicators developed through consultation with the oil palm industry stakeholders, to support certification schemes such as the Roundtable on Sustainable Palm Oil (RSPO).

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.

An object-based image analysis approach for comparing tree detection from satellite imagery at different scales; A case study in Sukumba Mali

This paper combines an object-based image analysis (OBIA) approach, with markov-random field based-super resolution mapping (MRF-SRM) technique to extract individual tree objects from satellite imagery. Extraction of individual trees in urban and developing cities using satellite imageries has been quite challenging and a subject of active research with many approaches to it. The study area is a part of Sukumba village located in Koutiala district, Mali. The aim is to evaluate the performance comparison of the combined MRF-SRM approach, which was demonstrated for each combination of scale factor and class separability. Due to varying contrast sensitivity, there exist a general limitation in the spatial distribution of land cover data sets derived from most coarse and fine resolution satellite imageries. A 10 m spatial resolution Sentinel-2 and 2 m spatial resolution Worldview-3 satellite images were used for the study. First, the pixel based MRF-SRM classification technique of Tolpekin and Stein, 2009 was applied on both images. Subsequently, the classified SRM thematic results were partitioned into objects (segments) using region growing segmentation algorithm as post a classification procedure. Finally, validation and comparison of the resulting tree objects was done using an object based image analysis approach to determine existential (TP), extensional (FP) and positional (PA) accuracy. For Sentinel-2 image and out of 1787 reference objects, 1214 (TP) was detected and 573 (FP) was undetected. The PA is 6.504 m out of 10 m, total detection error is 0.467 m and the final detection accuracy is 68%. Similarly, for Worldview-3 image and out of 1787 reference objects, 1772 (TP) was detected and 15 (FP) was undetected. The PA is 1.714 m out of 2 m, total detection error is 0.318 m and the final detection accuracy is 99%. The successful approach used in this study will support the capacity of monitoring agricultural change in data sparse regions and developing countries.

Mapping environmentally sustainable urban development within six US cities through object-based image change analysis of aerial orthoimagery

The rapid expansion of cities and continuous urban population growth underscores a need for sustainable urban development. Sustainable development is that which addresses human needs, contributes to well-being, is economically viable, and utilizes natural resources at a degree sustainable by the surrounding environmental systems. Urban green spaces, green roofs, and solar panels are examples of environmentally sustainable urban development (ESUD), or development that focuses on environmental impact, but also presents the potential to achieve social and economic sustainability. The aim of this study was to map and compare amounts of ESUD c. 2010 and c. 2019 through an object-based image analysis (OBIA) approach using National Agricultural Imagery Program (NAIP) aerial orthoimagery for six mid- to large-size cities in the USA.The results of this study indicate a hybrid OBIA and manual interpretation approach applied to NAIP orthoimagery may allow for reliable mapping and areal estimation of urban green space and green roof changes in urban areas. The reliability of OBIA-only mapping and estimation of areal extents of existing green roofs, and new and existing solar panels, is inconclusive due to low mapping accuracy and coarse spatial resolution of aerial orthoimagery relative to some ESUD features. The three urban study areas in humid continental climate zones (Dfa) were estimated to have greater areal extent of new and existing urban green space and existing green roofs, but less areal extent of new green roofs and existing solar panels compared to the three study areas in humid subtropical climate zones (Cfa).