Object-based Feature Extraction Research Articles

Exploring Effective Detection and Spatial Pattern of Prickly Pear Cactus (Opuntia Genus) from Airborne Imagery before and after Prescribed Fires in the Edwards Plateau

Over the past century, prickly pear (PP) cactus (e.g., genus Opuntia; subgenus Platyopuntia) has increased on semi-arid rangelands. Effective detection of cacti abundance and spatial pattern is challenging due to the inherent heterogeneity of rangeland landscapes. In this study, high-resolution multispectral imageries (0.21 m) were used to test object-based (OB) feature extraction, random forest (RF) machine learning, and spectral endmember (n-D) classification methods to map PP and evaluate its spatial pattern. We trained and tested classification methods using field-collected GPS location, plant cover, and spectrometry from 288 2 m radius polygons before a prescribed burn and 480 samples after the burn within a 69.2-ha burn unit. The most accurate classification method was then used to map PP distribution and quantify abundance before and after fire. As a case study, we assessed the spatial pattern of mapped PP cover, considering topoedaphic setting and burn conditions. The results showed that the endmember classification method, spectral angle mapper (SAM), outperformed the RF and OB classifications with higher kappa coefficients (KC) (0.93 vs. 0.82 and 0.23, respectively) and overall accuracies (OA) (0.96 vs. 0.91 and 0.49) from pre-fire imagery. KC and OA metrics of post-fire imagery were lower, but rankings among classification methods were similar. SAM classifications revealed that fire reduced PP abundance by 46.5%, but reductions varied by soil type, with deeper soils having greater decreases (61%). Kolmogorov-Smirnov tests indicated significant changes before and after fire in the frequency distribution of PP cover within deeper soils (D = 0.64, p = 0.02). A two-way ANOVA revealed that the interaction of season (pre- vs. post-fire) and soils significantly (p < 0.00001) influenced the spatial pattern of PP patches. Fire also reduced the size and shape of PP patches depending on the topoedaphic settings. This study provides an innovative and effective approach for integrating field data collection, remote sensing, and endmember classification methods to map prickly pear and assess the effects of prescribed fire on prickly pear spatial patterns. Accurate mapping of PP can aid in the design and implementation of spatially explicit rangeland management strategies, such as fire, that can help reduce and mitigate the ecological and economic impacts of prickly pear expansion.

UAV-SfM and Geographic Object-Based Image Analysis for Measuring Multi-Temporal Planimetric and Volumetric Erosion of Arctic Coasts

Monitoring and quantifying the rapid changes along Arctic coasts is becoming increasingly important as above average warming in the Arctic is contributing to increasing rates of erosion leading to dramatic impacts on coastal ecosystems and communities. Understanding the impacts of Arctic coastal erosion on the climate system across large coastal scales requires improvements in measurement techniques. We analyzed two coastal sites in Kugmallit Bay (near Tuktoyaktuk, Northwest Territories, Canada), over a one-week and one-year time interval. Using high-resolution imagery from Unoccupied Aerial Vehicles with Structure from Motion (UAV-SfM), we investigated the influence of unique coastal indicator features on reported planimetric and volumetric measurements and explored the use of Geographic Object Based Image Analysis (GEOBIA) to semi-automate the process of coastal feature extraction. We observed temporally dependent differences between coastal feature movements, planimetrically and volumetrically, and object-based feature extraction accuracy was found to be feature dependent. Our research has made methodological improvements to Arctic coastal measurements, particularly at high spatiotemporal scales, which highlights considerations relevant to broad scale Arctic coastal monitoring and quantification.

Woody Vegetation Cover, Attrition, and Patch Metrics over Eight Decades in Central Texas, United States

Woody plant encroachment into rangeland ecosystems is a widespread and often unwelcomed circumstance affecting rangeland management decisions worldwide. In the rangeland management profession, varying philosophies have been employed in the management of woody plant encroachment. Following World War II, total eradication of woody plant cover was commonly practiced, eventually giving way to a mosaic approach that benefits livestock, wildlife, and recreational objectives, with cover increasing or even stabilizing in many areas. Cultural practices such as land fragmentation, lifestyles not dependent on agricultural income, and shifts in herbivory from predominately browsers to grazers may also be contributing factors. Modern image analysis technologies, such as object-oriented feature extraction and patch metric analyses, can shed light on past paradigm shifts through spectral and textural assessment of modern and historical aerial photography. In this study, woody plant cover and patch metrics were analyzed for a period spanning from 1938 to 1940 through 2018 in the Bennett and Sulphur Creek watersheds of the Lampasas Cut Plain of Central Texas. Object-based feature extraction was used to calculate woody plant cover, and Fragstats was used for landscape patch metrics. Total woody cover was compared with past stewardship paradigms. There was a net decrease of total woody plant cover from 1938 to 1940 through 2018, with variation in between as management paradigms shifted. A pattern of decline, regrowth, and stabilization, like that observed in other research, was noticed for the Bennett Creek watersheds but was not apparent in Sulfur Creek. Patch size/shape varied as well, but fractal patch complexity was relatively stable through time. Raster algebra analysis showed that < 10% of the initial woody cover from 1938 to 1940 remained in 2018, although total cover went through various expansion/reduction phases. This research underscores the importance of long-term datasets and locally based knowledge in the application and interpretation of historical management paradigms.

Road Extraction from Cartosat-2F Multispectral Data with Object-Oriented Analysis

For detection of a road network, high-resolution satellite data have been used following the object-oriented classification approach. We used object-based feature extraction algorithms for detection of road networks from a high resolution Cartosat-2F multispectral data in an Indian city with varying terrain conditions ranging from a compact built-up area to a predominantly vegetated area. The approach involves multi-resolution segmentation (MRS ) and spectral difference segmentation (SDS ) followed by road extraction using fuzzy rule-based algorithm based on various object features, viz. gray-level co-occurrence matrix homogeneity, density, rectangular fit, etc. With overall accuracies ranging from 77.46% to 92% SDS approach performed better than MRS which could afford 60.46% to 75.0% only. However, both of these approaches score over the classical Gaussian maximum likelihood classifier which could register only 50.0% to 68.0% overall accuracy. Furthermore, the maximum overall accuracy was obtained in compact built-up site (85% to 92%) followed by sparsely built-up site (75.0% to 88%) and predominantly vegetated site (60.46% to 77.14%).

Factors Influencing Movement of the Manila Dunes and Its Impact on Establishing Non-Native Species

Unmanned aerial vehicles (UAVs) are being widely used to monitor microtopographic and vegetation changes in coastal habitats using remote sensing techniques. Sand dune habitats are vital ecosystems along the North coast of Humboldt County in California. This study was conducted at the Manila Dunes, west of the Humboldt Coastal Nature Center, in Manila, California. Various factors influence dune movements, including vegetative stabilization and the creation of social trails. The purpose of this paper is to understand the dune movements in relation to social vs. established trails, vegetation density, topography, and also, mapping invasive vs. native species in the Mal-le’l Dunes area of the Humboldt Bay National Wildlife Refuge. A DJI Mavic Pro multicopter small unmanned aerial vehicle (UAV) was used to fly a 22.5-acre plot of the Manila Dunes. The images from this flight were used to create an orthomosaic image using a photogrammetry process (Structure-from-Motion (SfM)). From our analysis, the installation of trails lessened the impact of dune movements. Social trails digitized within the study site were found to have more local movements than the established trails when compared to movements across the entire site. We compared two methods of classification, viz., the object-based feature extraction method and a pixel-based supervised maximum likelihood classification method, in order to identify the best way to classify dune vegetation. In conclusion, this study is useful for providing baseline dune movement information that can aid in informing how trail and infrastructure constructions can be impacted in land management or in areas with dynamic communities of flora and fauna.

Object-based feature extraction for hyperspectral data using firefly algorithm

Object-based classification methods can improve the accuracy of hyperspectral image classification due to the fact that they incorporate spatial information into the classification procedure by assigning neighboring pixels into the same class. In this paper, a new object-based feature extraction method is proposed which makes use of information theory to reduce the Bayes error. In this way, the proposed method exploits higher order statistics for feature extraction which are very effective for non Gaussian data such as hyperspectral images. The criterion to be minimized is composed of three mutual information terms. The first and second terms, consider the maximal relevance and minimal redundancy, respectively, while the third term takes into account the segmentation map containing disjoint spatial regions. To obtain the segmentation map, we apply the firefly clustering algorithm whose fitness function simultaneously considers the intra-distance between samples and their cluster centroids, and inter-distance between centroids of any two clusters. Our experimental results, performed using a variety of hyperspectral scenes, indicate that the proposed framework gives better classification results than some state-of the-art spectral–spatial feature extraction methods.

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.

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.

Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities

Over the last two decades (since ca. 2000), Geographic Object-Based Image Analysis (GEOBIA) has emerged as a new paradigm to analyzing high-spatial resolution remote-sensing imagery. During this time, research interests have demonstrated a shift from the development of GEOBIA theoretical foundations to advanced geo-object-based models and their implementation in a wide variety of real-world applications. We suggest that such a rapid GEOBIA evolution warrants the need for a systematic review that defines the recent developments in this field. Therefore, the main objective of this paper is to elucidate the emerging trends in GEOBIA and discuss potential opportunities for future development. The emerging trends were found in multiple subfields of GEOBIA, including data sources, image segmentation, object-based feature extraction, and geo-object-based modeling frameworks. It is our view that understanding the state-of-the-art in GEOBIA will further facilitate and support the study of geographic entities and phenomena at multiple scales with effective incorporation of semantics, informing high-quality project design, and improving geo-object-based model performance and results.

The application of discriminant analysis for mapping cereals and pasture using object-based features

ABSTRACTHigh mapping accuracies occur where crops differ spectrally (e.g.>90.0%; canola, corn, soybeans) and vice versa (e.g. <75.0%; cereals and pasture). Developing improved mapping methods has been an ongoing priority of Agriculture and Agri-Food Canada (AAFC) remote-sensing science. To this end, this study tests a data-driven object-based classification method using Discriminant Analysis (DA) method for mapping cereals and pasture from satellite data. In this approach, variables (number >400) derived from the image segmentation and object-based feature extraction of multi-date and multi-band optical (RapidEye) and microwave (RADARSAT-2) imagery were applied in a data-driven approach. We use in situ and satellite information collected over two study sites with different levels of heterogeneity (Winnipeg, Brandon) situated in the Canadian Prairies during the 2013 growing season to assess: (a) the type of DA model that most accurately classifies the cereals and pasture cover classes; and (b) how the classification accuracies obtained by the application of this DA model compare to those obtained from more traditional Maximum Likelihood (ML), Decision Tree (DT), and Random Forest (RF) classifications. We found that our DA-based approach was able to map cereals and pastures at our two study sites with the highest accuracies, but these accuracies did not improve significantly with the use of more complex DA model (including priori classification probabilities, more input principle components (PCs), the use of weights proportional to field area). Our results are encouraging for the wider application of the data-driven pre-processing of the inputs to the image classification by DA.

High-resolution Land Cover and Impervious Surface Classifications in the Twin Cities Metropolitan Area with NAIP Imagery

High-resolution land-use and land-cover (lulc) and impervious surface maps were traditionally created by time-consuming field surveys or aerial photo interpretation and digitizing methods. In the United States, the recent availability of 1 m digital color orthoimagery through the National Agriculture Imagery Program (naip) offers an opportunity to update land cover maps for areas of interest. In this study, an object-based feature extraction and a regression tree technique were applied to the NAIP imagery for the Twin Cities Metropolitan Areas (tcma) of Minnesota. An overall accuracy of 74 percent and 95 percent was achieved for the general LULC classification and the impervious surface map, respectively. It was found that both techniques could be used to extract impervious surfaces from the NAIP imagery with relatively high accuracy. The LULC classification results were compared to the 2006 National Land Cover Database (nlcd) and another Landsat-based 2006 classification. Implications for future classification studies were also discussed.

Rule Set Transferability for Object-Based Feature Extraction: An Example for Cirque mapping

AbstractCirques are complex landforms resulting from glacial erosion and can be used to estimate Equilibrium Line Altitudes and infer climate history. Automated extraction of cirquesmay help research on glacial geomorphology and climate change. Our objective was to test the transferability of an object-based rule set for the extraction of glacial cirques, using lidar data and color-infrared orthophotos. In Vorarlberg (W-Austria), we selected one training area with well-developed cirque components to parameterize segmentation and classification criteria. The rule set was applied to three test areas that are positioned in three altitudinal zones. Results indicate that the rule set was successful (81 percent) in the training area and a higher situated area (71 percent). Accuracy decreased in the two lower situated test areas (66 percent and 51 percent). We conclude that rule sets are transferable to areas with a comparable geomorphological history. Yet, significant deviation from the training area requires a different extraction strategy.

Woody vegetation and land cover changes in the Sahel of Mali (1967–2011)

In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmer's fields. Mean woody cover decreased in the plains (−4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.

Estimating potential outdoor water consumption in private urban landscapes by coupling high-resolution image analysis, irrigation water needs and evaporation estimation in Spain

In the past two decades, the Spanish Mediterranean coast has experienced urban and demographic growth through the expansion of low-density residential areas characterized by ornamental gardens, swimming pools, and lawns. To determine the potential outdoor water demand, land cover data were mapped from high-resolution WorldView-2 imagery along a climatic gradient from Mallorca, in the Balearic Islands, to Andalusia in Spain. WorldView-2 imagery was analyzed by adopting object-based feature extraction to generate spatial information layers as additional input features (predictor variables) for supervised per-pixel classification. Swimming pools and gardens planted with turf grass, trees and shrubs were mapped automatically with high levels of overall accuracy, ranging from 88.98% to 96.13%. The resulting GIS (geographic information systems)-ready vector layer was used to calculate net irrigation requirements with the landscape coefficient method and to assess evaporative loss from swimming pools. The method and results can be used by urban planners, water managers, property owners and others to assess the implications of landscaping styles for water demand. Net irrigation requirements (April–September) range from 2335m3/ha to 6330m3/ha compared to 5400m3/ha per year in irrigated agriculture in Spain. Per unit area, the same outdoor landscaping produces a 45.5% higher net irrigation requirement and 21.6% more evaporation from swimming pools along the climatic gradient. The combination of high-resolution image analysis and the landscape coefficient method captures the relevant information for the identification and quantification of outdoor water consumption in private urban landscapes.

High Spatial-Resolution Land Cover Classification and Wetland Mapping over Large Areas Using Integrated Geospatial Technologies

Land Use and Land Cover (LULC) and wetland classification maps are an important prerequisite for many environmental studies. In order to produce accurate LULC and wetland maps at high spatialresolution, a new approach was developed to integrate image classifications, spatial data layers, and analysis methods using Python scripting. Both Maximum Likelihood and Object-based Feature Extraction were adopted into the LULC classification. A spatial analysis approach was applied to wetland mapping based on available wetland inventories and soil data. Python scripts were created and used to automate these processes for each of the 30 reference sites across Minnesota and Wisconsin of the United States, which encompassed the entire study site. Results demonstrated that the proposed method allowed for the integration of geospatial data of varying sources and qualities to produce accurate LULC and wetland maps effectively. The results of accuracy assessment indicated that the classification maps for Minnesota and Wisconsin were of comparable quality. The objectbased classifier extracted LULC effectively from the Wisconsin imagery with acceptable accuracy despite lacking of the NIR spectral band. These maps were used as inputs to create a hydro geomorphicap-proach (HGM) guidebook (Hauer and Smith 1998) for both states (Cook et al. unpublished). The Python-based technique was found to be especially beneficial when dealing with big datasets over large study areas, as it allowed batch processing.

Investigation on pistachio distribution in the mountain regions of northeast Iran by ALOS

Iran supports five different vegetation zones. One of those is the Irano-Touranian zone that is located in the northeast of Iran. This vegetation zone includes arid and semi-arid lands, and its area is about 3.5 million hm2. It supports growth of pistachio (Pistacia vera), a deciduous-broadleaved species, which is one of the ecologically and economically most important native species. In this study, we analyzed three images acquired by ALOS satellite, including 10m resolution multispectral band (AVNIR-2), 2.5 m resolution “Backward” PRISM image, and 2.5 m resolution “Nadir” PRISM image, based on a provided rational polynomial coefficient (RPC). Using the “Backward” and “Nadir” images, a 2.5 m resolution digital elevation model (DEM) was produced. Four methods with AVNIR-2 and PRISM data were used to produce pan-sharpening images and conduct an object-based feature extraction process. Normalized Difference Vegetation Index (NDVI) was used to determine the maximum distribution of pistachio in related elevation. The accuracy of the DEM was tested on 28 ground control points in the pair image as tie points, with the value of parallax error of 0.9027 m. The created elevation map indicated that pistachio trees grow up at 650m above sea level (a.s.l.). The result from NDVI in the related elevation showed the maximum density of pistachio at 800m a.s.l. In addition, the result of feature extraction in the forest showed the area of each target element calculated. The results of this research will improve decision-making and lead to sustainable management in general.

Object-based feature extraction using high spatial resolution satellite data of urban areas

Urban morphology is characterized by a complex and variable coexistence of diverse, spatially and spectrally heterogeneous objects. Built-up areas are among the most rapidly changing and expanding elements of the landscape. Thus, remote sensing becomes an essential field for up-to-date and area-wide data acquisition, especially in explosively sprawling cities of developing countries. The urban heterogeneity requires high spatial resolution image data for an accurate geometric differentiation of the small-scale physical features. This study proposes an object-based, multi-level, hierarchical classification framework combining shape, spectral, hierarchical and contextual information for the extraction of urban features. The particular focus is on high class accuracies and stable transferability by fast and easy adjustments on varying urban structures or sensor characteristics. The framework is based on a modular concept following a chronological workflow from a bottom-up segmentation optimization to a hierarchical, fuzzy-based decision fusion top-down classification. The workflow has been developed on IKONOS data for the megacity Istanbul, Turkey. Transferability is tested based on Quickbird data from the various urban structures of the incipient megacity Hyderabad, India. The validation of both land-cover classifications shows an overall accuracy of more than 81 percent.

Helpful tools for medication screenings

In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmer's fields. Mean woody cover decreased in the plains (−4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.