Object-oriented Classification Approach Research Articles

Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal

The expansion of cities entails the abandonment of forest and agricultural lands, and these lands’ conversion into urban areas, which results in substantial impacts on ecosystems. Monitoring these changes and planning urban development can be successfully achieved using multitemporal remotely sensed data, spatial metrics, and modeling. In this paper, urban land use change analysis and modeling was carried out for the Concelhos of Setúbal and Sesimbra in Portugal. An existing land cover map for the year 1990, together with two derived land cover maps from multispectral satellite images for the years 2000 and 2006, were utilized using an object-oriented classification approach. Classification accuracy assessment revealed satisfactory results that fulfilled minimum standard accuracy levels. Urban land use dynamics, in terms of both patterns and quantities, were studied using selected landscape metrics and the Shannon Entropy index. Results show that urban areas increased by 91.11% between 1990 and 2006. In contrast, the change was only 6.34% between 2000 and 2006. The entropy value was 0.73 for both municipalities in 1990, indicating a high rate of urban sprawl in the area. In 2006, this value, for both Sesimbra and Setúbal, reached almost 0.90. This is demonstrative of a tendency toward intensive urban sprawl. Urban land use change for the year 2020 was modeled using a Cellular Automata based approach. The predictive power of the model was successfully validated using Kappa variations. Projected land cover changes show a growing tendency in urban land use, which might threaten areas that are currently reserved for natural parks and agricultural lands.

Pixel, object and hybrid classification comparisons

The choice of the best classification approach for thematic map generation relies on many factors, such as image resolution and minimum mapping unit. The generalized GIS-ready products derived from the results of pixel-based approaches and the availability of higher-resolution imagery have directed research towards object-based classification approaches. In this paper we present the superior performance of a hybrid methodology that combines the results of automatic segmentation with the land cover information derived from a pixel classification by means of the Winner Takes All (WTA) algorithm. Land use and land cover results obtained through this hybrid classification approach are compared with those of a One Against All (OAA) object-oriented classification approach.

Mapping spatial variation in acorn production from airborne hyperspectral imagery

Masting is a well-marked variation in yields of oak forests. In Japan, this phenomenon is also related to wildlife management and oak regeneration practices. This study demonstrates the capability of integrating remote sensing techniques into mapping spatial variation of acorn production. The hyperspectral images in 72 wavelengths (407–898 nm) were acquired over the study area ten times over a period of three years (2003–2005) during the early growing season of Quercus serrata using the Airborne Imaging Spectrometer Application (AISA) Eagle System. With the canopy spectral reflectance values of 22 sample trees extracted from the images, yield estimation models were developed via multiple linear regression (MLR) analyses. Using the object-oriented classification approach in eCognition, canopies representative of individual oak trees (Q. serrata) were identified from the corresponding hyperspectral imagery and combined with the fitted estimation models developed, acorn yield over the entire forest were estimated and visualized into maps. Three estimation models, obtained for June 27 in 2003, July 13 in 2004 and June 21 in 2005, showed good performance in acorn yield estimation both for the training and validation datasets, all with R2 > 0.4, p < 0.05 and RRMSE < 1 (the relative root mean square of error). The present study shows the potential of airborne hyperspectral imagery not only in estimating acorn yields during early growing seasons, but also in identifying Q. serrata from other image objects, based on which of the spatial distribution patterns of acorn production over large areas could be mapped. The yield map can provide within-stand abundance and valuable information for the size and spatial synchrony of acorn production.

Improvement of land-use classification using object-oriented and fuzzy logic approach

Traditional classification approaches are all pixel-based and do not utilize the spatial and context information of an object and its surroundings, which has potential to further enhance digital image classification. Instead of pixel-based, pixels groupings and object segmentation offers more innovative techniques to image classification. In this study, land cover types in the Klang valley, Malaysia were analyzed to compare classification accuracy between the pixel-based and the object-oriented image classification approaches. Landsat 7 ETM+ with six spectral bands was used for the land cover classification. In the pixel-based image classification, supervised classification was performed using the maximum likelihood classifier. On the other hand, the object-oriented image classification was performed using the combination of object segmentation using fuzzy dimension techniques. The selected parameters for image segmentation were: scale parameter 15, homogeneity composition criterion (color 0.7 and shape 0.3), shape criterion (smoothness 0.9 and compactness 0.1). Fuzzy dimension functions were devised to classify the segmented image objects. The classification results showed that the object-oriented cum fuzzy logic approach was superior to that of the pixel-based supervised classification. The former has achieved higher overall, producer and user accuracies for most of the land cover classes compared to those of the latter. In addition, the accuracy of the former has met the requirements of international standard for digital mapping with overall accuracy exceeding 85%; Kappa value above 0.85 while accuracy differences among the classes were kept minimal.

Potential and constraints of Unmanned Aerial Vehicle technology for the characterization of Mediterranean riparian forest

Unmanned Aerial Vehicle (UAV) technology provides potential for very high spatial resolution (<25 cm) mapping of relatively large areas at a user-defined re-survey frequency. In a riparian context, UAV technology provides a mechanism for riparian managers to (a) quantify riparian terrain and vegetation units and (b) identify standing dead wood and canopy mortality. In this study a paraglider UAV was used to survey 174 ha at 6.8–21.8 cm ground resolution. Pixel-based and object-oriented classification approaches were used at the scale of a single image and a channel mosaic. Significant potential was demonstrated: vegetation units were classified with an accuracy of kappa = 0.79 and standing dead wood units were identified with an average accuracy with respect to omission and commission errors of 80% and 65%, respectively. Work across multiple images identified that major constraints currently result from factors such as illumination conditions and sensor movement during flight, which create variations in spatial resolution and radiometry. It is expected that with further methodological refinement and more complex methods of automated radiometric correction UAV technology can provide the flexibility to rapidly produce very high resolution map products to aid riparian management.

A multi-resolution satellite imagery approach for large area mapping of ericaceous shrubs in Northern Quebec, Canada

Invasive ericaceous shrubs (e.g. Kalmia angustifolia, Rhododendron groenlandicum, Vaccinium spp.) may reduce the regeneration and early growth of black spruce (Picea mariana) seedlings, the most economically important boreal tree species in Quebec. Our study focused, therefore, on developing a method for mapping ericaceous shrubs from satellite images. The method integrates very high resolution satellite imagery (IKONOS) to guide classifiers applied to medium resolution satellite imagery (Landsat-TM). An object-oriented image classification approach was applied using Definiens eCognition software. An independent ground survey revealed 80% accuracy at the very high spatial resolution. We found that the partial use (70%) of classified polygons derived from the IKONOS images were an effective way to guide classification algorithms applied to the Landsat-TM imagery. The results of this latter classification (78.4% overall accuracy) were assessed by the remaining portion (30%) of unused very high resolution classified polygons. We further validated our method (65.5% overall accuracy) by assessing the correspondence of an ericaceous cover classification scheme done with a Landsat-TM image and results of our ground survey using an independent set of 275 sample plots. Discrimination of ericaceous shrub cover from other land cover types was achieved with precision at both spatial resolutions with producer accuracies of 87.7% and 79.4% from IKONOS and Landsat, respectively. The method is weaker for areas with sparse cover of ericaceous shrubs or dense tree cover. Our method is adapted, therefore, for mapping the spatial distribution of ericaceous shrubs and is compatible with existing forest stand maps.

Mapping urban and peri-urban agriculture using high spatial resolution satellite data

In rapidly changing peri-urban environments where biophysical and socio- economic processes lead to spatial fragmentation of agricultural land, remote sensing offers an efficient tool to collect land cover/land use (LCLU) data for decision-making. Compared to traditional pixel-based approaches, remote sensing with object-based classification methods is reported to achieve improved classification results in complex heterogeneous landscapes. This study assessed the usefulness of object-oriented analysis of Quickbird high spatial resolution satellite data to classify urban and peri-urban agriculture in a limited peri-urban area of Hanoi, Vietnam. The results revealed that segmentation was essential in developing the object-oriented classification approach. Accurate segmentation of shape and size of an object enhanced classification with spectral, textural, morphological, and topological features. A qualitative, visual comparison of the classification results showed successful localisation and identification of most LCLU classes. Quantitative evaluation was conducted with a classification error matrix reaching an overall accuracy of 67% and a kappa coefficient of 0.61. In general, object-oriented classification of high spatial resolution satellite data proved the promising approach for LCLU analysis at village level. Capturing small-scale urban and peri-urban agricultural diversity offers a considerable potential for environmental monitoring. Challenges remain with the delineation of field boundaries and LCLU diversity on more spatially extensive datasets.

Integrative Assessment of Informal Settlements Using VHR Remote Sensing Data—The Delhi Case Study

In contrast to the last century, where more people used to live in rural areas, at present, more than half of the world's population lives in urban settlements. Hence, the 21st century is the century of the cities and of urbanization. The rapid urbanization process experienced by the majority of developing countries during the last few decades has resulted in fundamental changes to the environment and to the social structure. In most of the megacities that have grown to unprecedented size, the pace of urbanization has far exceeded the growth of necessary infrastructure and services. In order to carry out the urban planning and development tasks necessary to improve the living conditions for the poorest world-wide, a detailed spatial data basis is required. Due to the high dynamics of megacities, traditional methods such as statistical analyses or fieldwork are limited to capture the urban process. Remote sensing provides the opportunity to monitor spatial patterns of urban structures with high spatial and temporal resolution. The present study investigates the potential to use very high-resolution (VHR) remote sensing data to identify urban structures and dynamics within Delhi, India. The paper presents a semi-automated, object-oriented classification approach which allows for the identification of informal settlements within the urban area. In order to provide indicators to identify socio-economic structures and their dynamics, the image classification results are embedded in an integrative analysis concept. Information on population and water related parameters are derived. This is understood to be a first step to the development of indicators which will help to identify and understand the different shapes, actors, and processes in megacities.

Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data.

Remote sensing techniques have been shown effective for large-scale damagesurveys after a hazardous event in both near real-time or post-event analyses. The paperaims to compare accuracy of common imaging processing techniques to detect tornadodamage tracks from Landsat TM data. We employed the direct change detection approachusing two sets of images acquired before and after the tornado event to produce a principalcomponent composite images and a set of image difference bands. Techniques in thecomparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment isbased on Kappa coefficient calculated from error matrices which cross tabulate correctlyidentified cells on the TM image and commission and omission errors in the result. Overall,the Object-oriented Approach exhibits the highest degree of accuracy in tornado damagedetection. PCA and Image Differencing methods show comparable outcomes. Whileselected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approachperforms significantly better with 15-20% higher accuracy than the other two techniques.

Modeling Residential Lawn Fertilization Practices: Integrating High Resolution Remote Sensing with Socioeconomic Data

This article investigates how remotely sensed lawn characteristics, such as parcel lawn area and parcel lawn greenness, combined with household characteristics, can be used to predict household lawn fertilization practices on private residential lands. This study involves two watersheds, Glyndon and Baisman's Run, in Baltimore County, Maryland, USA. Parcel lawn area and lawn greenness were derived from high-resolution aerial imagery using an object-oriented classification approach. Four indicators of household characteristics, including lot size, square footage of the house, housing value, and housing age were obtained from a property database. Residential lawn care survey data combined with remotely sensed parcel lawn area and greenness data were used to estimate two measures of household lawn fertilization practices, household annual fertilizer nitrogen application amount (N_yr) and household annual fertilizer nitrogen application rate (N_ha_yr). Using multiple regression with multi-model inferential procedures, we found that a combination of parcel lawn area and parcel lawn greenness best predicts N_yr, whereas a combination of parcel lawn greenness and lot size best predicts variation in N_ha_yr. Our analyses show that household fertilization practices can be effectively predicted by remotely sensed lawn indices and household characteristics. This has significant implications for urban watershed managers and modelers.

Detection of burned peat swamp forest in a heterogeneous tropical landscape: A case study of the Klias Peninsula, Sabah, Malaysia

Remote sensing plays an important role in providing an estimate of burned areas for fire management. However, detecting burned areas in the tropics is a difficult task due to the heterogeneous landscape and rapidly changing land cover. The Klias Peninsula contains the largest peat swamp forest in the northern end of Borneo, but was severely damaged by the 1998 El-Niño fires. We examined the use of three vegetation indices (Normalized Burn Ratio (NBR), Normalized Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI)) using an image differencing technique to detect burned area. The best threshold was detemined with a simple partitioning technique based on statiscal measures. Additional noise removal operations were necessary to improve the detection results. The NBR differencing is the superior method, but the burned area detected by the NDWI differencing is comparable to that of the NBR differencing. A prefire land cover map was generated using the object oriented classification approach to analyze the burned forest types. The analysis indicates that half and one-tenth of the good and degraded peat swamp forests in the peninsula were burned, respectively. The role of the Binsuluk Forest Reserve as a protection forest reserve was severely impaired, whereas only a small portion of the Klias Forest Reserve was affected by the fires.

A Multi-scale Segmentation Approach to Mapping Seagrass Habitats Using Airborne Digital Camera Imagery

The purpose of this study was to map the areal extent and density of submerged aquatic vegetation, principally the seagrasses, Zostera marina and Ruppia maritima, as part of ongoing monitoring for the Barnegat Bay, New Jersey National Estuary Program. We examine the utility of multiscale image segmentation/object-oriented image classification using the eCognition software to map seagrass across our 36,000 ha study area. The multi-scale image segmentation/ object oriented classification approach closely mirrored our conceptual model of the spatial structure of the seagrass habitats and successfully extracted the features of ecological interest. The agreement between the mapped results and the original field reference was 68 percent (Kappa � 56.5 percent) for the four category map and 83 percent (Kappa � 63.1 percent) for the presence/absence map; the agreement between the mapped results and the independent reference data was 71 percent (Kappa � 43.0 percent) for a simple presence/absence map. While the aerial digital camera imagery employed in this study had the advantage of flexible acquisition, suitable image scale, fast processing return time, and comparatively low cost, it had inconsistent radiometric response from image to image. This inconsistency made it difficult to develop a rule-based classification that was universally applicable across the 14 individual image mosaics. However, within the individual scene mosaics, using the eCognition software in a “manual classification” mode provided a flexible and time effective approach to mapping seagrass habitats.

Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery

Due to the ability of the NOAA-AVHRR sensor to cover a wide area and its high temporal frequency, it is possible to quickly obtain a general overview of the prevailing situation over a large area of terrain and, more specifically, quickly assess the damage caused by a recent large forest fire by mapping the extent of the burned area. The aim of this work was to map a large forest fire that recently took place on the Spanish Mediterranean coast using innovative image classification techniques and low spatial resolution imagery. The methodology involved developing an object-based classification model using spectral as well as contextual object information. The burned area map resulting from the image classification was compared with the fire perimeter provided by the Catalan Environmental Department in terms of spatial overlap and size in order to determine to what extent they were compatible. Results of the comparison indicated a high degree (≈90%) of spatial agreement. The total burned area of the classified image was found to be 6900 ha, compared to a fire perimeter of 6000 ha produced by the Catalan Environmental Department. It was concluded that, although the object-oriented classification approach was capable of affording very promising results when mapping a recent burn on the Spanish Mediterranean coast, the method in question required further assessment to ascertain its ability to map other burned areas in the Mediterranean.