Semi-automated Object-based Image Analysis Research Articles

Desert landform detection and mapping using a semi-automated object-based image analysis approach

Traditional landform modeling approaches are labor-intensive and time-consuming. We proposed and developed a semi-automated object-based image analysis (OBIA) rule set approach for desert landforms detection and mapping. Sentinel-2 image and digital elevation model (DEM) were acquired for the study area. The multi-resolution segmentation algorithm was employed on the datasets to select relevant features to define appropriate segmentation scales for all landform categories. Object-based rule sets were then employed using spatial (DEM and its derivatives, e.g., slope, aspect, and hillshade) and spectral information for semi-automated classification of the desert landforms. Desert landforms are detected and classified into four classes: saline dome, barchan, playa, and dune. The Fuzzy Synthetic Evaluation (FSE) technique was applied in concert with the error matrix to validate the accuracy of the classification results based on field data, Google Earth, and geological maps. Our findings demonstrated the highest confidence of overall accuracy (OA) 96.21%, 92.58%, 95.99%, and 95.05% respectively, for the saline dome, barchan, playa, and dune. Results showed the strong potential of the rule-based OBIA remote sensing approach for desert landform detection and delineation. Results further demonstrated the efficiency of spatial and spectral features for desert landforms detection and delineation.

March of the mangroves: Drivers of encroachment into southern temperate saltmarsh

Tidal wetlands are experiencing climate change driven shifts in their community composition, distribution, and functions. Mangroves have expanded into saltmarshes where their ranges overlap on five continents. Yet questions remain about the spatial variability of climate change drivers and subsequent local ecological feedbacks on these distributional shifts. Along the southernmost range of mangroves, we studied shifts between Avicennia marina and threatened saltmarsh communities. We used semi-automated object based image analysis to classify wetlands in aerial imagery from 1970 to 2017. Saltmarsh coverage decreased at the expense of mangrove encroachment. Landward migration of saltmarsh was negligible. Unlike other mangrove-saltmarsh ecotone studies, neither changes in seasonal extreme temperatures nor sea level rise were associated with the observed transitions. Rather, mangrove encroachment correlated with increasing annual maximum temperature and was more extensive at sites with lower mean elevations, shorter mean distances to earliest known mangrove-saltmarsh boundary, and higher densities of tidal creeks. Accounting for local variation in mangrove encroachment is informative for managing the provision of desirable ecosystem services. As climate change impacts the southernmost mangrove-saltmarsh ecotone, areas with higher mean elevations and fewer tidal creeks could be prioritized to mitigate the loss of saltmarsh.

Use of a semi-automated object based analysis to map benthic composition, Heron Reef, Southern Great Barrier Reef

ABSTRACTCoral reef scientists and managers require detailed information of reef environments to develop, implement and evaluate management strategies. Maps showing the distribution of corals, seagrass, algae, sand, rubble and rock are an essential component of this information. Using a semi-automated, object-based image analysis (OBIA) approach this study created a benthic community map for Heron Reef (area: approximately 27 km2), Southern Great Barrier Reef, Australia. Benthic cover information was acquired for calibration and validation via georeferenced photograph transects, which coincided with high spatial resolution Quickbird-2 satellite image capture in 2007. Through combination of these data sets and modification of a semi-automated OBIA classification scheme previously developed for seagrass environments to account for the heterogeneous nature of reef benthic communities, a benthic community map was created for Heron Reef with an overall accuracy of 61.6%. The robustness and repeatability of our method provide future opportunity to apply the semi-automated approach to a time series of field and high spatial resolution image data sets to create a time series of benthic community maps for Heron Reef. The results presented in this study therefore open up the opportunity to assess and understand the spatial and temporal dynamics of the reef environment.

An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain

The present paper is an attempt to integrate a semi-automated object-based image analysis (OBIA) classification framework and a cellular automata-Markov model to study land use/land cover (LULC) changes. Land use maps for the Sarab plain in Iran for the years 2000, 2006, and 2014 were created from Landsat satellite data, by applying an OBIA classification using the normalized difference vegetation index, salinity index, moisture stress index, soil-adjusted vegetation index, and elevation and slope indicators. The classifications yielded overall accuracies of 91, 93, and 94% for 2000, 2006, and 2014, respectively. Finally, using the transition matrix, the spatial distribution of land use was simulated for 2020. The results of the study revealed that the number of orchards with irrigated agriculture and dry-farm agriculture in the Sarab plain is increasing, while the amount of bare land is decreasing. The results of this research are of great importance for regional authorities and decision makers in strategic land use planning.

High resolution mapping of development in the wildland-urban interface using object based image extraction.

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m2) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.

Object-Based Image Analysis and Digital Terrain Analysis for Locating Landslides in the Urmia Lake Basin, Iran

The main objective of this research was to establish a semiautomated object-based image analysis (OBIA) methodology for locating landslides. We have detected and delineated landslides within a study area in north-western Iran using normalized difference vegetation index (NDVI), brightness, and textural features derived from satellite imagery (IRS-ID and SPOT-5) in combination with slope and flow direction derivatives from a digital elevation model (DEM) and topographically oriented gray-level cooccurrence matrices (GLCMs). We utilized particular combinations of these information layers to generate objects by applying multiresolution segmentation in a sequence of feature selection and object classification steps. The results were validated by using a landslide inventory database including 109 landslide events. In this study, a combination of these parameters led to a high accuracy of landslide delineation yielding an overall accuracy of 93.07%. Our results confirm the potential of OBIA for accurate delineation of landslides from satellite imagery and, in particular, the ability of OBIA to incorporate heterogeneous parameters such as DEM derivatives and surface texture measures directly in a classification process. The study contributes to the establishment of geographic object-based image analysis (GEOBIA) as a paradigm in remote sensing and geographic information science.

Object-based class modelling for multi-scale riparian forest habitat mapping

Object-based class modelling allows for mapping complex, hierarchical habitat systems. The riparian zone, including forests, represents such a complex ecosystem. Forests within riparian zones are biologically high productive and characterized by a rich biodiversity; thus considered of high community interest with an imperative to be protected and regularly monitored. Satellite earth observation (EO) provides tools for capturing the current state of forest habitats such as forest composition including intermixture of non-native tree species. Here we present a semi-automated object based image analysis (OBIA) approach for the mapping of riparian forests by applying class modelling of habitats based on the European Nature Information System (EUNIS) habitat classifications and the European Habitats Directive (HabDir) Annex 1. A very high resolution (VHR) WorldView-2 satellite image provided the required spatial and spectral details for a multi-scale image segmentation and rule-base composition to generate a six-level hierarchical representation of riparian forest habitats. Thereby habitats were hierarchically represented within an image object hierarchy as forest stands, stands of homogenous tree species and single trees represented by sunlit tree crowns. 522 EUNIS level 3 (EUNIS-3) habitat patches with a mean patch size (MPS) of 12,349.64m2 were modelled from 938 forest stand patches (MPS=6868.20m2) and 43,742 tree stand patches (MPS=140.79m2). The delineation quality of the modelled EUNIS-3 habitats (focal level) was quantitatively assessed to an expert-based visual interpretation showing a mean deviation of 11.71%.

Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach

The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100m2) scales. However, landscape scale (>100km2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142km2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.