Object-based image analysis for forest-type mapping in New Hampshire

The use of satellite imagery to classify New England forests is inherently complicated due to high species diversity and complex spatial distributions across a landscape. The use of imagery with high spatial resolutions to classify forests has become more commonplace as new satellite technology become available. Pixel-based methods of classification have been traditionally used to identify forest cover types. However, object-based image analysis (OBIA) has been shown to provide more accurate results. This study explored the ability of OBIA to classify forest stands in New Hampshire using two methods: by identifying stands within an IKONOS satellite image, and by identifying individual trees and building them into forest stands. Forest stands were classified in the IKONOS image using OBIA. However, the spatial resolution was not high enough to distinguish individual tree crowns and therefore, individual trees could not be accurately identified to create forest stands. In addition, the accuracy of labeling forest stands using the OBIA approach was low. In the future, these results could be improved by using a modified classification approach and appropriate sampling scheme more reflective of object-based analysis

Improving the quantification of land cover pressure on stream ecological status at the riparian scale using High Spatial Resolution Imagery

The aim of this paper is to demonstrate the interest of High Spatial Resolution Imagery (HSRI) and the limits of coarse land cover data such as CORINE Land Cover (CLC), for the accurate characterization of land cover structure along river corridors and of its functional links with freshwater ecological status on a large scale. For this purpose, we compared several spatial indicators built from two land cover maps of the Herault river corridor (southern France): one derived from the CLC database, the other derived from HSRI. The HSRI-derived map was obtained using a supervised object-based classification of multi-source remotely-sensed images (SPOT 5 XS-10 m and aerial photography-0.5 m) and presents an overall accuracy of 70 %. The comparison between the two sets of spatial indicators highlights that the HSRI-derived map allows more accuracy in the quantification of land cover pressures near the stream: the spatial structure of the river landscape is finely resolved and the main attributes of riparian vegetation can be quantified in a reliable way. The next challenge will consist in developing an operational methodology using HSRI for large-scale mapping of river corridor land cover,, for spatial indicator computation and for the development of related pressure/impact models, in order to improve the prediction of stream ecological status

Investigation of relationships between linears, total and hazy areas, and petroleum production in the Williston Basin: An ERTS approach

The author has identified the following significant results. ERTS-1 imagery in a variety of formats was used to locate linear, tonal, and hazy features and to relate them to areas of hydrocarbon production in the Williston Basin of North Dakota, eastern Montana, and northern South Dakota. Derivative maps of rectilinear, curvilinear, tonal, and hazy features were made using standard laboratory techniques. Mapping of rectilinears on both bands 5 and 7 over the entire region indicated the presence of a northeast-southwest and a northwest-southeast regional trend which is indicative of the bedrock fracture pattern in the basin. Curved lines generally bound areas of unique tone, maps of tonal patterns repeat many of the boundaries seen on curvilinear maps. Tones were best analyzed on spring and fall imagery in the Williston Basin. It is postulated that hazy areas are caused by atmospheric phenomena. The ability to use ERTS imagery as an exploration tool was examined where petroleum and gas are presently produced (Bottineau Field, Nesson and Antelope anticlines, Redwing Creek, and Cedar Creek anticline). It is determined that some tonal and linear features coincide with location of present production in Redwing and Cedar Creeks. In the remaining cases, targets could not be sufficiently well defined to justify this method

An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

[EN] Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata.This work was supported by the Spanish Ministerio de Economia y Competitividad and FEDER under [grant number CGL2013-46387-C2-1-R]; Fondo de Garantia Juvenil under [contract number PEJ-2014-A-45358].Ruiz Fernández, LÁ.; Recio Recio, JA.; Crespo-Peremarch, P.; Sapena, M. (2018). An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery. Geocarto International. 33(5):443-457. https://doi.org/10.1080/10106049.2016.1265595S44345733

Integrating spatial and spectral information for automatic feature identification in high -resolution remotely sensed images

This research used image objects, instead of pixels, as the basic unit of analysis in high-resolution imagery. Thus, not only spectral radiance and texture were used in the analysis, but also spatial context. Furthermore, the automated identification of attributed objects is potentially useful for integrating remote sensing with a vector-based GIS.;A study area in Morgantown, WV was chosen as a site for the development and testing of automated feature extraction methods with high-resolution data. In the first stage of the analysis, edges were identified using texture. Experiments with simulated data indicated that a linear operator identified curved and sharp edges more accurately than square shaped operators. Areas with edges that formed a closed boundary were used to delineate sub-patches. In the region growing step, the similarities of all adjacent subpatches were examined using a multivariate Hotelling T2 test that draws on the classes\u27 covariance matrices. Sub-patches that were not sufficiently dissimilar were merged to form image patches.;Patches were then classified into seven classes: Building, Road, Forest, Lawn, Shadowed Vegetation, Water, and Shadow. Six classification methods were compared: the pixel-based ISODATA and maximum likelihood approaches, field-based ECHO, and region based maximum likelihood using patch means, a divergence index, and patch probability density functions (pdfs). Classification with the divergence index showed the lowest accuracy, a kappa index of 0.254. The highest accuracy, 0.783, was obtained from classification using the patch pdf. This classification also produced a visually pleasing product, with well-delineated objects and without the distracting salt-and-pepper effect of isolated misclassified pixels. The accuracies of classification with patch mean, pixel based maximum likelihood, ISODATA and ECHO were 0.735, 0.687, 0.610, and 0.605, respectively.;Spatial context was used to generate aggregate land cover information. An Urbanized Rate Index, defined based on the percentage of Building and Road area within a local window, was used to segment the image. Five summary landcover classes were identified from the Urbanized Rate segmentation and the image object classification: High Urbanized Rate and large building sizes, Intermediate Urbanized Rate and intermediate building sizes, Low urbanized rate and small building sizes, Forest, and Water