Abstract
Remote sensing technology has been used widely in mapping forest and wetland communities, primarily with moderate spatial resolution imagery and traditional classification techniques. The success of these mapping efforts varies widely. The natural communities of the Laurentian Mixed Forest are an important component of Upper Great Lakes ecosystems. Mapping and monitoring these communities using high spatial resolution imagery benefits resource management, conservation and restoration efforts. This study developed a robust classification approach to delineate natural habitat communities utilizing multispectral high-resolution (60 cm) National Agriculture Imagery Program (NAIP) imagery data. For accurate training set delineation, NAIP imagery, soils data and spectral enhancement techniques such as principal component analysis (PCA) and independent component analysis (ICA) were integrated. The study evaluated the importance of biogeophysical parameters such as topography, soil characteristics and gray level co-occurrence matrix (GLCM) textures, together with the normalized difference vegetation index (NDVI) and NAIP water index (WINAIP) spectral indices, using the joint mutual information maximization (JMIM) feature selection method and various machine learning algorithms (MLAs) to accurately map the natural habitat communities. Individual habitat community classification user’s accuracies (UA) ranged from 60 to 100%. An overall accuracy (OA) of 79.45% (kappa coefficient (k): 0.75) with random forest (RF) and an OA of 75.85% (k: 0.70) with support vector machine (SVM) were achieved. The analysis showed that the use of the biogeophysical ancillary data layers was critical to improve interclass separation and classification accuracy. Utilizing widely available free high-resolution NAIP imagery coupled with an integrated classification approach using MLAs, fine-scale natural habitat communities were successfully delineated in a spatially and spectrally complex Laurentian Mixed Forest environment.
Highlights
The appropriateness of the input datasets was evaluated based on the overall accuracy (OA) and kappa (k) of the test samples (5801 pixels)
OA indicates the ratio between the total number of pixels and the total number of accurately classified pixels, and the k is a measure of the agreement between the accurately classified data and the reference data [13]
The lowest accuracy occurred when only the National Agriculture Imagery Program (NAIP) bands were used for classification and supports the need for ancillary data incorporation
Summary
An ecosystem is defined as “a community of organisms and their physical environment interacting as an ecological unit” [1]. Land cover grouped into types and systems by resource managers led Arthur Tansley [2] to coin the term “ecosystem”. Ecosystems with spatially related features are considered higher-order, larger-scale ecosystems, referred to as “macroecosystems” [3]. When ecosystems are viewed as macroscale patterns, they can be divided into ecoregions [4]. The term “ecoregion” was first proposed by Orie Loucks [5], a Canadian forest researcher. Ecoregions play an important role in resource conservation and management by enabling consideration of the natural process and patterns of communities which provide ecosystem sustainability in a particular region [6].
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