Abstract
With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.
Highlights
The OOB error obtained for the PB, Pixel-based including the image textural information (PBT), and OOG at decision trees (DT) = 100 results in a similar range of values (PB = 0.198, PBT = 0.199, OOG = 0.185) while the OB approach (OBP) approach showed the best performance producing the lowest OOB error (OBP = 0.151)
From the user’s perspective, the OBP approach significantly improves the accuracy of user’s accuracy (UA) of BSR, GRS, BLF, and SHR classes
The work aimed to compare and assess pixel-based and object-based approaches based on Random Forest (RF) to classify the Land use land cover (LULC) of the Maiella National Park using Landsat 8 data
Summary
Land use land cover (LULC) maps represent an essential tool in managing natural resources and are a crucial component supporting strategies for monitoring environmental changes [1]. Changes related to LULC have led to the reduction of various vital resources, like water, soil, and vegetation [4], biodiversity and related ecosystem services [5], and due to rapid, extensive, and intense mutations, natural reserves both locally, regionally, and nationally are in grave. Diachronic LULC maps and time-series of remote sensing (RS) data are essential to understand and measure these dynamics across space and time [7,8], considering landscape gradients [9,10]
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