Urban vegetation classification with high-resolution PlanetScope and SkySat multispectral imagery

Abstract

In this study two high-resolution satellite imagery, the PlanetScope, and SkySat were compared based on their classification capabilities of urban vegetation. During the research, we applied Random Forest and Support Vector Machine classification methods at a study area, center of Rome, Italy. We performed the classifications based on the spectral bands, then we involved the NDVI index, too. We evaluated the classification performance of the classifiers using different sets of input data with ROC curves and AUC values. Additional statistical analyses were applied to reveal the correlation structure of the satellite bands and the NDVI and General Linear Modeling to evaluate the AUC of different models. Although different classification methods did not result in significantly differing outcomes (AUC values between 0.96 and 0.99), SVM’s performance was better. The contribution of NDVI resulted in significantly higher AUC values. SkySat’s bands provided slightly better input data related to PlanetScope but the difference was minimal (~3%); accordingly, both satellites ensured excellent classification results.

Deep learning‐based training data augmentation combined with post‐classification improves the classification accuracy for dominant and scattered invasive forest tree species

“…RF is a non-parametric classification algorithm built on binary decision trees; each tree is built on a random part of the data derived from the reference dataset with bootstrapping. 36.7% of the training data is kept for validation to quantify the out-of-bag error and overall accuracy (OA) (Belgiu & Dragut, 2016;Breiman, 1984Breiman, , 2001Richter et al, 2016;Szab o et al, 2021). The number of decision trees and the node size can vary, we applied 100 decision trees, and the node size was set to the square root of the number of variables (according to Hastie et al, 2009).…”

Changes in the Patterns of Population Distribution and Built-Up Areas of the Rural–Urban Fringe in Post-Socialist Context—A Central European Case Study

“…Most of them were GIS methods, because of the size of the territories analyzed and the up-to-date character of resources. Sentinel [49], Landsat [7], other medium or high spatial resolution satellite imagery [50], orthophotos, hyperspectral imagery [51], Corine Land Cover (CLC) and Corine Land Cover Change (CLCC) databases [52][53][54], and Urban Atlas (UA) status and Urban Atlas Change layer [55] are frequently applied resources.…”

Assessing the capacity of high-resolution commercial satellite imagery for grapevine downy mildew detection and surveillance in New York state

“…The proliferation of the commercial high resolution Earth observation industry has reinvigorated research interest in spaceborne ecological surveillance. SkySat and PlanetScope imagery has been used for a range of ecosystem and agricultural applications, including biomass estimation, vegetation classification, plant disease detection, quantifying evapotranspiration, and soil moisture mapping (Kharel et al 2023; Guo et al 2022; Szabó et al 2021; Shi et al 2018; Baloloy et al 2018; Du et al 2022; Raza et al 2020; Aragon et al 2021). The frequent revisit times and fine spatial scale of these platforms enables monitoring diverse specialty crops, including grapevine, despite their smaller, spatially heterogeneous fields (Helman et al 2018; Meyers et al 2020).…”