Showing papers by "Qi Chen published in 2021"
TL;DR: The Orbita hyperspectral image satellite (OHSI) as discussed by the authors is a new HSI satellite in orbit with the highest spectral and spectral properties, which demonstrates a great potential for identifying crops.
Abstract: Hyperspectral remote sensing demonstrates a great potential for identifying crops. Orbita hyperspectral image satellite (OHS) is a new hyperspectral satellite in orbit with the highest spectral and...
9 citations
TL;DR: In this paper, the authors examined overtourism indicators at Kailua Beach Park in Hawaiʻi and demonstrated a systematic approach to assess carrying capacity by pairing descriptive indicators with more commo...
Abstract: This article examines overtourism indicators at Kailua Beach Park in Hawaiʻi, and demonstrates a systematic approach to assessing carrying capacity by pairing descriptive indicators with more commo...
7 citations
TL;DR: Kukui was an important element to indigenous Hawaiian agroforestry and retained some of its importance throughout Hawai'i's history as mentioned in this paper, however, kukui is declining, having lost an average of ∼58% of total canopy cover over the last 70 years.
Abstract: Kukui was an important element to indigenous Hawaiian agroforestry and retained some of its importance throughout Hawai‘i's history. We examine the historical ecology and trends of kukui, including a review of the ethnobotany. We use current and historical remote imagery to map kukui canopy on the five largest Hawaiian Islands. Kukui is still widespread through the state, being a significant component in many novel low-land forests. However, kukui is declining, having lost an average of ∼58% of total canopy cover over the last 70 years. Spatial trends suggest that kukui likely did not spread much following the large-scale shifts in Hawaiian socio-ecosystems that accompanied the arrival of colonial powers. We suggest that the footprint of kukui in Hawai‘i closely approximates the extent of indigenous agroforestry and forest alteration.
4 citations
TL;DR: In this article, the authors evaluate and compare the performance of different machine learning models for estimating plot-level forest AGB, including support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN).
Abstract: Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, and RBF-ANN & P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF & P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF & P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps.
4 citations