Seasonal Variation of GPS Accuracy and Precision in Forest Road Mapping

Abdi, Ehsan; Mariv, Hamid Soofi; Mashayekhi, Z.; Deljouei, Azade; Rahbarisisakht, S.

doi:10.31926/but.fwiafe.2022.15.64.1.1

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…As part of the model development procedure, validation is essential [89]. Validation is used in modeling procedures to demonstrate that the model produces accurate results [90]. Previous authors noted that ML is highly precise [84].…”

Section: Ls Susceptibility Modeling and Validation Mapmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Gharechaee

Samani

Sigaroodi

et al. 2023

Land

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Most published studies identify groundwater extraction as the leading cause of land subsidence (LS). However, the causes of LS are not only attributable to groundwater extraction. Other land-use practices can also affect the occurrence of LS. In this study, radar interferometric techniques and machine learning (ML) models were used for the prediction, susceptibility zoning, and prioritization of influential variables in the occurrence of LS in the Bakhtegan basin. The LS rate was characterized by applying an interferometric synthetic aperture radar (InSAR). The recursive feature elimination (RFE) method was used to detect and select the dominant combination of indicators to prepare an LS susceptibility map. Three ML models, including random forest (RF), k-nearest neighbors (KNN), and classification and regression trees (CART), were used to develop predictive models. All three models had acceptable performance. Among the ML models, the RF model performed the best (i.e., Nash–Sutcliffe efficiency, Kling–Gupta efficiency, correlation coefficient, and percent bias metrics of 0.76, 0.78, 0.88, and 0.70 for validating phase, respectively). The analysis conducted on all three ML model outputs showed that high and very high LS susceptibility classes were located on or near irrigated agricultural land. The results indicate that the leading cause of land LS in the study region is not due to groundwater withdrawals. Instead, the distance from dams and the proximity to anticlines, faults, and mines are the most important identifiers of LS susceptibility. Additionally, the highest probability of LS susceptibility was found at distances less than 18 km from synclines, 6 to 13 km from anticlines, 23 km from dams, and distances less than 20 to more than 144 km from mines. The validated methods presented in this study are reproducible, transferrable, and recommended for mapping LS susceptibility in semiarid and arid climate zones with similar environmental conditions.

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Section: Ls Susceptibility Modeling and Validation Mapmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Gharechaee

Samani

Sigaroodi

et al. 2023

Land

View full text Add to dashboard Cite

show abstract

“…A field inventory to measure FCC over 70 square plots (40 × 40 m) was carried out in August 2020. The locations of all plot centers were placed using a Global Positioning System (GPS) unit; since the accuracy of GPS is affected by many factors [60], in this study, we have used a GPS unit with an accuracy of ±1 m (model: Garmin Map 64S C). Previous studies [23,61] have stated that if the tree crown followed a circular shape, the average crown diameter could be calculated by measuring two diameters of the crowns (the largest and smallest diameters of the tree crown).…”

Section: In Situ Measurementsmentioning

confidence: 99%

The Influence of Data Density and Integration on Forest Canopy Cover Mapping Using Sentinel-1 and Sentinel-2 Time Series in Mediterranean Oak Forests

Nasiri

Sadeghi

Moradi

et al. 2022

IJGI

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Forest canopy cover (FCC) is one of the most important forest inventory parameters and plays a critical role in evaluating forest functions. This study examines the potential of integrating Sentinel-1 (S-1) and Sentinel-2 (S-2) data to map FCC in the heterogeneous Mediterranean oak forests of western Iran in different data densities (one-year datasets vs. three-year datasets). This study used very high-resolution satellite images from Google Earth, gridded points, and field inventory plots to generate a reference dataset. Based on it, four FCC classes were defined, namely non-forest, sparse forest (FCC = 1–30%), medium-density forest (FCC = 31–60%), and dense forest (FCC > 60%). In this study, three machine learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), were used in the Google Earth Engine and their performance was compared for classification. Results showed that the SVM produced the highest accuracy on FCC mapping. The three-year time series increased the ability of all ML models to classify FCC classes, in particular the sparse forest class, which was not distinguished well by the one-year dataset. Class-level accuracy assessment results showed a remarkable increase in F-1 scores for sparse forest classification by integrating S-1 and S-2 (10.4% to 18.2% increased for the CART and SVM ML models, respectively). In conclusion, the synergetic use of S-1 and S-2 spectral temporal metrics improved the classification accuracy compared to that obtained using only S-2. The study relied on open data and freely available tools and can be integrated into national monitoring systems of FCC in Mediterranean oak forests of Iran and neighboring countries with similar forest attributes.

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Seasonal Variation of GPS Accuracy and Precision in Forest Road Mapping

Cited by 2 publications

References 27 publications

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

The Influence of Data Density and Integration on Forest Canopy Cover Mapping Using Sentinel-1 and Sentinel-2 Time Series in Mediterranean Oak Forests

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