Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine

Xie, Bo; Cao, Chunxiang; Xu, Min; Yang, Xinwei; Duerler, Robert Shea; Bashir, Barjeece; Huang, Zhenhong; Wang, Kaimin; Chen, Yiyu; Guo, Heyi

doi:10.3390/rs14092051

Cited by 3 publications

(1 citation statement)

References 67 publications

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“…The method is helpful for the distribution of forest resources that are densely covered in the specified locations. In a further expansion of the work, suggest improving the accuracy by exploring more spatial resolution images and modelling new, improved accuracy [14]. The author discussed the remote sensing images-based water use efficiency in the specified region.…”

Section: Background Studymentioning

confidence: 99%

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Vijayalakshmi¹,

Kumar²

2023

Intelligent Automation &Amp; Soft Computing

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Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions. It is challenging to determine vegetation using traditional map classification approaches. The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties. It is more demandable to determine the multiple spectral analyses for improving the accuracy of vegetation mapping through remotely sensed images. The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping. The architecture comprises three approaches, feature-based approach, region-based approach, and texture-based approach for classifying the vegetation area. The novel Deep Meta fusion model (DMFM) is created with a unique fusion framework of residual stacking of convolution layers with Unique covariate features (UCF), Intensity features (IF), and Colour features (CF). The overhead issues in GPU utilization during Convolution neural network (CNN) models are reduced here with a lightweight architecture. The system considers detailing feature areas to improve classification accuracy and reduce processing time. The proposed DMFM model achieved 99% accuracy, with a maximum processing time of 130 s. The training, testing, and validation losses are degraded to a significant level that shows the performance quality with the DMFM model. The system acts as a standard analysis platform for dynamic datasets since all three different features, such as Unique covariate features (UCF), Intensity features (IF), and Colour features (CF), are considered very well.

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Section: Background Studymentioning

confidence: 99%

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Vijayalakshmi¹,

Kumar²

2023

Intelligent Automation &Amp; Soft Computing

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Estimation of forest canopy density through Geospatial Technology—a case study on Sathyamangalam Forest, Erode District, Tamil Nadu

Namasivayam,

Ramamoorthy

2024

Environ Monit Assess

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Estimation of Picea Schrenkiana Canopy Density at Sub-Compartment Scale by Integration of Optical and Radar Satellite Images

Wang,

Li,

Yang

et al. 2024

Forests

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This study proposes a novel approach to estimate canopy density in Picea Schrenkiana var. Tianschanica forest sub-compartments by integrating optical and radar satellite data. This effort is aimed at enhancing methodologies for forest resource surveys and monitoring, particularly vital for the sustainable development of semi-arid mountainous areas with fragile ecological environments. The study area is the West Tianshan Mountain Nature Reserve in Xinjiang, which is characterized by its unique dominant tree species, Picea Schrenkiana. A total of 411 characteristic factors were extracted from Gaofen-2 (GF-2) sub-meter optical satellite imagery, Gaofen-3 (GF-3) multi-polarization synthetic aperture radar satellite imagery, and digital elevation model (DEM) data. Consequently, 17 characteristic parameters were selected based on their correlation with canopy density data to construct an estimation model. Three distinct models were developed, including a multiple stepwise regression model (a linear approach), a Back Propagation (BP) neural network model (a neural network-based method), and a Cubist model (a decision tree-based technique). The results indicate that combining optical and radar image characteristics significantly enhances accuracy, with an Average Absolute Percentage Precision (AAPP) value improvement in estimation accuracy from 76.50% (with optical image) and 78.50% (with radar image) to 78.66% (with both). Of the three models, the BP neural network model achieved the highest overall accuracy (79.19%). At the sub-component scale, the BP neural network model demonstrated superior accuracy in low canopy density estimation (75.37%), whereas the Cubist model, leveraging radar image characteristics, excelled in medium density estimations (87.46%). Notably, the integrated Cubist model combining optical and radar data achieved the highest accuracy for high canopy density estimation (89.17%). This study highlights the effectiveness of integrating optical and radar data for precise canopy density assessment, contributing significantly to ecological resource monitoring methodologies and environmental assessments.

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Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine

Cited by 3 publications

References 67 publications

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Estimation of forest canopy density through Geospatial Technology—a case study on Sathyamangalam Forest, Erode District, Tamil Nadu

Estimation of Picea Schrenkiana Canopy Density at Sub-Compartment Scale by Integration of Optical and Radar Satellite Images

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