Remote sensing of subtropical tree diversity:The underappreciated roles of the practical definition of forest canopy and phenological variation

Tree species diversity is vital for maintaining ecosystem functions,yet our ability to map the distribution of tree diversity is limited due to difficulties in traditional field-based approaches.Recent developments in spaceborne remote sensing provide unprecedented opportunities to map and monitor tree diversity more efficiently.Here we built partial least squares regression models using the multispectral surface reflectance acquired by Sentinel-2 satellites and the inventory data from 74 subtropical forest plots to predict canopy tree diversity in a national natural reserve in eastern China.In particular,we evaluated the underappreciated roles of the practical definition of forest canopy and phenological variation in predicting tree diversity by testing three different definitions of canopy trees and comparing models built using satellite imagery of different seasons.Our best models explained 42%–63%variations in observed diversities in cross-validation tests,with higher explanation power for diversity indices that are more sensitive to abundant species.The models built using imageries from early spring and late autumn showed consistently better fits than those built using data from other seasons,highlighting the significant role of transitional phenology in remotely sensing plant diversity.Our results suggested that the cumulative diameter(60%–80%)of the biggest trees is a better way to define the canopy layer than using the subjective fixeddiameter-threshold(5–12 cm)or the cumulative basal area(90%–95%)of the biggest trees.Remarkably,these approaches resulted in contrasting diversity maps that call attention to canopy structure in remote sensing of tree diversity.This study demonstrates the potential of mapping and monitoring tree diversity using the Sentinal-2 data in species-rich forests.

Extraction of altered minerals from Aster remote sensing data in Gongchangling iron deposit of Liaoning, China

The precision of Aster data is higher than that of Landsat series of multispectral remote sensing data,which can more accurately reveal the distribution of altered minerals.It plays an important role in prospecting,but it is rarely used in areas with complex terrain and high vegetation coverage.Based on this purpose,this study used Aster remote sensing data,and took Gongchangling iron deposit as a case study.It combined the mineral spectrum theory and the basic geologic data of the study area,using the model of principal component analysis(PCA)and color synthesis to extract abnormal altered minerals.The results show that the distribution of identified anomalies is basically consistent with the existing geological data in this study area,which provides a reliable reference for the mineral resources ex-ploration and delineation of mining areas.

Robust Core Tensor Dictionary Learning with Modified Gaussian Mixture Model for Multispectral Image Restoration

The multispectral remote sensing image(MS-RSI)is degraded existing multi-spectral camera due to various hardware limitations.In this paper,we propose a novel core tensor dictionary learning approach with the robust modified Gaussian mixture model for MS-RSI restoration.First,the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the tensor.Then the task of MS-RSI restoration is formulated as a minimum sparse core tensor estimation problem.To improve the accuracy of core tensor coding,the core tensor estimation based on the robust modified Gaussian mixture model is introduced into the proposed model by exploiting the sparse distribution prior in image.When applied to MS-RSI restoration,our experimental results have shown that the proposed algorithm can better reconstruct the sharpness of the image textures and can outperform several existing state-of-the-art multispectral image restoration methods in both subjective image quality and visual perception.

Fusion of Remote Sensing Images Based on Nonsubsampled Contourlet Transform and Region Segmentation

The purpose of remote sensing images fusion is to produce a fused image that contains more clear,accurate and comprehensive information than any single image.A novel fusion method is proposed in this paper based on nonsubsampled contourlet transform(NSCT) and region segmentation.Firstly,the multispectral image is transformed to intensity-hue-saturation(IHS) system.Secondly,the panchromatic image and the component intensity of the multispectral image are decomposed by NSCT.Then the NSCT coefficients of high and low frequency subbands are fused by different rules,respectively.For the high frequency subbands,the fusion rules are also unalike in the smooth and edge regions.The two regions are segregated in the panchromatic image,and the segmentation is based on particle swarm optimization.Finally,the fusion image can be obtained by performing inverse NSCT and inverse IHS transform.The experimental results are evaluated by both subjective and objective criteria.It is shown that the proposed method can obtain superior results to others.