An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China

Spectral unmixing remains the most popular method for estimating the composition of mixed pixels. However, the spectral-based unmixing method cannot easily distinguish vegetation with similar spectral characteristics (e.g., different forest tree species). Furthermore, in large areas with significant...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yingying Yang, Taixia Wu, Yuhui Zeng, Shudong Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cover fraction evergreen forest FEVC-SAP large scale time series Science Q
Acceso en línea:	https://doaj.org/article/3c07329bf7b34d5c92a661ee80f425b7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3c07329bf7b34d5c92a661ee80f425b7
record_format	dspace
spelling	oai:doaj.org-article:3c07329bf7b34d5c92a661ee80f425b72021-11-25T18:55:19ZAn Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China10.3390/rs132246782072-4292https://doaj.org/article/3c07329bf7b34d5c92a661ee80f425b72021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4678https://doaj.org/toc/2072-4292Spectral unmixing remains the most popular method for estimating the composition of mixed pixels. However, the spectral-based unmixing method cannot easily distinguish vegetation with similar spectral characteristics (e.g., different forest tree species). Furthermore, in large areas with significant heterogeneity, extracting a large number of pure endmember samples is challenging. Here, we implement a fractional evergreen forest cover-self-adaptive parameter (FEVC-SAP) approach to measure FEVC at the regional scale from continuous intra-year time-series normalized difference vegetation index (NDVI) values derived from moderate resolution imaging spectroradiometer (MODIS) imagery acquired over southern China, an area with a complex mixture of temperate, subtropical, and tropical climates containing evergreen and deciduous forests. Considering the cover of evergreen forest as a fraction of total forest (evergreen forest plus non-evergreen forest), the dimidiate pixel model combined with an index of evergreen forest phenological characteristics (NDVIann-min: intra-annual minimum NDVI value) was used to distinguish between evergreen and non-evergreen forests within a pixel. Due to spatial heterogeneity, the optimal model parameters differ among regions. By dividing the study area into grids, our method converts image spectral information into gray level information and uses the Otsu threshold segmentation method to simulate the appropriate parameters for each grid for adaptive acquisition of FEVC parameters. Mapping accuracy was assessed at the pixel and sub-pixel scales. At the pixel scale, a confusion matrix was constructed with higher overall accuracy (87.5%) of evergreen forest classification than existing land cover products, including GLC 30 and MOD12. At the sub-pixel scale, a strong linear correlation was found between the cover fraction predicted by our method and the reference cover fraction obtained from GF-1 images (R2 = 0.86). Compared to other methods, the FEVC-SAP had a lower estimation deviation (root mean square error = 8.6%). Moreover, the proposed method had greater estimation accuracy in densely than sparsely forested areas. Our results highlight the utility of the adaptive-parameter linear unmixing model for quantitative evaluation of the coverage of evergreen forest and other vegetation types at large scales.Yingying YangTaixia WuYuhui ZengShudong WangMDPI AGarticlecover fractionevergreen forestFEVC-SAPlarge scaletime seriesScienceQENRemote Sensing, Vol 13, Iss 4678, p 4678 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cover fraction evergreen forest FEVC-SAP large scale time series Science Q
spellingShingle	cover fraction evergreen forest FEVC-SAP large scale time series Science Q Yingying Yang Taixia Wu Yuhui Zeng Shudong Wang An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China
description	Spectral unmixing remains the most popular method for estimating the composition of mixed pixels. However, the spectral-based unmixing method cannot easily distinguish vegetation with similar spectral characteristics (e.g., different forest tree species). Furthermore, in large areas with significant heterogeneity, extracting a large number of pure endmember samples is challenging. Here, we implement a fractional evergreen forest cover-self-adaptive parameter (FEVC-SAP) approach to measure FEVC at the regional scale from continuous intra-year time-series normalized difference vegetation index (NDVI) values derived from moderate resolution imaging spectroradiometer (MODIS) imagery acquired over southern China, an area with a complex mixture of temperate, subtropical, and tropical climates containing evergreen and deciduous forests. Considering the cover of evergreen forest as a fraction of total forest (evergreen forest plus non-evergreen forest), the dimidiate pixel model combined with an index of evergreen forest phenological characteristics (NDVIann-min: intra-annual minimum NDVI value) was used to distinguish between evergreen and non-evergreen forests within a pixel. Due to spatial heterogeneity, the optimal model parameters differ among regions. By dividing the study area into grids, our method converts image spectral information into gray level information and uses the Otsu threshold segmentation method to simulate the appropriate parameters for each grid for adaptive acquisition of FEVC parameters. Mapping accuracy was assessed at the pixel and sub-pixel scales. At the pixel scale, a confusion matrix was constructed with higher overall accuracy (87.5%) of evergreen forest classification than existing land cover products, including GLC 30 and MOD12. At the sub-pixel scale, a strong linear correlation was found between the cover fraction predicted by our method and the reference cover fraction obtained from GF-1 images (R2 = 0.86). Compared to other methods, the FEVC-SAP had a lower estimation deviation (root mean square error = 8.6%). Moreover, the proposed method had greater estimation accuracy in densely than sparsely forested areas. Our results highlight the utility of the adaptive-parameter linear unmixing model for quantitative evaluation of the coverage of evergreen forest and other vegetation types at large scales.
format	article
author	Yingying Yang Taixia Wu Yuhui Zeng Shudong Wang
author_facet	Yingying Yang Taixia Wu Yuhui Zeng Shudong Wang
author_sort	Yingying Yang
title	An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China
title_short	An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China
title_full	An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China
title_fullStr	An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China
title_full_unstemmed	An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China
title_sort	adaptive-parameter pixel unmixing method for mapping evergreen forest fractions based on time-series ndvi: a case study of southern china
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3c07329bf7b34d5c92a661ee80f425b7
work_keys_str_mv	AT yingyingyang anadaptiveparameterpixelunmixingmethodformappingevergreenforestfractionsbasedontimeseriesndviacasestudyofsouthernchina AT taixiawu anadaptiveparameterpixelunmixingmethodformappingevergreenforestfractionsbasedontimeseriesndviacasestudyofsouthernchina AT yuhuizeng anadaptiveparameterpixelunmixingmethodformappingevergreenforestfractionsbasedontimeseriesndviacasestudyofsouthernchina AT shudongwang anadaptiveparameterpixelunmixingmethodformappingevergreenforestfractionsbasedontimeseriesndviacasestudyofsouthernchina AT yingyingyang adaptiveparameterpixelunmixingmethodformappingevergreenforestfractionsbasedontimeseriesndviacasestudyofsouthernchina AT taixiawu adaptiveparameterpixelunmixingmethodformappingevergreenforestfractionsbasedontimeseriesndviacasestudyofsouthernchina AT yuhuizeng adaptiveparameterpixelunmixingmethodformappingevergreenforestfractionsbasedontimeseriesndviacasestudyofsouthernchina AT shudongwang adaptiveparameterpixelunmixingmethodformappingevergreenforestfractionsbasedontimeseriesndviacasestudyofsouthernchina
_version_	1718410517748383744

An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China

Ejemplares similares