Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data

Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous co...

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Autores principales:	Ranran Yang, Lei Wang, Qingjiu Tian, Nianxu Xu, Yanjun Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	GF-1 WFV time-series mixed broadleaf-conifer forest conifer-broadleaf ratio NDVI Science Q
Acceso en línea:	https://doaj.org/article/f94e879f130048ba821578df0b5a9fe4
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id	oai:doaj.org-article:f94e879f130048ba821578df0b5a9fe4
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spelling	oai:doaj.org-article:f94e879f130048ba821578df0b5a9fe42021-11-11T18:56:13ZEstimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data10.3390/rs132144262072-4292https://doaj.org/article/f94e879f130048ba821578df0b5a9fe42021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4426https://doaj.org/toc/2072-4292Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests.Ranran YangLei WangQingjiu TianNianxu XuYanjun YangMDPI AGarticleGF-1 WFVtime-seriesmixed broadleaf-conifer forestconifer-broadleaf ratioNDVIScienceQENRemote Sensing, Vol 13, Iss 4426, p 4426 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GF-1 WFV time-series mixed broadleaf-conifer forest conifer-broadleaf ratio NDVI Science Q
spellingShingle	GF-1 WFV time-series mixed broadleaf-conifer forest conifer-broadleaf ratio NDVI Science Q Ranran Yang Lei Wang Qingjiu Tian Nianxu Xu Yanjun Yang Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data
description	Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests.
format	article
author	Ranran Yang Lei Wang Qingjiu Tian Nianxu Xu Yanjun Yang
author_facet	Ranran Yang Lei Wang Qingjiu Tian Nianxu Xu Yanjun Yang
author_sort	Ranran Yang
title	Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data
title_short	Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data
title_full	Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data
title_fullStr	Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data
title_full_unstemmed	Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data
title_sort	estimation of the conifer-broadleaf ratio in mixed forests based on time-series data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f94e879f130048ba821578df0b5a9fe4
work_keys_str_mv	AT ranranyang estimationoftheconiferbroadleafratioinmixedforestsbasedontimeseriesdata AT leiwang estimationoftheconiferbroadleafratioinmixedforestsbasedontimeseriesdata AT qingjiutian estimationoftheconiferbroadleafratioinmixedforestsbasedontimeseriesdata AT nianxuxu estimationoftheconiferbroadleafratioinmixedforestsbasedontimeseriesdata AT yanjunyang estimationoftheconiferbroadleafratioinmixedforestsbasedontimeseriesdata
_version_	1718431682145550336

Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data

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