New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating...

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Main Authors: Pengfei Chen, Fangyong Wang
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/12/24/4170
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author Pengfei Chen
Fangyong Wang
author_facet Pengfei Chen
Fangyong Wang
author_sort Pengfei Chen
collection DOAJ
description Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)<sub>g</sub>) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)<sub>g</sub>), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.
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spelling doaj.art-dce7400d6d0f458bb392bf326fd712362023-11-21T01:43:31ZengMDPI AGRemote Sensing2072-42922020-12-011224417010.3390/rs12244170New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial VehiclePengfei Chen0Fangyong Wang1State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research of Chinese Academy of Sciences, Beijing 100101, ChinaCotton Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, ChinaAlthough textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)<sub>g</sub>) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)<sub>g</sub>), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.https://www.mdpi.com/2072-4292/12/24/4170textural indexbiomasscottonunmanned aerial vehicleoptical image
spellingShingle Pengfei Chen
Fangyong Wang
New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle
Remote Sensing
textural index
biomass
cotton
unmanned aerial vehicle
optical image
title New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle
title_full New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle
title_fullStr New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle
title_full_unstemmed New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle
title_short New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle
title_sort new textural indicators for assessing above ground cotton biomass extracted from optical imagery obtained via unmanned aerial vehicle
topic textural index
biomass
cotton
unmanned aerial vehicle
optical image
url https://www.mdpi.com/2072-4292/12/24/4170
work_keys_str_mv AT pengfeichen newtexturalindicatorsforassessingabovegroundcottonbiomassextractedfromopticalimageryobtainedviaunmannedaerialvehicle
AT fangyongwang newtexturalindicatorsforassessingabovegroundcottonbiomassextractedfromopticalimageryobtainedviaunmannedaerialvehicle