Potential of interval partial least square regression in estimating leaf area index

Abstract

ARTICLE INCLUDES:× Supplementary material × Data set FUNDING:None Leaf area index (LAI) is a critical parameter in determining vegetation status and health. In tropical grasslands, reliable determination of LAI, useful in determining above ground biomass, provides a basis for rangeland management, conservation and restoration. In this study, interval partial least square regression (iPLSR) in forward mode was compared to partial least square regression (PLSR) to estimate LAI from in-situ canopy hyperspectral data on a heterogeneous grassland at different periods (onset, mid and end) during summer. The performance of the two techniques was determined using the least relative root mean square error to the mean (nRMSEP) and the highest coefficients of determination (R 2 p ) between the predicted and the measured LAI. Results show that iPLSR models could explain LAI variation with R 2 p values ranging from 0.81 to 0.93 and low nRMSEP from 9.39% to 24.71%. The highest accuracies for estimates of LAI using iPLSR were at mid-and end of summer (R 2 p = 0.93 and nRMSEP = 9.39%; R 2 p = 0.89 and nRMSEP = 10.50%, respectively). Pooling data sets from the three assessed periods yielded the highest prediction error (nRMSEP=24.71%). Results show that iPLSR performed better than PLSR, which yielded R 2 pand RMSEP values ranging from 0.36 to 0.65 and from 28.44% to 33.47%, respectively. Overall, this study demonstrates the value of iPLSR in predicting LAI and therefore provides a basis for more accurate mapping and monitoring of canopy characteristics of tropical grasslands. Significance:• The relationship between LAI and canopy reflectance can be used in iPLSR modelling to provide more accurate mapping and monitoring of canopy characteristics for land management and conservation.

Insect Succession and Decomposition Pattern on Pig Carrion During Warm and Cold Seasons in Kwazulu-Natal Province of South Africa

“…The farm is under uMgungundlovu district of KwaZulu-Natal province, South Africa. The area is characterized by warm to hot summers and mild winters, which are often accompanied by irregular frost ( Kiala et al 2017 ). The average monthly temperature ranges between 13.2°C and 21.4°C, with an annual mean temperature of 17°C ( Mills and Frey 2004 , Everson et al 2013 , Kiala et al 2017 ).…”

“…The area is characterized by warm to hot summers and mild winters, which are often accompanied by irregular frost ( Kiala et al 2017 ). The average monthly temperature ranges between 13.2°C and 21.4°C, with an annual mean temperature of 17°C ( Mills and Frey 2004 , Everson et al 2013 , Kiala et al 2017 ). The area receives an annual precipitation of 680 mm in over 106 d per annum ( Kiala et al 2017 ) and it falls under the Southern Tall Grassveld and mainly herbaceous as a result of long-term burnings ( Mills and Frey 2004 , Kiala et al 2017 ).…”

“…The average monthly temperature ranges between 13.2°C and 21.4°C, with an annual mean temperature of 17°C ( Mills and Frey 2004 , Everson et al 2013 , Kiala et al 2017 ). The area receives an annual precipitation of 680 mm in over 106 d per annum ( Kiala et al 2017 ) and it falls under the Southern Tall Grassveld and mainly herbaceous as a result of long-term burnings ( Mills and Frey 2004 , Kiala et al 2017 ). The cold season is experienced from the month of May to end of August, whilst the warm season is from September to April.…”

Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images

“…PLSR is considered as an efficient method for feature extraction and dimension reduction without losing much information [18,37]. It combines features of principal component regression and stepwise multivariable regression, and is capable of tackling some common problems in regression models, such as collinearity, data noise, and over-fitting [13,[37][38][39]. PLSR has been used in previous studies for estimating vegetation LAI, biomass, or nitrogen content [8,35,39,40].…”

Determining Optimal New Generation Satellite Derived Metrics for Accurate C3 and C4 Grass Species Aboveground Biomass Estimation in South Africa

“…The component and associated variables with the lowest estimation errors were then considered for further analysis and the AGB estimation. The same approach was used successfully, for example, by Sibanda et al [27], Abdel-Rahman et al [34], and Kiala et al [35].…”