Evaluating the robustness of models developed from field spectral data in predicting African grass foliar nitrogen concentration using WorldView-2 image as an independent test dataset

Mutanga, Onisimo; Adam, Elhadi; Adjorlolo, Clement; Abdel‐Rahman, Elfatih M.

doi:10.1016/j.jag.2014.08.008

Cited by 58 publications

(27 citation statements)

References 61 publications

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“…However, our results showed that the addition of MCRC, NDI5, and NDSVI to the PLSR did not significantly improve the CRC estimation accuracy. Li et al [26] and Mutanga et al [27] indicated that the main spectral information (sensitive spectral bands and VIs) is sufficient to obtain high estimation accuracy, and our results were in agreement with their studies. The addition of Band2mean, Band3homogeneity, Band3dissimilarity,Band3entropy, Band3second moment, Band3correlation, and Band6mean further impoved the estimation accauracy of MRC based on Band3mean, Band4mean, and Band5mean using PLSR (Table 5).…”

Section: Discussionsupporting

confidence: 92%

“…Darvishzadeh et al [25] reported that using full spectral subsets or the maximum amount of spectral information available will require more computation time but is not likely to increase estimation accuracy. Moreover, several features are sufficient for obtaining the essential information to estimate LAI, nitrogen concentration, and vegetation classification [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Jin

Wen

et al. 2015

Remote Sensing

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Abstract:The application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC) from Landsat-8 OLI images using seven vegetation indices (VIs) and eight textural features; and (ii) compare the VI method, textural feature method, and combination method (integration of textural features and spectral information) for estimating MRC with partial least squares regression (PLSR). The results showed that the normalized difference tillage index (NDTI), simple tillage index (STI), normalized difference index 7 (NDI7), and shortwave red normalized difference index (SRNDI) were significantly correlated with MRC. The MRC model based on NDTI outperformed (R 2 = 0.84 and RMSE = 12.33%) the models based on the other VIs. Band3mean, Band4mean, and Band5mean were highly correlated with MRC. The regression between Band3mean and MRC was stronger (R 2 = 0.71 and RMSE = 15.21%) than those between MRC and the other textural features. The MRC estimation accuracy using the combination method (R 2 = 0.96 and RMSE = 8.11%) was better than that based on only the VI (R 2 = 0.88 and RMSE = 11.34%) or textural feature (R 2 = 0.90 and RMSE = 9.82%) methods. The results suggest that the combination method can be used to estimate MRC on a regional scale. OPEN ACCESSRemote Sens. 2015, 7 14560

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Jin

Wen

et al. 2015

Remote Sensing

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“…The spectral bands of WV-2 consist of four conventional bands (blue, green, red, and NIR1) and four additional bands (coastal blue, yellow, red edge, and a new NIR-2). Therefore, the satellite has the spectral and spatial resolutions that meet many applications like predicting and monitoring forest structural and biophysical traits at species level [73,74]. The WV-2 image was atmospherically corrected and transformed to at canopy reflectance using the Quick Atmospheric Correction (QUAC) procedure in ENVI (Environment for Visualizing Images) 4.7 software [75].…”

Section: Remotley Sensed Data and Pre-processingmentioning

confidence: 99%

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

et al. 2016

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Leaf area index (LAI) is an important biophysical trait for forest ecosystem and ecological modeling, as it plays a key role for the forest productivity and structural characteristics. The groundbased methods like the handheld optical instruments for predicting LAI are subjective, pricy and time-consuming. The advent of very high spatial resolutions multispectral data and robust machine learning regression algorithms like support vector machines (SVM) and artificial neural networks (ANN) has provided an opportunity to estimate LAI at tree species level. The objective of the this study was therefore to test the utility of spectral vegetation indices (SVI) calculated from the multispectral WorldView-2 (WV-2) data in predicting LAI at tree species level using the SVM and ANN machine learning regression algorithms. We further tested whether there are significant differences between LAI of intact and fragmented (open) indigenous forest ecosystems at tree species level. The study shows that LAI at tree species level could accurately be estimated using the fragmented stratum data compared with the intact stratum data. Specifically, our study shows that the accurate LAI predictions were achieved for Hymenocardia ulmoides using the fragmented stratum data and SVM regression model based on a validation dataset (R 2 Val = 0.75, RMSE Val = 0.05 (1.37% of the mean)). Our study further showed that SVM regression approach achieved more accurate models for predicting the LAI of the six endangered tree species compared with ANN regression method. It is concluded that the successful application of the WV-2 data, SVM and ANN methods in predicting LAI of six endangered tree species in the Dukuduku indigenous forest could help in making informed decisions and policies regarding management, protection and conservation of these endangered tree species.

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“…Hyperspectral remote sensing of grassland has been widely used in recent studies, including the monitoring of the nutritional status, aboveground biomass (AGB) and coverage of grasslands [7][8][9][10], species recognition [11,12], and health assessments [13]. In particular, the development of estimation models and the spatial mapping of forage N, based on a wide variety of hyperspectral spectrometers and sensors, has been a popular research topic [8,14,15]. Studies have indicated that several specific known absorption bands for N, proteins, and chlorophyll (i.e., 640 nm, 910 nm, 1510 nm, and 2300 nm) can be successfully used for forage N estimation [14,[16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…The growth period of forage has a strong correlation with the changes and distribution of N content, and it significantly affects the characteristics and morphology of the vegetation canopy spectrum. Most of the aforementioned hyperspectral parameters (i.e., VIs and absorption features) were proposed based on a specific growth period, especially the vigorous growing stage when the effect of biomass on N can be minimized [8,9]. Moreover, most studies have focused on the detection of spatial differences in forage N during a particular period without considering changes in the temporal dimension (different growth periods) [25,26].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Alpine Grassland Forage Nitrogen Coupled with Hyperspectral Characteristics during Different Growth Periods on the Tibetan Plateau

et al. 2019

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The applicability of hyperspectral remote sensing models for forage nitrogen (N) retrieval during different growth periods is limited. This study aims to develop a multivariate model feasible for estimating the forage N for the growth periods (June to November) in an alpine grassland ecosystem. The random forest (RF) algorithm is employed to determine the optimum combinations of 38 spectral variables capable of capturing dynamic variations in forage N. The results show that (1) throughout the growth period, the red-edge first shifts toward longer wavelengths and then shifts toward shorter wavelengths, the amplitude (AMP) and absorption depth (AD) gradually decrease, and the absorption position (AP) changes slightly; (2) the importance of spectral variables for forage N estimation differs during the different growth periods; (3) the multivariate model achieves better results for the first four periods (June to October) than for the last period (when the grass is completely senesced) (V-R2: 0.58–0.68 versus 0.23); and (4) for the whole growth period (June to November), the prediction accuracy of the general N estimation model validated by the unknown growth period is lower than that validated by the unknown location (V-R2 is 0.28 and 0.55 for the validation strategies of Leave-Time-Out and Leave-Location-Out, respectively). This study demonstrates that the changes in the spectral features of the red wavelength (red-edge position, AMP and AD) are well coupled with the forage N content. Moreover, the development of a multivariate RF model for estimating alpine grasslands N content during different growth periods is promising for the improvement of both the stability and accuracy of the model.

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Evaluating the robustness of models developed from field spectral data in predicting African grass foliar nitrogen concentration using WorldView-2 image as an independent test dataset

Cited by 58 publications

References 61 publications

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Estimation of Alpine Grassland Forage Nitrogen Coupled with Hyperspectral Characteristics during Different Growth Periods on the Tibetan Plateau

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