# Mapping Foliar C, N, and P Concentrations in An Ecological Restoration Area with Mixed Plant Communities Based on LiDAR and Hyperspectral Data

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

^{2}was used to compare the accuracy of the three models as follows: R

^{2}values for C were 0.07, 0.42, and 0.56, for N they were 0.20, 0.48, and 0.53, and for P they were 0.32, 0.39, and 0.44; the random forest model demonstrated the highest accuracy. (3) The accuracy of the concentration estimates could be ranked as C > N > P. (4) The inclusion of LiDAR features significantly improved the accuracy of the C concentration estimation, with increases of 22.20% and 47.30% in the multiple linear regression and random forest models, respectively, although the inclusion of LiDAR features did not notably enhance the accuracy of the N and P concentration estimates. Therefore, LiDAR and hyperspectral data can be used to effectively map C, N, and P concentrations in a mixed plant community in a restored area, revealing their heterogeneity in terms of species and spatial distribution. Future efforts should involve the use of hyperspectral data with additional bands and a more detailed classification of plant communities. The application of this information will be useful for analyzing C, N, and P limitations, and for planning for the maintenance of restored plant communities.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}. Situated in the transitional zone between grassland and forest steppe, the original vegetation types include deciduous trees, shrubs, sandy-soil vegetation, and grassland.

#### 2.2. Data

#### 2.2.1. Remote Sensing Data

^{2}. The elevation of the study area was 1200–1296 m, and the number of echoes was two. The LiDAR 360 6.0 software was acquired from GreenValley International (Berkeley, CA, USA) and was used for preprocessing. The preprocessing included creating an aerial strip mosaic, point cloud registration, strip redundancy removal, noise removal, point cloud feature extraction, and point cloud classification [26]. First, mosaic the LiDAR data and then register to minimize the spatial position differences between the points. Then, remove the redundant point cloud data of the overlapping part of the strip and point cloud noise, and then divide the point cloud into ground and non-ground points. Finally, extract the point cloud data features. Before extracting the LiDAR features, it was necessary to normalize the LiDAR data by subtracting the elevation value Z of each point in the LiDAR point cloud data. Normalization can remove the influence of terrain fluctuations on the elevation value of the point cloud data.

#### 2.2.2. Field Data

#### 2.3. Methods

#### 2.3.1. Technical Process

#### 2.3.2. Extraction of Features

- Spectrum

- 2.
- Texture

- 3.
- Vegetation indices

- 4.
- Height and vegetation structure parameters

#### 2.3.3. Selection of Features

- Build random forest: construct a random forest model using the original dataset. This typically involves multiple decision trees, each trained on a random subset.
- Calculate the importance of the original features: for each feature, compute its relative importance using the random forest model. This is achieved by measuring the contribution of the feature to the model’s predictive accuracy.
- Create shadow features: for each original feature, generate a corresponding “shadow” feature. A shadow feature is created by randomly permuting the values of the original feature.
- Build an extended random forest: build another random forest model using a dataset that includes both the original and shadow features.
- Compute the importance of the shadow features: calculate the relative importance of each shadow feature in the extended random forest.
- Compare the importance of the original and shadow features: for each original feature, compare its actual importance with the average importance of its shadow features. If the original feature’s importance is higher than the average importance of its shadow features, retain the feature; otherwise, label it as unimportant.
- Repeat steps 4–6: iterate through steps 4–6 until the stopping criteria are met, such as reaching a specified number of features or marking all features as important.
- Final feature selection: retain the features labeled as important for modeling or further analysis.

#### 2.3.4. Estimation Model

- Causal band model

- 2.
- Multiple linear regression algorithm

- 3.
- Random forest algorithm

- n samples randomly drawn from the training set are put back.
- Create a decision tree from a dataset consisting of these n samples.
- At each node: randomly select d features without putting them back.
- Use maximized learning gain or other methods to split nodes based on these features.

- Repeat steps 1–2 several (k) times.
- Finally, take the average value according to the estimation results of these decision trees as the final estimation results.

#### 2.3.5. Validation

^{2}, root mean squared error (RMSE), and mean absolute error (MAE) were calculated. RMSE and MAE have the same unit for the C, N, and P concentrations. The R

^{2}values report how well the model explains the variation of the dependent variable. The RMSE values report the degree of difference between the measured and estimated values. MAE is the average of the absolute error between the measured and estimated values. The calculation formulas of R

^{2}, RMSE, and MAE are, respectively, as follows:

## 3. Results

#### 3.1. Selected Feature

#### 3.2. Accuracy of the Estimation Model

^{2}and RMSE for different models and feature combinations. For C, the accuracy of the three models ranged from 0.07 to 0.56, with the RMSE ranging from 1.19% to 9.49%. Among them, the multiple linear regression and random forest models outperformed the causal bands model. Additionally, combinations with LiDAR features outperformed those with hyperspectral features. The best performance was observed in the random forest model with hyperspectral + LiDAR, where R

^{2}reached 0.56. For N, the accuracy of the models ranged from 0.20 to 0.53, with the RMSE ranging from 0.57% to 0.92%. Similar to C, the multiple linear regression and random forest models outperformed the causal band model. The difference in R

^{2}between the models with hyperspectral + LiDAR and hyperspectral features was 0.00. The best performance was observed in the random forest model with hyperspectral features, reaching an R

^{2}of 0.53. For P, the accuracy of the models ranged from 0.32 to 0.44, with the RMSE ranging from 0.40 g/kg to 0.52 g/kg. The multiple linear regression and random forest models outperformed the causal band model; the difference in R

^{2}between the models with hyperspectral + LiDAR and hyperspectral features was only 0.01. The best performance was observed in the random forest model with hyperspectral + LiDAR, achieving an R

^{2}of 0.44.

#### 3.3. Map of Foliar C, N, and P Concentrations

## 4. Discussion

#### 4.1. The Role of Features in the Model

#### 4.2. Implications of the Foliar C, N, and P Map

#### 4.3. Limitations and Future Work

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Sun, X.; Yuan, L.; Liu, M.; Liang, S.; Li, D.; Liu, L. Quantitative estimation for the impact of mining activities on vegetation phenology and identifying its controlling factors from Sentinel-2 time series. Int. J. Appl. Earth Obs. Geoinf.
**2022**, 111, 102814. [Google Scholar] [CrossRef] - Chugh, Y.P.; Schladweiler, B.K.; Skilbred, C. Sustainable and responsible mining through sound mine closure. Int. J. Coal Sci. Technol.
**2023**, 10, 14. [Google Scholar] [CrossRef] - Fischer, J.; Riechers, M.; Loos, J.; Martin-Lopez, B.; Temperton, V.M. Making the UN Decade on ecosystem restoration a social-ecological endeavour. Trends Ecol. Evol.
**2020**, 36, 20–28. [Google Scholar] [CrossRef] [PubMed] - Krzyszowska Waitkus, A. Sustainable reclamation practices for a large surface coal mine in shortgrass prairie, semiarid environment (Wyoming, USA): Case study. Int. J. Coal Sci. Technol.
**2022**, 9, 32. [Google Scholar] [CrossRef] - Banerjee, B.P.; Raval, S. Mapping sensitive vegetation communities in mining eco-space using UAV-LiDAR. Int. J. Coal Sci. Technol.
**2022**, 9, 40. [Google Scholar] [CrossRef] - Zhang, Y.; Zhou, W. Remote sensing of vegetation fraction for monitoring reclamation dynamics: A case study in Pingshuo Mining area. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10–15 July 2016; pp. 5197–5200. [Google Scholar] [CrossRef]
- Ustin, S. Remote sensing of canopy chemistry. Proc. Natl. Acad. Sci. USA
**2013**, 110, 804–805. [Google Scholar] [CrossRef] [PubMed] - Gastauer, M.; Silva, R.J.; Junior, C.F.C.; Ramos, S.J.; Filho, P.; Neto, A.; Siqueira, J.O. Mine land rehabilitation: Modern ecological approaches for more sustainable mining. J. Clean. Prod.
**2018**, 172, 1409–1422. [Google Scholar] [CrossRef] - McGroddy, M.E.; Daufresne, T.; Hedin, L.O. Scaling of C: N: P stoichiometry in forests worldwide: Implications of terrestrial redfield-type ratios. Ecology
**2004**, 85, 2390–2401. [Google Scholar] [CrossRef] - Han, W.; Fang, J.; Reich, P.B.; Ian Woodward, F.; Wang, Z. Biogeography and variability of eleven mineral elements in plant leaves across gradients of climate, soil and plant functional type in China. Ecol. Lett.
**2011**, 14, 788–796. [Google Scholar] [CrossRef] - Xing, K.; Zhao, M.; Niinemets, U.; Niu, S.; Tian, J.; Jiang, Y.; Chen, H.; White, P.J.; Guo, D.; Ma, Z. Relationships between leaf carbon and macronutrients across woody species and forest ecosystems highlight how carbon is allocated to leaf structural function. Front. Plant Sci.
**2021**, 12, 674932. [Google Scholar] [CrossRef] - Högberg, P.; Näsholm, T.; Franklin, O.; Högberg, M.N. Tamm review: On the nature of the nitrogen limitation to plant growth in Fennoscandian boreal forests. For. Ecol. Manag.
**2017**, 403, 161–185. [Google Scholar] [CrossRef] - Urbina, I.; Sardans, J.; Grau, O.; Beierkuhnlein, C.; Jentsch, A.; Kreyling, J.; Peñuelas, J. Plant community composition affects the species biogeochemical niche. Ecosphere
**2017**, 8, e01801. [Google Scholar] [CrossRef] - Güsewell, S. N:P ratios in terrestrial plants: Variation and functional significance. New Phytol.
**2004**, 164, 243–266. [Google Scholar] [CrossRef] [PubMed] - Reich, P.B.; Oleksyn, J. Global patterns of plant leaf N and P in relation to temperature and latitude. Proc. Natl. Acad. Sci. USA
**2004**, 101, 11001–11006. [Google Scholar] [CrossRef] [PubMed] - Xiong, J.; Yu, M.; Cheng, X.; Wang, C.; Zou, H. Effects of light and N-P supply ratios on growth and stoichiometric of Schimasuperba. Acta Ecol. Sin.
**2021**, 41, 2140–2150. [Google Scholar] [CrossRef] - Avolio, M.L.; Koerner, S.E.; La Pierre, K.J.; Wilcox, K.R.; Wilson, G.W.T.; Smith, M.D.; Collins, S.L. Changes in plant community composition, not diversity, during a decade of nitrogen and phosphorus additions drive above-ground productivity in a tallgrass prairie. J. Ecol.
**2014**, 102, 1649–1660. [Google Scholar] [CrossRef] - Stiles, W.A.V.; Rowe, E.C.; Dennis, P. Long-term nitrogen and phosphorus enrichment alters vegetation species composition and reduces carbon storage in upland soil. Sci. Total Environ.
**2017**, 593–594, 688–694. [Google Scholar] [CrossRef] - McKenna, P.B.; Lechner, A.M.; Phinn, S.; Erskine, P.D. Remote sensing of mine site rehabilitation for ecological outcomes: A global systematic review. Remote Sens.
**2020**, 12, 3535. [Google Scholar] [CrossRef] - Yang, Y.; Tang, J.; Zhang, Y.; Zhang, S.; Zhou, Y.; Hou, H.; Liu, R. Reforestation improves vegetation coverage and biomass, but not spatial structure, on semi-arid mine dumps. Ecol. Eng.
**2022**, 175, 106508. [Google Scholar] [CrossRef] - Wang, J.; Wang, T.; Shi, T.; Wu, G.; Skidmore, A.K. A wavelet-based area parameter for indirectly estimating copper concentration in Carex Leaves from canopy reflectance. Remote Sens.
**2015**, 7, 15340–15360. [Google Scholar] [CrossRef] - Dou, Z.; Cui, L.; Li, J.; Zhu, Y.; Gao, C.; Pan, X.; Lei, Y.; Zhang, M.; Zhao, X.; Li, W. Hyperspectral estimation of the chlorophyll content in short-term and long-term restorations of mangrove in Quanzhou Bay Estuary, China. Sustainability
**2018**, 10, 1127. [Google Scholar] [CrossRef] - Liu, W.; Li, M.; Zhang, M.; Wang, D.; Guo, Z.; Long, S.; Yang, S.; Wang, H.; Li, W.; Hu, Y.; et al. Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance. Ecosyst. Health Sustain.
**2020**, 6, 1726211. [Google Scholar] [CrossRef] - Ren, H.; Zhao, Y.; Xiao, W.; Hu, H. A review of UAV monitoring in mining areas: Current status and future perspectives. Int. J. Coal Sci. Technol.
**2019**, 6, 320–333. [Google Scholar] [CrossRef] - Nie, S.; Wang, C.; Zeng, H.; Xi, X.; Li, G. Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest. Ecol. Indic.
**2017**, 78, 221–228. [Google Scholar] [CrossRef] - Tang, J.; Liang, J.; Yang, Y.; Zhang, S.; Hou, H.; Zhu, X. Revealing the structure and composition of the restored vegetation cover in semi-arid mine dumps based on LiDAR and Hyperspectral Images. Remote Sens.
**2022**, 14, 978. [Google Scholar] [CrossRef] - Ewald, M.; Aerts, R.; Lenoir, J.; Fassnacht, F.; Nicolas, M.; Skowronek, S.; Piat, J.; Honnay, O.; Garzón-López, C.; Feilhauer, H.; et al. LiDAR derived forest structure data improves predictions of canopy N and P concentrations from imaging spectroscopy. Remote Sens. Environ.
**2018**, 211, 13–25. [Google Scholar] [CrossRef] - Zhang, Y.; Wang, T.; Guo, Y.; Skidmore, A.; Zhang, Z.; Tang, R.; Song, S.; Tang, Z. Estimating community-level plant functional traits in a species-rich alpine meadow using UAV image spectroscopy. Remote Sens.
**2022**, 14, 3399. [Google Scholar] [CrossRef] - Bi, K.; Gao, S.; Niu, Z.; Zhang, C.; Huang, N. Estimating leaf chlorophyll and nitrogen contents using active hyperspectral LiDAR and partial least square regression method. J. Appl. Remote Sens.
**2019**, 13, 034513. [Google Scholar] [CrossRef] - Yu, F.; Feng, S.; Yao, W.; Wang, D.; Xing, S.; Xu, T. BAS-ELM based UAV hyperspectral remote sensing inversion modeling of rice canopy nitrogen content. Int. J. Precis. Agric. Aviat.
**2018**, 1, 59–64. [Google Scholar] [CrossRef] - Ye, X.; Abe, S.; Zhang, S. Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging. Precis. Agric.
**2020**, 21, 198–225. [Google Scholar] [CrossRef] - Cui, L.; Dou, Z.; Liu, Z.; Zuo, X.; Lei, Y.; Li, J.; Zhao, X.; Zhai, X.; Pan, X.; Li, W. Hyperspectral inversion of phragmites communis carbon, nitrogen, and phosphorus stoichiometry using three models. Remote Sens.
**2020**, 12, 1998. [Google Scholar] [CrossRef] - Wang, Z.; Chen, J.; Zhang, J.; Tan, X.; Raza, M.A.; Ma, J.; Zhu, Y.; Yang, F.; Yang, W. Assessing canopy nitrogen and carbon content in maize by canopy spectral reflectance and uninformative variable elimination. Crop J.
**2022**, 10, 15. [Google Scholar] [CrossRef] - Li, K.; Chen, Y.; Xu, Z.; Huang, X.; Hu, X.; Wang, X. Hyperspectral estimation method of chlorophyll content in Phyllostachys pubescens under pest stress. Spectrosc. Spectr. Anal.
**2020**, 40, 2578–2583. [Google Scholar] [CrossRef] - Lohmann, P.; Koch, A.; Schaeffer, M. Approaches to the filtering of laser scanner data. Int. Arch. Photogramm. Remote Sens.
**2000**, 33, 540–547. [Google Scholar] - Gitelson, A.A.; Keydan, G.P.; Merzlyak, M.N. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys. Res. Lett.
**2006**, 33, 431–433. [Google Scholar] [CrossRef] - Maire, G.L.; Fran, O.C.; Dufrêne, E. Towards universal broad leaf chlorophyll indices using prospect simulated database and hyperspectral reflectance measurements. Remote Sens. Environ.
**2004**, 89, 1–28. [Google Scholar] [CrossRef] - Richardson, A.J.; Wiegand, C.L. Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sens.
**1977**, 43, 100–120. [Google Scholar] - Huete, A.; Justice, C.; Liu, H. Development of vegetation and soil indices for MODIS-EOS. Remote Sens. Environ.
**1994**, 49, 224–234. [Google Scholar] [CrossRef] - Datt, B. A new reflectance index for remote sensing of chlorophyll content in higher plants: Tests using eucalyptus leaves. J. Plant Physiol.
**1999**, 154, 30–36. [Google Scholar] [CrossRef] - Daughtry, C.S.T.; Walthall, C.L.; Kim, M.S.; De Colstoun, E.B.; McMurtrey Iii, J.E. Estimating corn leaf chlorophyll concentration from leaf and canopy re-flectance. Remote Sens. Environ.
**2000**, 74, 229–239. [Google Scholar] [CrossRef] - Sims, D.A.; Gamon, J.A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ.
**2002**, 81, 337–354. [Google Scholar] [CrossRef] - Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ.
**1994**, 48, 119–126. [Google Scholar] [CrossRef] - Driss, H.; John, R.M.; Elizabeth, P.; Pablo, J.Z.T.; Ian, B.S. Hyperspectral vegetation indices and novel algorithms for predicting green lai of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ.
**2004**, 10, 100–120. [Google Scholar] [CrossRef] - Miller, J.R.; Hare, E.W.; Wu, J. Quantitative characterization of the vegetation red edge reflectance 1. An inverted-gaussian reflectance model. Int. J. Remote Sens.
**1990**, 11, 1755–1773. [Google Scholar] [CrossRef] - Peuelas, J.; Gamon, J.A.; Fredeen, A.L.; Merino, J.; Field, C.B. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ.
**1994**, 48, 135–146. [Google Scholar] [CrossRef] - Rao, N.R.; Garg, P.K.; Ghosh, S.K.; Dadhwal, V.K. Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery. J. Agric. Sci.
**2008**, 146, 65–75. [Google Scholar] [CrossRef] - Thenot, F.; Méthy, M.; Winkel, T. The Photochemical Reflectance Index (PRI) as a water-stress index. Int. J. Remote Sens.
**2002**, 23, 5135–5139. [Google Scholar] [CrossRef] - Blackburn, G.A. Spectral Indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. Int. J. Remote Sens.
**1998**, 19, 657–675. [Google Scholar] [CrossRef] - Metternicht, G. Vegetation indices derived from high-resolution airborne videography for precision crop management. Int. J. Remote Sens.
**2003**, 24, 2855–2877. [Google Scholar] [CrossRef] - Schlerf, M.; Atzberger, C.; Hill, J. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens. Environ.
**2005**, 95, 177–194. [Google Scholar] [CrossRef] - Merton, R.; Huntington, J. Early simulation of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. In Proceedings of the Summaries of the Eight JPL Airborne Earth Science Workshop, Pasadena, CA, USA, 8–11 February 1999; JPL Publication: Pasadena, CA, USA, 1999; Volume 99, pp. 299–307. [Google Scholar]
- Maccioni, A.; Agati, G.; Mazzinghi, P. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J. Photochem. Photobiol. B Biol.
**2001**, 61, 52–61. [Google Scholar] [CrossRef] [PubMed] - Buschman, C.; Nagel, E. In vivo spectroscopy and internal optics of leaves as a basis for remote sensing of vegetation. Int. J. Remote Sens.
**1993**, 14, 711–722. [Google Scholar] [CrossRef] - Huete, R.A. A Soil-Adjusted Vegetation Index (SAVI). Remote Sens. Environ.
**1988**, 20, 100–120. [Google Scholar] [CrossRef] - McKee, T.B.; Doesken, N.J.; Kliest, J. The relationship of drought frequency and duration to time scales. In Proceedings of the Eighth Conference on Applied Climatology, American Meteorological Society, Anaheim, CA, USA, 17–22 January 1993; pp. 179–184. [Google Scholar]
- Broge, N.H.; Leblanc, E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy Chlorophyll density. Remote Sens. Environ.
**2001**, 76, 156–172. [Google Scholar] [CrossRef] - Gitelson, A.A.; Kaufman, Y.J.; Stark, R.; Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ.
**2002**, 80, 76–87. [Google Scholar] [CrossRef] - Zarco-Tejada, P.J.; Miller, J.R.; Noland, T.L.; Mohammed, G.H.; Sampson, P.H. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans. Geosci. Remote Sens.
**2001**, 39, 1491–1507. [Google Scholar] [CrossRef] - Penuelas, J.; Pinol, J.; Ogaya, R.; Filella, I. Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). Int. J. Remote Sens.
**1997**, 18, 2869–2875. [Google Scholar] [CrossRef] - Kursa, M.B.; Rudnicki, W.R. Feature selection with the Boruta package. J. Stat. Softw.
**2010**, 36, 11. [Google Scholar] [CrossRef] - Porder, S.; Asner, G.P.; Vitousek, P.M. Ground-based and remotely sensed nutrient availability across a tropical landscape. Proc. Natl. Acad. USA
**2005**, 102, 10909–10912. [Google Scholar] [CrossRef] - Kokaly, R.F.; Asner, G.P.; Ollinger, S.V.; Martin, M.E.; Wessman, C.A. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens. Environ.
**2009**, 113 (Suppl. 1), S78–S91. [Google Scholar] [CrossRef] - Curran, P.J. Remote sensing of foliar chemistry. Remote Sens. Environ.
**1989**, 30, 271–278. [Google Scholar] [CrossRef] - Wang, J.; Shi, T.; Liu, H.; Wu, G. Successive projections algorithm-based three-band vegetation index for foliar phosphorus estimation. Ecol. Indic.
**2016**, 67, 12–20. [Google Scholar] [CrossRef] - Berger, K.; Verrelst, J.; Féret, J.B.; Hank, T.; Wocher, M.; Mauser, W.; Camps-Valls, G. Retrieval of aboveground crop nitrogen content with a hybrid machine learning method. Int. J. Appl. Earth Obs. Geoinf.
**2020**, 92, 174. [Google Scholar] [CrossRef] - Homolová, L.; Malenovský, Z.; Clevers, J.G.P.W.; García-Santos, G.; Schaepman, M.E. Review of optical-based remote sensing for plant trait mapping. Ecol. Complex.
**2013**, 15, 1–16. [Google Scholar] [CrossRef] - Huber, S.; Kneubühler, M.; Psomas, A.; Itten, K.; Zimmermann, N.E. Estimating foliar biochemistry from hyperspectral data in mixed forest canopy. For. Ecol. Manag.
**2008**, 256, 491–501. [Google Scholar] [CrossRef] - van Deventer, H.; Cho, M.A.; Mutanga, O.; Ramoelo, A. Capability of models to predict leaf N and P across four seasons for six sub-tropical forest evergreen trees. ISPRS J. Photogramm. Remote Sens.
**2015**, 101, 209–220. [Google Scholar] [CrossRef] - Peng, Y.; Zhang, M.; Xu, Z.; Yang, T.; Su, Y.; Zhou, T.; Wang, H.; Wang, Y.; Lin, Y. Estimation of leaf nutrition status in degraded vegetation based on field survey and hyperspectral data. Sci. Rep.
**2020**, 10, 4361. [Google Scholar] [CrossRef] [PubMed] - Loozen, Y.; Rebel, K.T.; de Jong, S.M.; Lu, M.; Ollinger, S.V.; Wassen, M.J.; Karssenberg, D. Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method. Remote Sens. Environ.
**2020**, 247, 111933. [Google Scholar] [CrossRef] - Zhang, J.; He, N.; Liu, C.; Xu, L.; Chen, Z.; Li, Y.; Wang, R.; Yu, G.; Sun, W.; Xiao, C.; et al. Variation and evolution of C:N ratio among different organs enable plants to adapt to N-limited environments. Glob. Change Biol.
**2020**, 26, 2534–2543. [Google Scholar] [CrossRef] - Song, S.; Xiong, K.; Chi, Y. Ecological stoichiometric characteristics of plant–soil–microorganism of grassland ecosystems under different restoration modes in the karst desertification area. Agronomy
**2023**, 13, 2016. [Google Scholar] [CrossRef] - Koerselman, W.; Meuleman, A. The vegetation N:P ratio: A new tool to detect the nature of nutrient limitation. J. Appl. Ecol.
**1996**, 33, 1441–1450. [Google Scholar] [CrossRef] - Yang, X.; Yuan, Z.; Ye, Y.; Wang, D.; Hua, K.; Guo, Z. Winter wheat total nitrogen content estimation based on UAV hyperspectral remote sensing. Spectrosc. Spectr. Anal.
**2022**, 42, 3269–3274. [Google Scholar] [CrossRef] - Lapaz Olveira, A.; Saínz Rozas, H.; Castro-Franco, M.; Carciochi, W.; Nieto, L.; Balzarini, M.; Ciampitti, I.; Reussi Calvo, N. Monitoring corn nitrogen concentration from radar (C-SAR), optical, and sensor satellite data fusion. Remote Sens.
**2023**, 15, 824. [Google Scholar] [CrossRef]

**Figure 1.**Geographical situation of the study area (

**a**,

**b**) in Shaanxi Province, north China; (

**c**) orthophoto view of the study area.

**Figure 2.**Foliar carbon (C), nitrogen (N), and phosphorus (P) concentrations of eight dominant plant communities in the study area.

**Figure 3.**Technical roadmap for foliar C, N, and P concentrations mapping. Note: LiDAR, laser imaging, detection, and ranging; MAE, mean absolute error; RMSE, root mean squared error; R

^{2}, coefficient of determination.

**Figure 4.**Selection results of the features of (

**a**) C, (

**b**) N, and (

**c**) P. All features were ranked according to the Z score calculated by the Boruta algorithm. The red, yellow, and green box plots represent the Z scores for the rejected, tentative, and confirmed features, respectively. For C, red-edge bands, height variables, and vegetation structure parameters were identified as comparatively important. For N, textural features, height percentiles of 40–95%, and vegetation structure parameters were deemed significant. For P, spectral features, a height percentile of 80%, and 1 m foliage height diversity were considered crucial.

**Figure 8.**Study site maps produced using the random forest model for foliar: (

**a**) C, (

**b**) N, and (

**c**) P concentrations.

Min | Max | Mean | Standard Deviation | |
---|---|---|---|---|

C (%) | 34.46 | 52.28 | 45.80 | 2.36 |

N (%) | 1.09 | 4.06 | 2.62 | 0.87 |

P (g/kg) | 0.63 | 3.08 | 1.73 | 0.49 |

No. | Texture Feature | Formula |
---|---|---|

1 | Mean | $\sum _{i=0}^{N-1}{\displaystyle \sum _{j=0}^{N-1}iP(i,j)}$ |

2 | Variance | $\sum _{i=0}^{N-1}{\displaystyle \sum _{j=0}^{N-1}{(i-u)}^{2}P(i,j)}$ |

3 | Entropy | $\sum _{i=0}^{N-1}{\displaystyle \sum _{j=0}^{N-1}P(i,j)\mathrm{log}P(i,j)}$ |

4 | Data range | $P(i,j)\mathrm{max}-P(i,j)\mathrm{min}$ |

5 | Skewness | $\frac{1}{N}{\displaystyle \sum _{i=0}^{N-1}{\displaystyle \sum _{j=0}^{N-1}{{\displaystyle \left(\frac{P(i,j)-u}{\sigma}\right)}}^{3}}}$ |

No. | Indices | Formula |
---|---|---|

1 | CI_{green} [36] | $({R}_{780}/{R}_{550})-1$ |

2 | CI_{red_edge} [36] | $({R}_{780}/{R}_{710})-1$ |

3 | DD [37] | $({R}_{750}-{R}_{722})/({R}_{700}-{R}_{670})$ |

4 | DVI [38] | ${R}_{800}-{R}_{670}$ |

5 | EVI [39] | $[0.5\times ({R}_{800}-{R}_{638})]/({R}_{800}+2.5\times {R}_{800}-6.0\times {R}_{472}+7.5)$ |

6 | GM [36] | ${R}_{750}/{R}_{702}$ |

7 | GNDVI [36] | $({R}_{780}-{R}_{552})/({R}_{780}+{R}_{552})$ |

8 | LCI [40] | $({R}_{850}-{R}_{710})/({R}_{850}+{R}_{682})$ |

9 | MCARI [41] | $[({R}_{720}-{R}_{680})-0.2\times ({R}_{720}-{R}_{550})]\times ({R}_{720}/{R}_{680})]$ |

10 | mND_{705} [42] | $({R}_{750}-{R}_{705})/({R}_{750}+{R}_{705}-2\times {R}_{445})$ |

11 | MSAVI [43] | $0.5\times (2\times {R}_{800}+1-\sqrt{{(2\times {R}_{800}+1)}^{2}-8\times ({R}_{800}-{R}_{680})})$ |

12 | mSR_{705} [37] | $({R}_{750}-{R}_{445})/({R}_{705}-{R}_{445})$ |

13 | MTVI1 [44] | $1.2\times [1.2\times ({R}_{802}-{R}_{550})-2.5\times ({R}_{670}-{R}_{550})]$ |

14 | NDI [36] | $({R}_{834}-{R}_{662})/({R}_{834}+{R}_{662})$ |

15 | NDVI [45] | $({R}_{450}-{R}_{680})/({R}_{450}+{R}_{680})$ |

16 | NPCI [46] | $({R}_{450}-{R}_{680})/({R}_{450}+{R}_{680})$ |

17 | PBI [47] | ${R}_{810}/{R}_{562}$ |

18 | PRI [48] | $({R}_{531}-{R}_{570})/({R}_{531}+{R}_{570})$ |

19 | PSND_{a} [49] | $({R}_{802}-{R}_{682})/({R}_{802}+{R}_{682})$ |

20 | PSND_{b} [49] | $({R}_{802}-{R}_{634})/({R}_{802}+{R}_{634})$ |

21 | PVR [50] | $({R}_{550}-{R}_{650})/({R}_{550}+{R}_{650})$ |

22 | RVI [51] | ${R}_{800}/{R}_{670}$ |

23 | RVSI [52] | $({R}_{714}-{R}_{752})/2-{R}_{733}$ |

24 | R_{680} [53] | Reflectance at 680 nm |

25 | R_{800} [54] | Reflectance at 800 nm |

26 | SAVI [55] | $({R}_{800}-{R}_{670})/[1.5\times ({R}_{800}+{R}_{670}+0.5)]$ |

27 | SPI [56] | $({R}_{802}-{R}_{450})/({R}_{802}+{R}_{682})$ |

28 | SRPI [46] | ${R}_{430}/{R}_{680}$ |

29 | TVI [57] | $0.5\times [120\times ({R}_{750}-{R}_{550})-200\times ({R}_{670}-{R}_{550})]$ |

30 | VARI [58] | $({R}_{730}-{R}_{662})/({R}_{730}+{R}_{662})$ |

31 | VOG_{a} [59] | ${R}_{742}/{R}_{722}$ |

32 | VOG_{2} [59] | $({R}_{742}-{R}_{746})/({R}_{714}+{R}_{722})$ |

33 | WI [60] | ${R}_{900}/{R}_{970}$ |

_{680}in the table indicates the spectral reflectance at 680 nm; other parameters are analogized. Chlorophyll index—green (CI

_{green}); chlorophyll index—red edge (CI

_{red_edge}); double difference index (DD); difference vegetation index (DVI); enhanced vegetation index (EVI); Gitelson and Merzlyak index (GM); green normalized difference vegetation index (GNDVI); land cover index (LCI); modified chlorophyll absorption in reflectance index (MCARI); modified normalized difference (mND

_{705}); modified simple ratio (mSR

_{705}); modified soil-adjusted vegetation index (MSAVI); modified triangular vegetation index (MTVI1); normalized vegetation index (NDI); normalized difference vegetation index (NDVI); normalized pigment chlorophyll index (NPCI); plant biochemical index (PBI); photochemical reflectance index (PRI); pigment specific normalized difference of chlorophyll a (PSND

_{a}); pigment specific normalized difference of chlorophyll b (PSND

_{b}); perpendicular vegetation reflectance (PVR); ratio vegetation index (RVI); red edge vegetation stress index (RVSI); the range of leaf reflectance at 680 nm (R

_{680}); the range of leaf reflectance at 800 nm (R

_{800}); soil-adjusted vegetation index (SAVI); standardized precipitation index (SPI); simple ratio pigment index (SRPI); triangle vegetation index (TVI); visual atmospheric resistant index (VARI); Vogelmann red edge index 1 (VOG

_{a}); Vogelmann red edge index 2 (VOG

_{2}); water index (WI).

No. | LiDAR Features | Variable Symbols |
---|---|---|

1 | Maximum height | H_{max} |

2 | Minimum height | H_{min} |

3 | Average height | H_{mean} |

4 | Height kurtosis | H_{kurt} |

5 | Median | H_{median_z} |

6 | Height skewness | H_{skew} |

7 | Height standard deviation | H_{std} |

8 | Height variance | H_{var} |

9 | Canopy relief ratio | H_{crr} |

10 | Canopy density metrics | d_{0}, d_{1}, d_{2}, d_{3}, d_{4}, d_{5}, d_{6}, d_{7}, d_{8}, d_{9} |

11 | Height percentile | HP_{1st}, HP_{5th}, HP_{10th}, HP_{20th}, HP_{25th}, HP_{30th}, HP_{40th}, HP_{50th}, HP_{60th}, HP_{70th}, HP_{75th}, HP_{80th}, HP_{90th}, HP_{95th}, HP_{99th} |

12 | Canopy height | CHM |

13 | Canopy cover | CC |

14 | Gap fraction | GF |

15 | Leaf area index | LAI |

16 | Foliage height diversity | FHD-1m, FHD-2m, FHD-3m |

Model | Feature Combination | R^{2} | RMSE | MAE | |
---|---|---|---|---|---|

C | Causal bands | (970, 990 nm) | 0.07 | 5.16 | 3.64 |

Multiple linear regression model | Hyperspectral features | 0.34 | 4.69 | 3.11 | |

Hyperspectral + LiDAR | 0.42 | 2.24 | 2.13 | ||

Random forest model | Hyperspectral features | 0.38 | 3.47 | 2.74 | |

Hyperspectral + LiDAR | 0.56 | 1.19 | 1.00 | ||

N | Causal bands | (510, 700–750, 910 nm) | 0.20 | 0.92 | 0.60 |

Multiple linear regression model | Hyperspectral features | 0.46 | 0.87 | 0.52 | |

Hyperspectral + LiDAR | 0.48 | 0.85 | 0.48 | ||

Random forest model | Hyperspectral features | 0.53 | 0.57 | 0.46 | |

Hyperspectral + LiDAR | 0.53 | 0.57 | 0.46 | ||

P | Causal bands | (400–900 nm) | 0.32 | 0.52 | 0.45 |

Multiple linear regression model | Hyperspectral features | 0.37 | 0.51 | 0.36 | |

Hyperspectral + LiDAR | 0.39 | 0.50 | 0.36 | ||

Random forest model | Hyperspectral features | 0.43 | 0.40 | 0.31 | |

Hyperspectral + LiDAR | 0.44 | 0.40 | 0.31 |

Hyperspectral Features | C | N | P | LiDAR Features | C | N | P |
---|---|---|---|---|---|---|---|

R_{399} | √ | d_{6} | √ | ||||

R_{404} | √ | √ | d_{8} | √ | |||

R_{409} | √ | d_{9} | √ | ||||

R_{415} | √ | √ | H_{max} | √ | |||

R_{420} | √ | √ | H_{mean} | √ | √ | ||

R_{451} | √ | H_{median_z} | √ | √ | |||

R_{462} | √ | HP_{1st} | √ | ||||

R_{473} | √ | HP_{10th} | √ | ||||

R_{483} | √ | HP_{20th} | √ | ||||

R_{504} | √ | HP_{25th} | √ | ||||

R_{510} | √ | HP_{30th} | √ | ||||

R_{515} | √ | HP_{40th} | √ | √ | |||

R_{758} | √ | HP_{50th} | √ | ||||

R_{763} | √ | HP_{60th} | √ | √ | |||

EVI | √ | √ | HP_{70th} | √ | √ | ||

GNDVI | √ | HP_{75th} | √ | √ | |||

MTVI1 | √ | HP_{80th} | √ | √ | √ | ||

NPCI | √ | HP_{90th} | √ | √ | |||

PVR | √ | HP_{95th} | √ | √ | |||

R_{680} | √ | HP_{99th} | √ | ||||

SRPI | √ | H_{std} | √ | √ | |||

WI | √ | √ | H_{var} | √ | √ | ||

B_{1}-Data Range | √ | CC | √ | ||||

B_{1}-Mean | √ | √ | CHM | √ | √ | ||

B_{1}-Variance | √ | FHD-1m | √ | √ | √ | ||

B_{3}-Data Range | √ | √ | FHD-2m | √ | |||

B_{3}-Variance | √ | √ | FHD-3m | √ | |||

/ | / | / | / | GF | √ |

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## Share and Cite

**MDPI and ACS Style**

Yang, Y.; Dong, J.; Tang, J.; Zhao, J.; Lei, S.; Zhang, S.; Chen, F.
Mapping Foliar C, N, and P Concentrations in An Ecological Restoration Area with Mixed Plant Communities Based on LiDAR and Hyperspectral Data. *Remote Sens.* **2024**, *16*, 1624.
https://doi.org/10.3390/rs16091624

**AMA Style**

Yang Y, Dong J, Tang J, Zhao J, Lei S, Zhang S, Chen F.
Mapping Foliar C, N, and P Concentrations in An Ecological Restoration Area with Mixed Plant Communities Based on LiDAR and Hyperspectral Data. *Remote Sensing*. 2024; 16(9):1624.
https://doi.org/10.3390/rs16091624

**Chicago/Turabian Style**

Yang, Yongjun, Jing Dong, Jiajia Tang, Jiao Zhao, Shaogang Lei, Shaoliang Zhang, and Fu Chen.
2024. "Mapping Foliar C, N, and P Concentrations in An Ecological Restoration Area with Mixed Plant Communities Based on LiDAR and Hyperspectral Data" *Remote Sensing* 16, no. 9: 1624.
https://doi.org/10.3390/rs16091624