Combining Spectral and Texture Features of UAS-Based Multispectral Images for Maize Leaf Area Index Estimation

The leaf area index (LAI) is of great significance for crop growth monitoring. Recently, unmanned aerial systems (UASs) have experienced rapid development and can provide critical data support for crop LAI monitoring. This study investigates the effects of combining spectral and texture features extracted from UAS multispectral imagery on maize LAI estimation. Multispectral images and in situ maize LAI were collected from test sites in Tongshan, Xuzhou, Jiangsu Province, China. The spectral and texture features of UAS multispectral remote sensing images are extracted using the vegetation indices (VIs) and the gray-level co-occurrence matrix (GLCM), respectively. Normalized texture indices (NDTIs), ratio texture indices (RTIs), and difference texture indices (DTIs) are calculated using two GLCM-based textures to express the influence of two different texture features on LAI monitoring at the same time. The remote sensing features are prescreened through correlation analysis. Different data dimensionality reduction or feature selection methods, including stepwise selection (ST), principal component analysis (PCA), and ST combined with PCA (ST_PCA), are coupled with support vector regression (SVR), random forest (RF), and multiple linear regression (MLR) to build the maize LAI estimation models. The results reveal that ST_PCA coupled with SVR has better performance, in terms of the VIs + DTIs (R2 = 0.876, RMSE = 0.239) and VIs + NDTIs (R2 = 0.877, RMSE = 0.236). This study introduces the potential of different texture indices for maize LAI monitoring and demonstrates the promising solution of using ST_PCA to realize the combining of spectral and texture features for improving the estimation accuracy of maize LAI.

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Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing

Remote Sensing ◽

10.3390/rs13163263 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3263

Author(s):

Zhijie Liu ◽

Pengju Guo ◽

Heng Liu ◽

Pan Fan ◽

Pengzong Zeng ◽

...

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Shaanxi Province ◽

Gradient Boosting ◽

Support Vector ◽

Apple Orchards ◽

Apple Trees ◽

Area Index ◽

Spatial Efficiency

The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees.

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A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index

Remote Sensing ◽

10.3390/rs12010016 ◽

2019 ◽

Vol 12 (1) ◽

pp. 16 ◽

Cited By ~ 3

Author(s):

Naichen Xing ◽

Wenjiang Huang ◽

Qiaoyun Xie ◽

Yue Shi ◽

Huichun Ye ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Vegetation Indices ◽

Saturation Effect ◽

Estimation Accuracy ◽

Growth Monitoring ◽

Area Index ◽

Multispectral Data ◽

Red Edge

Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R2 and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.

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Time series 30m resolution leaf area index estimation and vegetation change monitoring in Saihanba, China

10.5194/egusphere-egu2020-12577 ◽

2020 ◽

Author(s):

Hongmin Zhou ◽

Guodong Zhang ◽

Changjing Wang ◽

Jindi Wang

Keyword(s):

Time Series ◽

High Resolution ◽

Leaf Area Index ◽

Leaf Area ◽

Dynamic Model ◽

Support Vector ◽

Growth Monitoring ◽

Area Index ◽

Modis Lai ◽

Reflectance Data

<p>Leaf area index (LAI) is one of the most important biophysical variables for regulating the physiological processes of vegetation canopy. Time series high-resolution LAI data is critical for vegetation growth monitoring, surface process simulation and global change research. However, there are no high-resolution LAI data products that are continuous in time and space. In this paper, we use MODIS LAI products and Landsat surface reflectance data to generate time series high-resolution LAI datasets from 2000 to 2018 in Saihanba based on the ensemble kalman filter, and uses time-series LAI data to monitor surface vegetation changes according to the Prophet model. Firstly, the multi-step Savitzky&#8211;Golay filtering algorithm is used to smooth the MODIS LAI data, and the upper envelope of time series LAI is generated. A dynamic model is constructed according to the trend of LAI upper envelope to provide the short-range forecast of LAI. Then the ground measured LAI data and the corresponding Landsat reflectance data are used to train a Back Propagation neural network. High-resolution LAI data from BP model is used to update the dynamic model in real time to generate high-resolution time series LAI data based on EnKF. Finally, the time series LAI data is used as the input of Prophet deep learning model to obtain the LAI time series prediction values of a certain year. Comparing the prediction results with the LAI of current year, the correlation coefficient and the root mean square error distribution maps can be obtained, a support vector machine method is used to classify the disturbed pixels and the normal pixels. The LAI time series estimation has a high accuracy of R&#178;larger than 0.90, and RMSE less than 0.54. The disturbance monitoring results indicate that vegetation in 2009, 2010, 2013, 2014, 2015, 2017 is seriously disturbed, Variation of meteorological conditions and deforest contributes heavily to the disturbance.</p>

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Estimation of Forest Leaf Area Index from Remote Sensing Data Using the Algorithm Based on Geometric-optical Model

Geo-information Science ◽

10.3724/sp.j.1047.2012.00358 ◽

2012 ◽

Vol 14 (3) ◽

pp. 358-365

Author(s):

Hanyue CHEN ◽

Wenjiang HUANG ◽

Zheng NIU ◽

Shuai GAO

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Optical Model ◽

Remote Sensing Data ◽

Area Index ◽

Sensing Data

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UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

Remote Sensing ◽

10.3390/rs13040803 ◽

2021 ◽

Vol 13 (4) ◽

pp. 803

Author(s):

Lingchen Lin ◽

Kunyong Yu ◽

Xiong Yao ◽

Yangbo Deng ◽

Zhenbang Hao ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Point Cloud ◽

Forest Canopy ◽

Estimation Method ◽

Vertical Direction ◽

Estimation Accuracy ◽

3D Point Cloud ◽

Area Index ◽

Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

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Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

Remote Sensing ◽

10.3390/rs13081427 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1427

Author(s):

Kasturi Devi Kanniah ◽

Chuen Siang Kang ◽

Sahadev Sharma ◽

A. Aldrie Amir

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Primary Productivity ◽

Peninsular Malaysia ◽

Area Index ◽

Mangrove Area ◽

The Mean ◽

Shape Complexity ◽

The Impact

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

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