Methane adsorption constrained by pore structure in high rank coals using FESEM, CO2 adsorption and NMRC techniques

This research was funded by the National Natural Science Fund (grant nos. 41830427, 41772160 and 41602170), the National Major Research Program for Science and Technology of China (grant no. 2016ZX05043-001), Key Research and Development Projects of The Xinjiang Uygur Autonomous Region (grant no. 2017B03019-01) and the Fundamental Research Funds for Central Universities (grant no. 2652018002).Peer reviewedPublisher PD

Investigation on the methane adsorption capacity in coals : considerations from nanopores by multifractal analysis

ACKNOWLEDGEMENTS This research was funded by the National Natural Science Foundation of China (grant numbers 41830427, 41922016, and 41772160).Peer reviewedPostprin

Size Distribution and Fractal Characteristics of Coal Pores through Nuclear Magnetic Resonance Cryoporometry

This research was funded by the National Natural Science Foundation of China (Grant no. 41602170), the Research Program for Excellent Doctoral Dissertation Supervisor of Beijing (grant no. YB20101141501), the Key Project of Coal-based Science and Technology in Shanxi Province-CBM accumulation model and reservoir evaluation in Shanxi province (grant no. MQ2014-01) and the Fundamental Research Funds for Central Universities (grant no. 35832015136).Peer reviewedPostprin

A novel method for maize leaf disease classification using the RGB-D post-segmentation image data

Maize (Zea mays L.) is one of the most important crops, influencing food production and even the whole industry. In recent years, global crop production has been facing great challenges from diseases. However, most of the traditional methods make it difficult to efficiently identify disease-related phenotypes in germplasm resources, especially in actual field environments. To overcome this limitation, our study aims to evaluate the potential of the multi-sensor synchronized RGB-D camera with depth information for maize leaf disease classification. We distinguished maize leaves from the background based on the RGB-D depth information to eliminate interference from complex field environments. Four deep learning models (i.e., Resnet50, MobilenetV2, Vgg16, and Efficientnet-B3) were used to classify three main types of maize diseases, i.e., the curvularia leaf spot [Curvularia lunata (Wakker) Boedijn], the small spot [Bipolaris maydis (Nishik.) Shoemaker], and the mixed spot diseases. We finally compared the pre-segmentation and post-segmentation results to test the robustness of the above models. Our main findings are: 1) The maize disease classification models based on the pre-segmentation image data performed slightly better than the ones based on the post-segmentation image data. 2) The pre-segmentation models overestimated the accuracy of disease classification due to the complexity of the background, but post-segmentation models focusing on leaf disease features provided more practical results with shorter prediction times. 3) Among the post-segmentation models, the Resnet50 and MobilenetV2 models showed similar accuracy and were better than the Vgg16 and Efficientnet-B3 models, and the MobilenetV2 model performed better than the other three models in terms of the size and the single image prediction time. Overall, this study provides a novel method for maize leaf disease classification using the post-segmentation image data from a multi-sensor synchronized RGB-D camera and offers the possibility of developing relevant portable devices

Vegetation Horizontal Occlusion Index (VHOI) from TLS and UAV Image to Better Measure Mangrove LAI

Accurate measurement of the field leaf area index (LAI) is crucial for assessing forest growth and health status. Three-dimensional (3-D) structural information of trees from terrestrial laser scanning (TLS) have information loss to various extents because of the occlusion by canopy parts. The data with higher loss, regarded as poor-quality data, heavily hampers the estimation accuracy of LAI. Multi-location scanning, which proved effective in reducing the occlusion effects in other forests, is hard to carry out in the mangrove forest due to the difficulty of moving between mangrove trees. As a result, the quality of point cloud data (PCD) varies among plots in mangrove forests. To improve retrieval accuracy of mangrove LAI, it is essential to select only the high-quality data. Several previous studies have evaluated the regions of occlusion through the consideration of laser pulses trajectories. However, the model is highly susceptible to the indeterminate profile of complete vegetation object and computationally intensive. Therefore, this study developed a new index (vegetation horizontal occlusion index, VHOI) by combining unmanned aerial vehicle (UAV) imagery and TLS data to quantify TLS data quality. VHOI is asymptotic to 0.0 with increasing data quality. In order to test our new index, the VHOI values of 102 plots with a radius of 5 m were calculated with TLS data and UAV image. The results showed that VHOI had a strong linear relationship with estimation accuracy of LAI (R2 = 0.72, RMSE = 0.137). In addition, as TLS data were selected by VHOI less than different thresholds (1.0, 0.9, …, 0.1), the number of remaining plots decreased while the agreement between LAI derived from TLS and field-measured LAI was improved. When the VHOI threshold is 0.3, the optimal trade-off is reached between the number of plots and LAI measurement accuracy (R2 = 0.67). To sum up, VHOI can be used as an index to select high-quality data for accurately measuring mangrove LAI and the suggested threshold is 0.30

Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR

Forest biomass is an important descriptor for studying carbon storage, carbon cycles, and global change science. The full-waveform spaceborne Light Detection And Ranging (LiDAR) Geoscience Laser Altimeter System (GLAS) provides great possibilities for large-scale and long-term biomass estimation. To the best of our knowledge, most of the existing research has utilized average tree height (or height metrics) within a GLAS footprint as the key parameter for biomass estimation. However, the vertical distribution of tree height is usually not as homogeneous as we would expect within such a large footprint of more than 2000 m2, which would limit the biomass estimation accuracy vastly. Therefore, we aim to develop a novel canopy height layering biomass estimation model (CHL-BEM) with GLAS data in this study. First, all the trees with similar height were regarded as one canopy layer within each GLAS footprint. Second, the canopy height and canopy cover of each layer were derived from GLAS waveform parameters. These parameters were extracted using a waveform decomposition algorithm (refined Levenberg–Marquardt—RLM), which assumed that each decomposed vegetation signal corresponded to a particular canopy height layer. Third, the biomass estimation model (CHL-BEM) was established by using the canopy height and canopy cover of each height layer. Finally, the CHL-BEM was compared with two typical biomass estimation models of GLAS in the study site located in Ejina, China, where the dominant species was Populus euphratica. The results showed that the CHL-BEM presented good agreement with the field measurement biomass (R2 = 0.741, RMSE = 0.487, %RMSE = 24.192) and achieved a significantly higher accuracy than the other two models. As a whole, we expect our method to advance all the full-waveform LiDAR development and applications, e.g., the newly launched Global Ecosystem Dynamics Investigation (GEDI)

UAV multispectral images for accurate estimation of the maize LAI considering the effect of soil background

The high proportion of soil background pixels in UAV remote sensing images is an important reason for the uncertainty of high-precision leaf area index (LAI) estimation at early growth stages of crops. Although the traditional method of removing soil pixels from images based on canopy coverage (CC) eliminates pure soil pixels, it can cause spectral saturation at the early stages and therefore affect the accuracy of LAI estimation. In this study, a new method called reduced soil contribution (CS) was constructed to improve the accuracy of LAI estimation. This method can be improved by introducing a quantitative method to account for the contribution of soil information, which can be used to correct the calculation of vegetation indices and eliminate soil interference in maize LAI estimation. A six-rotor UAV equipped with a multispectral camera was used to collect field image data. Experimental plots with different maize breeding varieties were laid out to carefully evaluate the accuracy of the estimation model using UAV multispectral images collected at different growth stages. The performance of four estimation models, a light gradient boosting machine, gradient-boosting decision tree, random forest regression model and extreme gradient boosting, for LAI estimation was evaluated. The CS-based approach significantly improved the accuracy of the LAI model estimation, reducing the rRMSE by 1.89% for a single growing season compared to the traditional method. On average, the rRMSE for all growth stages decreased by 3.5%, demonstrating its effectiveness in improving maize LAI estimation accuracy. Randomness in error measured by Moran’s I metrics showed that the GBDT (gradient-boosting decision trees) model based on the CS method showed less spatial aggregation. These results showed that the CS model can effectively reduce the influence of soil on the estimation of the maize LAI and improve the LAI estimation accuracy compared with the direct removal of soil background pixels from an image

Evaluating the Canopy Chlorophyll Density of Maize at the Whole Growth Stage Based on Multi-Scale UAV Image Feature Fusion and Machine Learning Methods

Maize is one of the main grain reserve crops, which directly affects the food security of the country. It is extremely important to evaluate the growth status of maize in a timely and accurate manner. Canopy Chlorophyll Density (CCD) is closely related to crop health status. A timely and accurate estimation of CCD is helpful for managers to take measures to avoid yield loss. Thus, many methods have been developed to estimate CCD with remote sensing data. However, the relationship between the CCD and the features used in these CCD estimation methods at different growth stages is unclear. In addition, the CCD was directly estimated from remote sensing data in most previous studies. If the CCD can be accurately estimated from the estimation results of Leaf Chlorophyll Density (LCD) and Leaf Area Index (LAI) remains to be explored. In this study, Random Forest (RF), Support Vector Machines (SVM), and Multivariable Linear Regression (MLR) were used to develop CCD, LCD, and LAI estimation models by integrating multiple features derived from unmanned aerial vehicle (UAV) multispectral images. Firstly, the performances of the RF, SVM, and MLR trained over spectral features (including vegetation indices and band reflectance; dataset I), texture features (dataset II), wavelet coefficient features (dataset III), and multiple features (dataset IV, including all the above datasets) were analyzed, respectively. Secondly, the CCDP was calculated from the estimated LCD and estimated LAI, and then the CCD was estimated based on multiple features and the CCDP was compared. The results show that the correlation between CCD and different features is significantly different at every growth stage. The RF model trained over dataset IV yielded the best performance for the estimation of LCD, LAI, and CCD (R2 values were 0.91, 0.97, and 0.97, and RMSE values were 6.59 μg/cm2, 0.35, and 24.85 μg/cm2). The CCD directly estimated from dataset IV is slightly closer to the ground truth CCD than the CCDP (R2 = 0.96, RMSE = 26.85 μg/cm2) calculated from LCD and LAI. The results indicated that the CCD of maize can be accurately estimated from multiple multispectral image features at the whole growth stage, and both CCD estimation strategies can be used to estimate the CCD accurately. This study provides a new reference for accurate CCD evaluation in precision agriculture

Improved Crop Biomass Algorithm with Piecewise Function (iCBA-PF) for Maize Using Multi-Source UAV Data

Maize is among the most important grain crops. Aboveground biomass (AGB) is a key agroecological indicator for crop yield prediction and growth status monitoring, etc. In this study, we propose two new methods, improved crop biomass algorithm (iCBA) and iCBA with piecewise function (iCBA-PF), to estimate maize AGB. Multispectral (MS) images, visible-band (RGB) images, and light detection and ranging (LiDAR) data were collected using unmanned aerial vehicles (UAVs). Vegetation indices (VIs) and the VI-weighted canopy volume model (CVMVI) were calculated and used as input variables for AGB estimation. The two proposed methods and three benchmark methods were compared. Results demonstrated that: (1) The performance of MS and RGB data in AGB estimation was similar. (2) AGB was estimated with higher accuracy using CVMVI than using VI, probably because the temporal trends of CVMVI and AGB were similar in the maize growing season. (3) The best estimation method was the iCBA-PF (R2 = 0.90 ± 0.02, RMSE = 190.01 ± 21.55 g/m2), indicating that AGB before and after maize heading should be estimated with different methods. Our method and findings are possibly applicable to other crops with a heading stage