Search

Fertilizer overuse is a common phenomenon in global agroforestry production, and this overuse causes ecological destruction. The ability to accurately estimate the nutrient content of plant leaves in real-time would be a wonderful solution to reduce the degree of environmental damage. In recent years, remote sensing technology has been widely used in the diagnosis of crop nutrition in many countries. Most studies focus on optimal band selection or create new vegetation indices, but these studies have ignored the random impact of natural environmental factors on the estimated results. This paper proposed an estimation model of total nitrogen content (TNC) in sandalwood leaves that takes sampling season and site conditions as the dummy variable and random effect, respectively. Three forestry farms with different locations and site conditions were selected as study areas to enhance the universality of this model. Multispectral images of leaves were obtained using a low-cost five-band camera (RedEdge3, MicaSense, USA), and the experimental results indicate the following: (1) the growth of the tree height, crown width and stem effectively increased under the medium gradient level (N2), whereas a high gradient level (N3) significantly promoted all aspects except tree height; (2) the mean and variance of some image texture features of the G, RE and NIR band were significantly correlated with TNC at the 0.05 and 0.01 levels, and the texture mean value index (TMVI) proposed in this paper can improve the correlation with TNC; and (3) the results obtained using the nonlinear mixed-effects model with dummy variables improved the fitting degree and estimation accuracy compared with results of SVR and BPNN. This study demonstrates the advantages of using the nonlinear mixed-effects model with dummy variables to obtain a more reliable estimation model for the nutritional diagnosis of rare tree species. [ABSTRACT FROM AUTHOR]

Accurately estimating and mapping forest aboveground carbon density (AGCD) is important for evaluating forest carbon sequestration efficiency and dynamics. This study compares different modelling approaches for estimating AGCD using Landsat 8 Operational Land Imager (OLI) imagery and field forest inventory data. We tested it in Yongshun County, Hunan Province, China. Linear regression (LR) and Random Forest (RF) were used to map crown density (CD). We fitted models with and without CD dummy variables for AGCD estimations. The fitting results and performances of these two types of AGCD models were analysed and compared. There was a difference in the estimation of remote-sensing-based CD models and AGCD models; the coefficients of determination (R2) were 0.66 for LR and 0.84 for RF, respectively, in CD estimations; R2 values were 0.46 for LR and 0.65 for RF, respectively, in AGCD estimations as indicated by basic AGCD models. RF was the best algorithm for CD and AGCD estimations. All of the dummy variable AGCD models provided more accurate AGCD estimates than their basic AGCD counterparts. The dummy variable models (DLR and DRF), which took the observed CD as a dummy variable, were the most accurate for the R2 were 0.61 and 0.85, respectively. The AGCD estimations were significantly overestimated and underestimated by basic AGCD models in thin and dense CD classes, respectively. Nevertheless, none of the AGCD estimates produced by any of the dummy variable models showed this problem. The models that used predicted CD as a dummy variable were more consistent with those that considered observed CD as a dummy variable. The accuracies of AGCD estimations were significantly improved by the dummy variable AGCD models, especially in thin and dense plots; in addition, the CD estimations produced by CD models could be used as dummy variables in AGCD remote-sensing-based models. This work provides that a method considering CD dummy variable in remote sensing-based models was reliable for mapping AGCD at a regional scale. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)