Nowadays, sensors begin to play an essential role in smart-agriculture practices. Spectroscopy and the ground-based sensors have inspired widespread interest in the field of weed detection. Most studies focused on detection under ideal conditions, such as indoor or under artificial lighting, and more studies in the actual field environment are needed to test the applicability of this sensor technology. Meanwhile, hyperspectral image data collected by imaging spectrometer often has hundreds of channels and, thus, are large in size and highly redundant in information. Therefore, a key element in this application is to perform dimensionality reduction and feature extraction. However, the processing of highly dimensional spectral imaging data has not been given due attention in recent studies. In this study, a field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed and used to discriminate carrot and three weed species (purslane, humifuse, and goosegrass) in the crop field. Dimensionality reduction was performed on the spectral data based on wavelet transform; the wavelet coefficients were extracted and used as the classification features in the weed detection model, and the results were compared with those obtained by using spectral bands as the classification feature. The classification features were selected using Wilks’ statistic-based stepwise selection, and the results of Fisher linear discriminant analysis (LDA) and the highly dimensional data processing-oriented support vector machine (SVM) were compared. The results indicated that multiclass discrimination among weeds or between crops and weeds can be achieved using a limited number of spectral bands (8 bands) with an overall classification accuracy of greater than 85%. When the number of spectral bands increased to 15, the classification accuracy was improved to greater than 90%; further increasing the number of bands did not significantly improve the accuracy. Bands in the red edge region of plant spectra had strong discriminant capability. In terms of classification features, wavelet coefficients outperformed raw spectral bands when there were a limited number of variables. However, the difference between the two was minimal when the number of variables increased to a certain level. Among different discrimination methods, SVM, which is capable of nonlinear classification, performed better.

This study seeks a further understanding on climate trends in a subtropical mountain forest, SW China. Air (T_a) and soil temperature (T_s), both in open land (1983–2010) and under a forest canopy (1986–2010), were investigated. Short‐term radiation components were also measured simultaneously both in open land and understory to explore the relationships of microclimatic variables. Correlations of T_a and T_s with sunshine hours (S_t) and wind speed (W_s) were also analysed as driving factors of the temperature trends.The results showed that (1) Understory radiation components were greatly reduced by the forest canopy, showing a strong effect of forest canopy on microclimatic variables. T_s_0 in open land was significantly correlated with solar radiation. Wind speed had significant influences on differences between T_a and T_s_0, between open land T_s_0 and understory T_s_0. The long‐term data showed that T_s_0 under forest canopy were closely coupled with T_a in open land. (2) T_a had a larger increase than T_s_0 in open land, and temperature increases in winter were greater than in other seasons. Soil temperature at depths under forest canopy had nearly twice the increases of those on open land; we attributed this to the higher relative increase of W_s over S_t. (3) A slope change in 1998 was detected in the T_s_0 and T_a difference (T_s_0 − T_a) series, suggesting different response of T_s_0 and T_a since that year. Deceleration of S_t and stability of W_s may have been factors.This study improves our understanding of warming in a nature reserve where anthropogenic influences are absent. Further studies are needed for the biological and biochemical implications on subtropical mountain forest. Copyright © 2012 Royal Meteorological Society