Inter-annual variations of vegetation dynamics to climate change in Ordos, Inner Mongolia, China.

To reveal the characteristics of climate change and the controlling factors for vegetation dynamics in the Ordos, Inner Mongolia, China, 34 years (1982-2015) of regional climate variables and vegetation dynamics were investigated. The results show that: Annual mean air temperature (TMP) significantly increased with a linear slope of 0.473°C/10yr. Annual precipitation (PRE) had a non-significant positive trend nearly 5 times lower than the trend of potential evapotranspiration (PET). The average Normalized Difference Vegetation Index (NDVI) computed for the region was found to show a significant positive trend (6.131×10-4/yr). However, all climate variables displayed non-significant correlations with NDVI at annual scale. The reduction of desert and the increase of grassland over the past decades were accountable for the increased NDVI. Principal components analysis revealed that the regional climate change can be characterized as changes in temperature, humidity and the availability of radiant energy. Based on principal components regression coefficients, NDVI was mostly sensitive to humidity component, followed by growing season warmth (WMI). Spatially, 93.1% of the pixels displayed positive trend and 61.8% of the pixels displayed significant change over the past decades. Both principal regression analysis and partial correlation analysis revealed that NDVI in eastern part of Ordos was sensitive to TMP, whereas, NDVI in southern and western areas of Ordos displayed the high sensitivity to combined effects of PRE and cloud coverage (CLD). Partial correlation analyses also revealed that TMX was a surrogate for aridity, TMN was a representative of humidity, and temperature variations below the threshold of 5°C (CDI) were less important than WMI. We conclude that regional climate change can be characterized by warming and increased aridity. The significant positive trend of regional NDVI and the non-significant correlations between NDVI and climate variables at annual scale suggests the hidden role of the human activities.

Crop/Weed Discrimination Using a Field Imaging Spectrometer System.

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.

Investigation of temperature and aridity at different elevations of Mt. Ailao, SW China.

Our current understanding is that plant species distribution in the subtropical mountain forests of Southwest China is controlled mainly by inadequate warmth. Due to abundant annual precipitation, aridity has been less considered in this context, yet rainfall here is highly seasonal, and the magnitude of drought severity at different elevations has not been examined due to limited access to higher elevations in this area.In this study, short-term micrometeorological variables were measured at 2,480 m and 2,680 m, where different forest types occur. Drought stress was evaluated by combining measurements of water evaporation demand (E p) and soil volumetric water content (VWC). The results showed that: (1) mean temperature decreased 1 °C from 2,480 m to 2,680 m and the minimum temperature at 2,680 m was above freezing. (2) Elevation had a significant influence on E p; however, the difference in daily E p between 2,480 m and 2,680 m was not significant, which was possibly due to the small difference in elevation between these two sites. (3) VWC had larger range of annual variation at 2,680 m than at 2,480 m, especially for the surface soil layer.We conclude that the decrease in temperature does not effectively explain the sharp transition between these forest types. During the dry season, plants growing at 2,680 m are likely to experience more drought stress. In seeking to understand the mountain forest distribution, further studies should consider the effects of drought stress alongside those of altitude.