Alpine timberline is a great place for monitoring climate change. The study of alpine and subalpine timberline in Qinling Mountains has led to early warning that reveals the response and adaptation of terrestrial vegetation ecosystem to climate change. Based on the remote sensing image classification method, the typical timberline area in Qinling Mountains was determined. Temperature and normalized difference vegetation index (NDVI) data were extracted from the typical timberline area based on spatial interpolation and NDVI data. The relationship between NDVI and temperature change and the critical temperature value affecting vegetation response in the timberline area in Qinling Mountains were analyzed. Correlation between NDVI and air temperature in the alpine and subalpine timberline areas of Qinling Mountains exhibited an upward trend, which implied that temperature promotes vegetation activity. A strong correlation between temperature and NDVI in typical timberline areas of Qinling Mountains, and a significant correlation between temperature and NDVI in the early growing season. A phenomenon of NDVI lagging behind air temperature was observed. Temperature response showed synchronization and hysteresis. The correlation between cumulative temperature and vegetation was similar between Taibai Mountain and Niubeiliang timberline, and the correlation between NDVI in April and cumulative temperature in the first 12 months was the strongest. Temperature threshold range of Taibai Mountain timberline played a dominant role in vegetation growth. Our results provide insights and basis for future studies of early warning signs of climate change, specifically between 0.34 and 1.34 °C. The threshold ranges of temperature response of different vegetation types vary. Compared with alpine shrub meadow, the threshold ranges of temperature effect of Coniferous forest and Larix chinensis Beissn. are smaller, implying that these vegetation types are more sensitive to temperature change.

Under the current background of carbon neutrality, the quantification of carbon neutrality in Qinling Mountains, the calculation of the spatial service scope, and the estimation of economic value are of great significance for the improvement of carbon neutrality spatial planning and the carbon trading market. The Intelligent Urban Ecosystem Management System (IUEMS) was used to calculate the carbon sequestration of the Qinling Mountains. The carbon emission of Qinling Mountains was spatialized through model fitting, using the night light data and energy consumption data of various cities. The static carbon content of the Qinling Mountains was obtained. Combined with the common gas diffusion coefficient, under normal temperature and pressure, the service range of the Qinling Mountains’ carbon neutralization to the surrounding area was calculated. The spatial distribution of carbon sequestration in 2030 and 2050 was simulated using Patch generating Land Use Simulation (PLUS) model and Net Present Value (NPV) model. Under two discount rates, the model could calculate the carbon value in 2012, 2016, 2020, 2030, and 2050, respectively. The results proved that the value of carbon in the Qinling Mountains is gradually increasing. Since 2012, the service range of carbon neutralization in the Qinling Mountains was 175–262.63 km, and the service range has been gradually expanded. The discount rate is inversely proportional to the carbon value.

Similarity search is a foundational task in time series data mining. Although there are many ways to measure the similarity of time series, a lot of evidence indicates that dynamic time warping (DTW) has the best robustness in many applications. Unfortunately, the expensive computational cost limits its application in large-scale databases. To speed up similarity search under DTW, we design a framework of two-stage similarity search for time series. In the first stage, we propose an improved lower bounding distance, which can be used to discard plenty of dissimilar series to get a set of candidate sequences. In the second stage, to efficiently get the retrieval result from the set of candidate sequences, we explore early abandoning strategy to avoid the full calculation of DTW. Extensive experiments are conducted on real-world data sets. The experimental results indicate that the proposed method can improve the retrieval efficiency of similarity search under DTW and guarantee no false dismissals.

Aiming at the two-dimensional (2D) incoherently distributed (ID) sources, we explore a direction-of-arrival (DOA) estimation algorithm based on uniform rectangular arrays (URA). By means of Taylor series expansion of steering vector, rotational invariance relations with regard to nominal azimuth and nominal elevation between subarrays are deduced under the assumption of small angular spreads and small sensors distance firstly; then received signal vectors can be described by generalized steering matrices and generalized signal vectors; thus, an estimation of signal parameters via rotational invariance techniques (ESPRIT) like algorithm is proposed to estimate nominal elevation and nominal azimuth respectively using covariance matrices of constructed subarrays. Angle matching method is proposed by virtue of Capon principle lastly. The proposed method can estimate multiple 2D ID sources without spectral searching and without information of angular power distribution function of sources. Investigating different SNR, sources with different angular power density functions, sources in boundary region, distance between sensors and number of sources, simulations are conducted to investigate the effectiveness of the proposed method.