Objective: The influence of climate on forest stands cannot be ignored, but most of the previous forest stand growth models were constructed under the presumption of invariant climate and could not estimate the stand growth under climate change. The model was constructed to provide a theoretical basis for forest operators to take reasonable management measures for fir under the influence of climate. Methods: Based on the survey data of 638 cedar plantation plots in Hunan Province, the optimal base model was selected from four biologically significant alternative stand basal area models, and the significant climate factors without serious covariance were selected by multiple stepwise regression analysis. The optimal form of random effects was determined, and then a model with climatic effects was constructed for the cross-sectional growth of fir plantations. Results: Richards formula is the optimal form of the basic model of stand basal area growth. The coefficient of adjustment  was 0.8355; the average summer maximum temperature  and the water vapor loss  in Hargreaves climate affected the maximum and rate of fir stand stand growth respectively, and were negatively correlated with the stand growth. The adjusted coefficient of determination  of the fir stand area break model with climate effects was 0.8921, the root mean square error (RMSE) was 3.0792, and the mean relative error absolute value (MARE) was 9.9011; compared with the optimal base model,  improved by 6.77%, RMSE decreased by 19.04%, and MARE decreased by 15.95%. Conclusion: The construction of the stand cross-sectional area model with climate effects indicates that climate has a significant influence on stand growth, which supports the rationality of considering climate factors in the growth model, and it is important for the regional stand growth harvest and management of cedar while improving the accuracy and applicability of the model.

Spectrum map is the foundation of spectrum resource management, security governance and spectrum warfare. Aiming at the problem that the traditional spectrum mapping is limited to two-dimensional space, a three-dimensional spectrum data acquisition and mapping system architecture for the integration of space, sky and earth is presented, and a spectrum map reconstruction scheme driven by propagation model is proposed, which can achieve high-precision three-dimensional spectrum map rendering under the condition of sparse sampling. The spectrum map reconstructed by this method in the case of single radiation source and multiple radiation sources is in good agreement with the theoretical results based on ray tracing method. In addition, the measured results of typical scenes further verify the feasibility of this method.

In order to promote the application of noise map in high-speed railway noise management, the high-speed railway noise map drawing technology based on the combination of noise prediction model and geographic information system (GIS) is studied. Firstly, according to the distribution characteristics of noise sources and line structure characteristics of high-speed railway, the prediction model of multi equivalent sound sources and the calculation method of sound barrier insertion loss of high-speed railway are optimized; secondly, a three-dimensional geographic information model of a high-speed railway is built in GIS software, and the railway noise prediction technology based on the model is developed again; then, the noise of discrete nodes is calculated, and the continuous noise distribution map is drawn by spatial interpolation. The research results show that the comparison error between the noise map of a high-speed railway drawn by this technology and the measured results is less than 1 dB (A), which verifies the accuracy and practicality of the high-speed railway noise map, and can be used as a reference for the railway noise management department to formulate noise control countermeasures.

Cartography includes two major tasks: map making and map application, which is inextricably linked to artificial intelligence technology. The cartographic expert system experienced the intelligent expression of symbolism. After the spatial optimization decision of behaviorism intelligent expression, cartography faces the combination of deep learning under connectionism to improve the intelligent level of cartography. This paper discusses three problems about the proposition of “deep learning + cartography”. One is the consistency between the deep learning method and the map space problem solving strategy, based on gradient descent, local correlation, feature reduction and non-linear nature that answer the feasibility of the combination of “deep learning + cartography”; the second is to analyze the challenges faced by the combination of cartography from its unique disciplinary characteristics and technical environment, involving the non-standard organization of map data, professional requirements for sample establishment, the integration of geometric and geographical features, as well as the inherent spatial scale of the map; thirdly, the entry points and specific methods for integrating map making and map application into deep learning are discussed respectively.