Dynamic monitoring algorithm of natural resources in scenic spots based on MODIS Remote Sensing technology

Maolin Li

doi:10.15446/esrj.v25n1.93869

Abstract

A dynamic monitoring algorithm of natural resources in scenic spots based on MODIS remote sensing technology is proposed to improve natural resources monitoring accuracy in scenic spots. The remote sensing images of scenic spots obtained by MODIS were preprocessed by TM image processing, atmospheric correction, and other technologies to get high-precision remote sensing images. The remote sensing images of scenic spots were segmented by the multi-scale segmentation method, and then the hierarchical supervision classification method was used. The change points of natural resources were extracted. The resource changes and independent variables of scenic spots were analyzed based on the least square method to realize the dynamic monitoring of natural resources in scenic locations. The experimental results show that the technique can accurately monitor the dynamic changes of forest resources and water resources in scenic spots, and the monitoring results have high accuracy.

Highlights

Remote sensing technology (RS) is a high-tech applied to agriculture in the late 1970s
When the terrain is increased by 100 m, the temperature drops by 0.55°C; The valley area is higher than the mountain area of the same height, the East-West Valley is higher than the North-South Valley, the south slope is higher than the north slope, and the closed valley basin is higher than the same high slope
In order to develop the economy in the Libo area, the forest burning and logging in large areas greatly reduced the forest area and destroyed the ecological balance of the original forest

Summary

Introduction

Remote sensing technology (RS) is a high-tech applied to agriculture in the late 1970s. The popularization of RS technology has driven the development of some related technologies, and its application has been continuously improved It is widely used in the dynamic monitoring of land use. Typical research examples include NASA, engineers, and soldiers They use the method of combining remote sensing and natural resource models to study the integrated natural resource simulation and management in the United States (McRoberts et al, 2018). All three scholars are experts who have made great achievements in modeling bidirectional reflection and multi-angle remote sensing. They have a profound understanding and incisive opinions on the essence and difficulties of remote sensing inversion. Hyperspectral remote sensing is in the experimental research stage gradually turns to the practical application stage (Cai et al, 2017)

Methods

Results

Conclusion