In the design of multimedia data mining systems, one of the most important issues is how to search and analyze media data, according to contexts. We have introduced a semantic associative search method based on our 'Mathematical Model of Meaning (MMM) [1, 2, 3]'. This model is applied to compute semantic correlations between keywords, images, music and documents dynamically in a context-dependent way. We have constructed 'A Multimedia Data Mining System for International and Collaborative Research in Global Environmental Analysis,' as a new platform of a multimedia data mining environment between our research team and international organizations. This environment is constructed by creating the following subsystems: (1) Multimedia Data Mining System with semantic associative-search functions and (2) 5D Space Sharing and Collaboration System for cooperative creation and manipulation of multimedia objects. It is very important to memorize those situations and compute environment change in various aspects and contexts, in order to discover what are happening in the nature of our planet. We have various (almost infinite) aspects and contexts in environmental changes in our planet, and it is essential to realize a new analyzer for computing differences in those situations for discovering actual aspects and contexts existing in the nature. We propose a new method for Differential Computing in our Multi-dimensional World map [4, 5, 6]. We utilize a multi-dimensional computing model, the Mathematical Model of Meaning (MMM), and a multi-dimensional space filtering method with, adaptive axis adjustment mechanism to implement differential computing. Computing environmental changes in multi-aspects and contexts using differential computing, important factors that change natural environment are highlighted. We also present a method to visualize the highlighted factors using our Multi-dimensional World Map. Semantic computing is an important and promising approach to semantic analysis for various environmental phenomena and changes in real world. This paper presents a new semantic computing method with multi-spectral images for analyzing and interpreting environmental phenomena and changes occurring in the physical world. We have presented a concept of 'Semantic Computing System' for realizing global environmental analysis. This paper presents a new semantic computing method to realize semantic associative search for the multiple-colours-spectral images in the multi-dimensional semantic space, that is 'multi-spectral semantic-image space' consisting of (a) Infra-Red filtered axis, (b) Red axis, (c) Green filtered axis, (d) Blue filtered axis, (e) NDVI axis, and (f) NDWI axis, with semantic projection functions. This space is created for dynamically computing semantic equivalence, similarity and difference between multi-spectral images and environmental situations. The most essential and significant point of our 'multispectral-semantic computing method' is that it realizes 'the interpretation of substances (materials)' appearing and reflected in the multi-spectrum images by using '6-dimensional multi-spectral semantic-image space' and 'semantic projection functions'. That is, this method interprets the substances appearing in the image into 'the names of substances' by using 'knowledge of substances' expressed in this semantic-image space. This is corresponding to the human-level interpretation when we look at an image and recognize the substances appearing in the image. This method realizes this human-level interpretation with 'multi-spectral semantic-image space' and 'semantic projection functions'. We apply this system to global environmental analysis as a new platform of environmental computing. We have already presented the 5D World Map System, as an international research environment with spatio-temporal and semantic analysers. We also present several new approaches to global environmental-analysis for multi-spectrum images in 'multi-spectral semantic-image space.'