Recently, GIS technology is widely applied in many fields. DEM (Digital elevation model) is one of quite important data for GIS. Many methods were introduced for elevation interpolation from elevation contour data so far. However, every method of them has difficulty to get satisfactory result when the elevation contour forms complicated pattern. The fundamental fault of them is the limitation to take straight line only as reference line for elevation interpolation. Therefore, it cannot be realised suitable interpolation at the blind part of complicated contour line which occurs frequently in actual elevation contour data.
Newly developed elevation interpolation method takes elevation contour plane as elevation potential field. The idea is rather similar to treat electric field which is formed by giving electric potential correspond to elevation value against contour line. This method is realized taking 2-dimensional flexibility of reference line for elevation interpolation. The application of this method enables to get better quality DEM even elevation contour is so complicated. Author call this method as Quasi Potential Method.

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NOAA/EPAが作成したGlobal Ecosystem Database version 1.0 CD-ROM (GEDB) に含まれるグローバルデータセットを使用して, モンスーンアジアにおける土地被覆の変化に伴う広域可能蒸発量の変化を推定した。
モンスーンアジアの現植生図を作成するために, NOAA/AVHRRによる1990年の月NDVI (Normalized Difference Vegetation Index) の季節変化に基づき, クラスター分析を行った。48クラスに分類された各クラスごとに気温データから求めることのできる温量指数降水量の特性を求め, 地理的位置を考慮しながら各クラスの植生・土地被覆タイプを決定した。
蒸発量の計算にはPriestley & Taylor法に基づいたAhn and Tateishi (1994) の方法を用いた。計算に必要なパラメーターはGEDBに含まれる気温・日照率・アルベドデータセットを現植生図を重ね合わせ, 各クラスの特長値を求めることによって得た。これらのパラメーターを用いて現植生図に対応した広域可能蒸発量を計算した。
自然植生図の復元のために, Holdridge Life Zoneデータセットを参照し, 現在草地あるいは農地となっている場所で, 気候的には森林が成立可能な地域を抽出した。その結果, 計算対象範囲の陸域の約24%の土地被覆が変化していることが推定された。土地被覆の変化面積が大きい地域は中国東部～南部とインドで, タイおよびミャンマーの中央平原がこれに次ぐ。復元された自然植生図に対して現植生図と同じ方法で, 可能蒸発量を計算した。

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A neural network was applied to land cover classification for the purpose of utilizing spatial information. Co-occurrence matrices, which are extracted from single band image data, were used for the input feature matrix to the neural network. The adopted model of the neural network has multi layered architecture with the back propagation algorithm as the training method. Firstly, various models and parameters were evaluated by classification experiments which were threshold models of each neuron, learning parameters at back propagation, the number of hidden layers, the number of neurons in the hidden layers and the size of co-occurrence matrix. Through the experiments, the best performance in landcover classification was achieved with sigmoid function for the threshold model, large learning rate and small momentum as the learning parameters, single hidden layer, two times of output neurons as the number of the hidden neurons and 16×16 to 32×32 as the size of co occurrence matrix. Secondly, landcover classification using the proposed method and three kinds of conventional methods were conducted with Landsat TM and SPOT HRV images. The conventional methods were a pixel wise maximum likelihood classifier, a pixel wise neural network classifier and a 3× 3 pixel wise neural network classifier. As a result, the proposed neural network classifier with the aid of co-occurrence matrix showed 5% to 17% higher classification accuracies than those of the conventional classifiers.

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A number of distinct signatures suspected of relating to Maya relics are observed over northern Yucatan Peninsula, serendipitously, during the geological study on JERS-1/SAR imagery. Among other things, ancient roadways (“sacbe”in indigenous language) are prominent being expressed as linear features, mostly with dark tones, throughout the whole region. Preliminary interpretation shows that these archaeological lineaments might also be indications of putative causeways, moats, walls and the like constructed by Maya people in the past. Another interesting patterns to be noted are numerous bright spots which suggest remains of human dwellings consisted of aggregation of huge mounds, courtyards, buildings including pyramids, from place to place. No verification with on-site ground check is practised yet ; however, the result of correlation with existing data seems to be encouraging to demonstrate the unique potentiality of JERS-1 imagery in radar archaeology, particularly, in terms of penetration through vegetative cover.

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The record of India in the field of remote sensing over the last two decades is extremely impressive. Now India builds its own sensor systems and has its own satellites launched into space by its own launcher. It operates its own receiving station for satellite data and distributes the data to users throughout the country. The operational Indian Remote Sensing (IRS) satellites lA and lB launched in March 1988 and August 1991, respectively are providing good quality data with a combined repetitivity of lldays. IRS-P2 carried on the developmental flight of indigenously built Polar Satellite Launch Vehicle (PSLV-D2) launched in October 1994 is already operational. The second generation satellites of the same series namely IRS-1C and 1D, with better capabilities, are scheduled for launch during 1995-98 time frame. Besides, the data from a number of foreign satellites like Landsat, NOAA, SPOT and ERS-1 are directly received and readily available in the country. It now has good and wide spread infrastructural facilities in remote sensing. Remote Sensing Application Centers have been operationalized in almost all the states to aid and assist users in mapping monitoring, monitoring and management of the natural resources of the area. Most of the remote sensing institutes and centers have acquired sophisticated image processing and data analysis systems. A number of scientists are involved in the use of remote sensing data for application in a wide range of resource themes like agriculture, soils, land use, forests, ecology, environment, water resources, geology, oceanography and disaster management, etc. The use of remote sensing technology has been operationalized in many areas like wasteland mapping, land use/land cover mapping, forest mapping, sea surface temperature mapping, etc. Many national level projects have been initiated to harness the benefits of the technology. Research and development work is also underway at various centers and universities. Regular training programs are being offered to keep pace with the rapidly increasing demand of the trained manpower in the field of remote sensing. The remote sensing technology in poised to become an integral part of the planning pmocess of the country in a not too distant future.

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