Fallen (i.e. snow damage and wind thrown) and withering (i.e. disease and insects) of trees in abandoned forests are one of the major problems in forestry. However the current investigation method relies on a ground survey, which is difficult to grasp the conditions extensively. Recently, usage of high spatial resolution satellite imagery and LiDAR (Light Detection And Ranging) data are anticipated as an effective solution for the forest monitoring. High resolution satellite imagery is effective for detecting withered and fallen damage, although this data has a difficulty in distinguishing between withered and fallen damage. Digital Surface Model (DSM) and Digital Elevation Model (DEM) which are made from LiDAR data are effective for detecting fallen damage, although this data has a difficulty in detecting withered damage. In the developing method, integration of high resolution satellite imagery and LiDAR data were utilized to detect two types of damage separately at same time. Multinomial Logit Model (MLM) was utilized for integrated processing. Red, NIR channel and gap areas detected by DSM and DEM were dependent variables for MLM. This method was examined on the IKONOS Multispectral Imagery and LiDAR data in the test area. Accuracy assessments were conducted from the aspect of omission (User's accuracy) and commission (Producer's accuracy). In withered damage detection, 78% and 74% of pixels were correctly detected, respectively. In fallen damage detection, 82% and 84% of pixels were correctly detected, respectively. From these results, this method was demonstrated that integration of two data can detect fallen and withering damage in high accuracy.
Alteration zones are important features for the exploration of porphyry copper deposits. The ASTER sensor is able to identify the types of alteration and its alteration zoning, which are important information for mineral resource exploration. The combined utilization of three specific images are described, 1) alteration image, 2) band ratio image, and 3) principal component analysis (PCA) image. The alteration image is a false color image using band4, band6 and band8 (R : G : B=4 : 6 : 8). The alteration zones appear as pink or green colors in the alteration image. The band ratio image is a color image that uses the band ratio images of band4/band6, band5/band6 and band5/band8 (R : G : B=4/6 : 5/6 : 5/8). The band ratio image is able to provide information of the alteration zoning. Band4/band6 enhances the advanced argillic alteration, band5/band6 enhances the phyllic alteration and band5/band8 enhances the propylitic alteration. The PCA image (“Crosta technique”) is also able to provide information of the alteration zoning. The PCA image is a color image that consists of the alteration PCA images that are obtained from three PCA processes (R : G : B=advanced argillic : phyllic : propylitic). The alteration PCA image can be chosen by checking the eigen vectors. The advanced argillic alteration image is obtained from PCA of band1, band4, band6 and band7, the phyllic alteration image is obtained from PCA of band1, band3, band5 and band6 and the propylitic alteration image is obtained from PCA of band1, band3, band5 and band8. These methods are applied to the Meiduk area of Iran, which is known for porphyry copper mineralizations. Each of the methods are able to detect the hydrothermal alteration or zonings related to porphyry copper mineralization clearly.
In order to grasp the balance between supply and demands of vegetables such as cabbage, lettuce and Chinese cabbage which are produced on a large scale, forecast of their harvest time and area is a problem to be awaiting solution in Japan. This study proposed an approach of forecasting the harvest time and area of cabbage by using SPOT images in Tsumagoi country and discussed practical application to provide forecasting information. Firstly, cabbages field-based were extracted by the proposed post-classification method using the field polygon data. The field polygon data is a section of cultivated field which was interpreted from the digital aerial photography. Next, the pixel-based cabbages harvest time was estimated for extracted cabbage fields from the profiles of normalized difference vegetation index (NDVI) corresponding to cabbage growing and relationship between NDVI and harvest date. Results of extracted cabbage field and estimated harvesting period respectively derived from SPOT5 images of May 5 and July 28, 2005, were verified by ground investigation. According to the profile of NDVI, cabbage pixels with multiple growing stages in the image can be divided into two groups of leaves number increase term and leaves weight increase term. Results for the cabbages during the early stage in term of leaves number increase, presented a low accuracy because their spectral reflectance in SPOT is similar to bareland. Results for the cabbages in term of leaves weight increase showed strong coincidence with ground investigation. Furthermore, it was found that results of forecasted harvest area respectively from multi-years SPOT image can give a trend analysis of cabbage possible supplying field comparing with the historical information of the past harvested field and shipped amount to market. As a result, this study suggested an operational application of satellite remote sensing in forecast of vegetable harvest time and area, aimed to provide the services for the coordination between supply and market demand of vegetables. Moreover the past information contained in ancillary data such as remote sensing data, shipped field data and so on should be incorporated into the forecasting process.
A method for estimation of forest parameters, species, tree shape, distance between canopies by means of Monte-Carlo based radiative transfer model with forestry surface model is proposed. The model is verified through experiments with the miniature model of forest, tree array of relatively small size of trees. Two types of miniature trees, ellipse-looking and cone-looking canopy are examined in the experiments. It is found that the proposed model and experimental results show a coincidence so that the proposed method is validated. It is also found that estimation of tree shape, trunk tree distance as well as distinction between deciduous or coniferous trees can be done with the proposed model. Furthermore, influences due to multiple reflections between trees and interaction between trees and under-laying grass are clarified with the proposed method.
Due to the scarcity of reliable validation data and difficulties associated with the collocation of validation data and satellite measurements, an approach for verifying Tropical Rainfall Measuring Mission (TRMM) rainfall products has been proposed. Consistency between TRMM Microwave Imager (TMI)-observed brightness temperatures (TBs) at 10.7 and 19.4 GHz channels and those simulated from the Precipitation Radar (PR) rainfall estimates using a radiative transfer model were statistically examined. Accurate and computationally efficient simulations of TBs are critical for this approach. In this study, convolution, a slant-path approximation, a melting-layer parameterization, and a drop size distribution (DSD) model employed in simulations of brightness temperatures are presented and evaluated. Impact of the melting-layer parameterization and the DSD model on simulated brightness temperatures and thus on validation results are also examined.