Verification of operational solar flare forecast: Case of Regional Warning Center Japan

In this article, we discuss a verification study of an operational solar flare forecast in the Regional Warning Center (RWC) Japan. The RWC Japan has been issuing four-categorical deterministic solar flare forecasts for a long time. In this forecast verification study, we used solar flare forecast data accumulated over 16 years (from 2000 to 2015). We compiled the forecast data together with solar flare data obtained with the Geostationary Operational Environmental Satellites (GOES). Using the compiled data sets, we estimated some conventional scalar verification measures with 95% confidence intervals. We also estimated a multi-categorical scalar verification measure. These scalar verification measures were compared with those obtained by the persistence method and recurrence method. As solar activity varied during the 16 years, we also applied verification analyses to four subsets of forecast-observation pair data with different solar activity levels. We cannot conclude definitely that there are significant performance difference between the forecasts of RWC Japan and the persistence method, although a slightly significant difference is found for some event definitions. We propose to use a scalar verification measure to assess the judgment skill of the operational solar flare forecast. Finally, we propose a verification strategy for deterministic operational solar flare forecasting.Comment: 29 pages, 7 figures and 6 tables. Accepted for publication in Journal of Space Weather and Space Climate (SWSC

5.IEEE-IGARSS in Seoul, Korea

金沢大学大学院自然科学研究科The purpose of this study is detection and classification of tree crowns using forest imagery taken by IKONOS. A forest image contains many tree crowns of different sizes and shape that are touching each other. By using IKONOS pan-sharpened data, discernment of tree crown and species is possible. To detect tree crowns in the image, we used Watershed segmentation. If an image is viewed as a surface, with mountains and valleys, the Watershed segmentation finds intensity valleys in an image. In this study, a gradient of intensity in an image was used in order to find valleys separating tree crowns from shadows. To classify tree species, the spatial features of each segmented region were calculated. Image features for the classification were extracted by texture analysis using gray level co-occurrence matrix. Image texture is produced by an aggregation of unit features, such as tree leaves and leaf shadows. Variations in crown texture are important in the identification of species. Supervised classification using maximum likelihood decision rules with these features was performed. Classification accuracies on the order 80% were achieved. © 2005 IEEE.Project Number 14404021, Peport of Research Project ; Grant-in-Aid for Scientific Research(B)(2), from April 2002 to March 2006, Edited by Muramoto,Ken-ichiroKamata, NaotoKawanishi, TakuyaKubo, MamoruLiu, JiyuanLee, Kyu-Sung , 人工衛星データ活用のための東アジアの植生調査、課題番号14404021, 平成14年度～平成17年度科学研究費補助金, 基盤研究（B)(2)研究成果報告書, 研究代表者：村本, 健一郎, 金沢大学自然科学研究科教授Environmental Monitoring In East Asia ; Remote Sensing and Forest

Detection of individual tree crowns in high spatial resolution remote sensing imagery

金沢大学大学院自然科学研究科2005 International Symposium on Environmental Mornitoring in East Asia -Remote Sensing and Forests-,Hosted The EMEA Project, Kanazawa University 21st=Century COE Program -Environmental Monitoring and Predicition of Long- and Short- Term Dynamics of Pan-Japan Sea Area- ,予稿集, EMEA 2005 in Kanazawa, 国際学術研究公開シンポジウム『東アジアの環境モニタリング』－リモートセンシングと森林－,年月日：200511月28日～29日, 場所：KKRホテル金沢, 金沢大学自然科学研究科, 主催：金沢大学EMEAプロジェクト, 共催：金沢大学21世紀COEプログラム「環日本海域の環境変動と長期・短期変動予測

高分解能衛星画像を用いた森林樹冠地図の作成

森林を撮影した高分解能衛星画像を解析して,単木樹冠地図を自動的に作成する手法を開発した.長野県北佐久郡軽井沢町の上信越高原国立公園内で研究分担者が管理している試験林を研究対象とした.現地調査を行い,80m×40mの範囲で林冠の上層部を構成している樹高が約16mから18mの樹木の計約100本について,樹冠の外形および位置を計測した.樹冠の外形は,幹から8方位の樹冠外縁を地上に投影した点と幹との水平距離を計測して8角形とした.各樹木位置の基準となる地点の位置情報をGPS装置で取得し,そこから各樹木の幹の位置も計測した.これらのデータから個々の樹冠の位置と形状を地上に投影して表す林冠投影図を作成した.また,森林の高分解能衛星データから個々の樹冠を抽出する画像処理手法を開発した.対象地域の高分解能衛星データを購入し,自動的に個々の樹冠領域を抽出した.最後に,画像処理結果と林冠投影図を重ね合わせた.高精度の重ね合わせには一方を固定し他方をアフィン行列によって幾何変換する必要がある.この処理には一般的に,複数の地上基準点を画像処理結果と林冠投影図の両方から選択する必要があるが,森林には特徴的な基準点がなく,衛星画像から有効な地上基準点を選択することができない.そこで重ね合わせの精度を示す評価式を提案し,地上基準点を使用しないで高精度の重ね合わせを実現した.重ね合わせた衛星画像から個々の8角形樹冠の領域を抽出した.この樹冠画像と位置を3次元コンピュータグラフィックス技法で処理して,単木樹冠地図を立体的に可視化した.さらに,デジタルカメラを使った森林の3次元計測手法を提案した.In forest area, there are few landmarks to be ground control points (GCPs) used for registration of satellite images or maps. Additionally, geographic information from the Global Positioning System (GPS) in field measurement survey is insufficient accuracy to identify individual tree crowns from satellite image. In this study, we propose the method of identifying individual Mae crowns from satellite image using field measured data. First, in order to obtain the field measured data, we collected several information of individual trees in the test site. These are the tree stand locations, the distances between the tree trunk and outermost branch in eight directions, the diameter at breast height, and tree species. Then, using the field measured data, we created the projected on-ground crown map which has the location and shape of individual trees. The each shape of tree crown is octagonal. Next, we detected the regions of tree crown from IKONOS panchromatic image using Watershed image segmentation method. The segmented regions were classified to discriminate tree crown using the feature of spectral signature. Finally, we found out individual tree crowns related with field measured data from satellite image. Using a GCP by GPS equipment, we performed roughly registration of the satellite image to the projaied on-ground crown map. For each tree crown in the map, we found out the same tree, which has the highest corresponding possibility to the bee crown in the map, among segmented regions obtained from satellite image. This tree-to-tree matching algorithm was performed using the fitness value of the location and octagonal shape of both tree crowns in image and map. We could obtain the optimum registration by affine transformation of highest fitness value without ground control points. Consequently, we could identify individual tree crowns from satellite image by image-to-map rectification.研究課題/領域番号:18580259, 研究期間(年度):2006-2007出典：「高分解能衛星画像を用いた森林樹冠地図の作成索」研究成果報告書　課題番号18580259 (KAKEN：科学研究費助成事業データベース（国立情報学研究所）)　　　本文データは著者版報告書より作

6.EMEA International Symposium in Kanazawa, Japan

金沢大学大学院自然科学研究科Project Number 14404021, Peport of Research Project ; Grant-in-Aid for Scientific Research(B)(2), from April 2002 to March 2006, Edited by Muramoto,Ken-ichiroKamata, NaotoKawanishi, TakuyaKubo, MamoruLiu, JiyuanLee, Kyu-Sung , 人工衛星データ活用のための東アジアの植生調査、課題番号14404021, 平成14年度～平成17年度科学研究費補助金, 基盤研究（B)(2)研究成果報告書, 研究代表者：村本, 健一郎, 金沢大学自然科学研究科教

Classification of clouds in the Japan Sea area using NOAA AVHRR satellite images and self-organizing map

金沢大学大学院自然科学研究科知能情報・数理This paper introduces a method for cloud classification using NOAA AVHRR satellite images. AVHRR (Advanced Very High Resolution Radiometer) data consists of five-channel multi-spectral images. To reduce the dimensionality of the data, principal component analysis (PCA) is calculated for each channel separately. The most significant principal component values are then composed into an image feature vector. Finally, the feature vectors are clustered using self-organizing map (SOM). This method is applied for the study of winter season clouds in the Japan Sea area. © 2007 IEEE

Precipitation monitoring using multi-instrument ovservation system at various spatiotemporal scales

金沢大学大学院自然科学研究科2005 International Symposium on Environmental Mornitoring in East Asia -Remote Sensing and Forests-,Hosted The EMEA Project, Kanazawa University 21st=Century COE Program -Environmental Monitoring and Predicition of Long- and Short- Term Dynamics of Pan-Japan Sea Area- ,予稿集, EMEA 2005 in Kanazawa, 国際学術研究公開シンポジウム『東アジアの環境モニタリング』－リモートセンシングと森林－,年月日：200511月28日～29日, 場所：KKRホテル金沢, 金沢大学自然科学研究科, 主催：金沢大学EMEAプロジェクト, 共催：金沢大学21世紀COEプログラム「環日本海域の環境変動と長期・短期変動予測

6.EMEA International Symposium in Kanazawa, Japan

金沢大学大学院自然科学研究科Project Number 14404021, Peport of Research Project ; Grant-in-Aid for Scientific Research(B)(2), from April 2002 to March 2006, Edited by Muramoto,Ken-ichiroKamata, NaotoKawanishi, TakuyaKubo, MamoruLiu, JiyuanLee, Kyu-Sung , 人工衛星データ活用のための東アジアの植生調査、課題番号14404021, 平成14年度～平成17年度科学研究費補助金, 基盤研究（B)(2)研究成果報告書, 研究代表者：村本, 健一郎, 金沢大学自然科学研究科教

Geostatistical modeling for forest management using IKONOS imagery

金沢大学大学院自然科学研究科2005 International Symposium on Environmental Mornitoring in East Asia -Remote Sensing and Forests-,Hosted The EMEA Project, Kanazawa University 21st=Century COE Program -Environmental Monitoring and Predicition of Long- and Short- Term Dynamics of Pan-Japan Sea Area- ,予稿集, EMEA 2005 in Kanazawa, 国際学術研究公開シンポジウム『東アジアの環境モニタリング』－リモートセンシングと森林－,年月日：200511月28日～29日, 場所：KKRホテル金沢, 金沢大学自然科学研究科, 主催：金沢大学EMEAプロジェクト, 共催：金沢大学21世紀COEプログラム「環日本海域の環境変動と長期・短期変動予測

6.EMEA International Symposium in Kanazawa, Japan

金沢大学大学院自然科学研究科Project Number 14404021, Peport of Research Project ; Grant-in-Aid for Scientific Research(B)(2), from April 2002 to March 2006, Edited by Muramoto,Ken-ichiroKamata, NaotoKawanishi, TakuyaKubo, MamoruLiu, JiyuanLee, Kyu-Sung , 人工衛星データ活用のための東アジアの植生調査、課題番号14404021, 平成14年度～平成17年度科学研究費補助金, 基盤研究（B)(2)研究成果報告書, 研究代表者：村本, 健一郎, 金沢大学自然科学研究科教