尺度变换的正确率分析

采用优势规则和随机规则为基础的尺度分析方法．对分类的TM数据(景观类型图，包含8类型)进行了尺度变换分析。随着尺度的增加。优势规则分析方法使景观中优势景观类型的面积增加，相反．面积较小的非优势景观类型的面积减少。随机规则使各景观类型的面积基本上保持不变。随着尺度的增加．随机Kappa指数、位置Kappa指数和标准Kappa指数减少。在优势规则分析法中数量Kappa指数减少，但在随机规则为基础的处理中它保持100％。优势规则处理中的正确率大于随机规则处理的。由景观类型的面积百分比引起的数量正确率在优势规则处理中增加．但在随机规则处理中保持9．64％不变；相反数量错误在优势规则处理中明显增加。但在随机规则处理中少量增加。偶然正确率保持12．50％不变。位置正确率减少，相反位置错误明显增加。层和亚层水平上的位置正确率和错误的变化不明显．而网格水平上的位置正确率和错误大幅度减少。网格水平上的位置正确率和错误率决定了整个位置正确率和错误率．同时位置正确率和错误率基本上决定了整个正确率和错误率。标准Kappa指数大于等于70％作为选择依据．认为210m是优势规则处理法的尺度阈值，150m是随机规则处理法的尺度阈值。欲提高尺度阈值，必须改变研究范围或分类系统。

Analyzing the agreement of maps through spatial aggregations

Spatial aggregation of raster data based on majority and random rule were used in this study. To access the agreement of aggregation/scaling-up effects on landscape patterns, a classified TM imagery (8 cover types) covering 1.37 million ha with 30m resolution was aggregated incrementally from 30m to 990m. For proportions of most common cover types in majority rule-based aggregation increased slowly, whereas proportions of less common cover types decreased rapidly with increasing resolutions. For random rule-based aggregation, proportion of each cover type remained constant value. Kappa index for no ability, for location, for quantity and standard Kappa index decreased with increasing scales in majority and random rule-based aggregations. For Majority rule-based aggregation, Kappa index for quantity decreased gradually, but for random rule-based aggregation, it maintained 100%. Agreements of maps obtained from majority rule-based aggregation are higher than those from random rule-based aggregation. Agreements due to quantity increased in majority rule-based aggregation, but it maintained a fixed value 9.64% in random rule-based aggregation with increasing resolutions. Agreement due to chance maintained 12.50% in all aggregations. Agreement due to location obviously decreased, whereas error due to location substantially increased. There were no apparent changes in agreement and error due to location at stratum and substratum levels in all aggregations. To the contrary, agreement and error due to location at grid cell levels substantially increased in all aggregations. Agreement and error due to location at grid cell levels determined the agreement and error due to location, furthermore, agreement and error due to location determined the agreement and error of the whole map. If standard kappa was higher than 70% was considered satisfactory, the critical value in spatial scale was 210m for majority rule-based aggregation, and it was 150m for random rule-based aggregation. If a higher critical value was needed in study, the extent or classification system should be altered according to objective of study.