Chương này tập trung vào việc áp dụng đánh giá độ chính xác như là một giai đoạn cuối cùng trong việc đánh giá chất lượng chuyên đề của bản đồ (LC) bao gồm đất bao gồm một khu vực rộng lớn như một tiểu bang hay tỉnh, quốc gia, hay lục địa. Bản đồ này được giả định là phân loại theo phân loại rõ nét hoặc cứng chương trình, như trái ngược với một chương trình phân loại mờ (Foody, 1999) | CHAPTER 2 Sampling Design for Accuracy Assessment of Large-Area Land-Cover Maps Challenges and Future Directions Stephen V. Stehman CONTENTS Meeting the Challenge of Cost-Effective Sampling Strata vs. Clusters The Cost vs. Precision Flexibility of the NLCD Comparison of the Three Stratification and Local Spatial Existing Added-Value Uses of Accuracy Assessment Nonprobability Policy Aspects of Probability vs. Nonprobability Statistical Computing .23 Practical Realities of Sampling Principle Principle Principle Principle Summary .26 INTRODUCTION This chapter focuses on the application of accuracy assessment as a final stage in the evaluation of the thematic quality of a land-cover LC map covering a large region such as a state or province country or continent. The map is assumed to be classified according to a crisp or hard classification scheme as opposed to a fuzzy classification scheme Foody 1999 . The standard protocol for accuracy assessment is to compare the map LC label to the reference label at sample locations 13 2004 by Taylor Francis Group LLC 14 REMOTE SENSING AND GIS ACCURACY ASSESSMENT where the reference label is assumed to be correct. The source of reference data may be aerial photography ground visit or videography. Discussion will be limited to the case in which the assessment unit for comparing the map and reference label is a pixel. Similar issues apply to sampling both pixels and polygons but a greater assortment of design options has been developed for pixel-based assessments. Most of the chapter will focus on site-specific accuracy which is accuracy determined on a pixel-by-pixel basis. In contrast nonsite-specific accuracy provides a comparison aggregated over some spatial extent. For
