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Remote Sens. 2016, 8(9), 704; doi:10.3390/rs8090704

A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

1
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
2
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
3
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Jose Moreno, Clement Atzberger and Prasad S. Thenkabail
Received: 1 June 2016 / Revised: 19 August 2016 / Accepted: 23 August 2016 / Published: 26 August 2016
View Full-Text   |   Download PDF [4039 KB, uploaded 26 August 2016]   |  

Abstract

Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountainous areas present extreme topographic variability, and are characterized by more spatial heterogeneity and inaccessibility compared with flat terrain. It is difficult to collect representative ground-truth measurements, and the validation of LAI in mountainous areas is still problematic. A cost-constrained sampling strategy (CSS) in support of LAI validation was presented in this study. To account for the influence of rugged terrain on implementation cost, a cost-objective function was incorporated to traditional conditioned Latin hypercube (CLH) sampling strategy. A case study in Hailuogou, Sichuan province, China was used to assess the efficiency of CSS. Normalized difference vegetation index (NDVI), land cover type, and slope were selected as auxiliary variables to present the variability of LAI in the study area. Results show that CSS can satisfactorily capture the variability across the site extent, while minimizing field efforts. One appealing feature of CSS is that the compromise between representativeness and implementation cost can be regulated according to actual surface heterogeneity and budget constraints, and this makes CSS flexible. Although the proposed method was only validated for the auxiliary variables rather than the LAI measurements, it serves as a starting point for establishing the locations of field plots and facilitates the preparation of field campaigns in mountainous areas. View Full-Text
Keywords: cost-constrained sampling strategy (CSS); leaf area index (LAI); mountainous areas; validation; representativeness cost-constrained sampling strategy (CSS); leaf area index (LAI); mountainous areas; validation; representativeness
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yin, G.; Li, A.; Zeng, Y.; Xu, B.; Zhao, W.; Nan, X.; Jin, H.; Bian, J. A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas. Remote Sens. 2016, 8, 704.

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