An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities

Abstract

Abstract:A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer scale is an important step in the validation process for moderate-resolution leaf area index (LAI) products. Current LAI-sampling strategies are unable to consider the vegetation seasonal changes and are better suited for single-day LAI product validation, whereas the increasingly used wireless sensor network for LAI measurement (LAINet) requires an optimal sampling strategy across both spatial and temporal scales. In this study, we developed an efficient and robust LAI Sampling strategy based on Multi-temporal Prior knowledge (SMP) for long-term, fixed-position LAI observations. The SMP approach employed multi-temporal vegetation index (VI) maps and the vegetation classification map as a priori knowledge. The SMP approach minimized the multi-temporal bias of the VI frequency histogram between the ESUs and the entire site and maximized the nearest-neighbor index to ensure that ESUs were dispersed in the geographical space. The SMP approach was compared with four sampling OPEN ACCESS Remote Sens. 2015, 7 1301 strategies including random sampling, systematic sampling, sampling based on the land-cover map and a sampling strategy based on vegetation index prior knowledge using the PROSAIL model-based simulation analysis in the Heihe River basin. The results indicate that the ESUs selected using the SMP method spread more evenly in both the multi-temporal feature space and geographical space over the vegetation cycle. By considering the temporal changes in heterogeneity, the average root-mean-square error (RMSE) of the LAI reference maps can be reduced from 0.12 to 0.05, and the relative error can be reduced from 6.1% to 2.2%. The SMP technique was applied to assign the LAINet ESU locations at the Huailai Remote Sensing Experimental Station in Beijing, China, from 4 July to 28 August 2013, to validate three MODIS C5 LAI products. The results suggest that the average R 2 , RMSE, bias and relative uncertainty for the three MODIS LAI products were 0.60, 0.33, −0.11, and 12.2%, respectively. The MCD15A2 product performed best, exhibiting a RMSE of 0.20, a bias of −0.07 and a relative uncertainty of 7.4%. Future efforts are needed to obtain more long-term validation datasets using the SMP approach on different vegetation types for validating moderate-resolution LAI products in time series.

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

“…Since it is impractical to collect field reference LAI measurements for all the pixels in high-resolution imagery due to the limitations of time, budget, and geographical accessibility, spatial sampling is often needed. To assure the accuracy of the derived transfer function, the sampling plots should be representative of the dynamic ranges of the auxiliary variables [15,16]. The term "representative" means that sample from a population will be a scaled-down version of the entire population, where all different characteristics of the population are preserved [24], i.e., the frequency distribution histograms of the sample and of the population should be as similar as possible.…”

“…The numerator indicates the mean distance between every two nearest sampling plots, and the denominator is the expectation of that distance when the sampling plots are randomly scattered in the study area. The NNI is a good criteria to measure the dispersion of the sampling plots over the entire study area, a larger NNI indicates a more dispersed distribution, i.e., a better representativeness in geographic space [15,16]. To ensure the representativeness in both feature and geographic space, the representativenessobjective function (O) is defined as follows:…”

“…VTB sampling, allocating the number of sampling plots in proportion to the areas [21]. CLH sampling is the same to CSS but using Equation (5) as the overall objective function [16]. VTB, CLH, and CSS sampling strategies all exploit a priori information and are considered superior to random and systematic sampling in LAI validation [10,15,16].…”

Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China

“…Prior to validation of the LAI products, a land cover map and the historical multi-temporal VI products were used as a priori knowledge to describe the vegetation spatial distribution and growing characteristics at the validation sites during the observation periods (Figures 3 and 4). The sampling sites with minor inter-annual changes during vegetation growth stages and more reasonable characteristics according to the vegetation phenology can be used to parameters validation [46,47]. Figure 3 shows that some of the planting area for corn crops in July 2012 were used for other crops in July 2013.…”

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method

“…The actual data may differ slightly from published data, but will still be valid as a reference for this region since the Genhe region is covered by old-growth primary forest that has never suffered major human disturbance. Twenty essential sample units (ESUs), 20 m × 20 m in size, were established in the experimental area according the optimal sampling method [33]. For the measurement of the smartphone method, a smartphone with the CPU of Quad-Core Processer of 2.5 GHz, the camera of 13 million pixels and the aperture of F1.8 was configured.…”