A hybrid visual estimation method for the collection of ground truth fractional coverage data in a humid tropical environment

Delamater, Paul L.; Messina, Joseph P.; Qi, Jiaguo; Cochrane, Mark A.

doi:10.1016/j.jag.2011.10.005

Cited by 18 publications

(14 citation statements)

References 58 publications

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“…The "true" value of FVC for quadrats is important for evaluating SMA models. It has been assessed by several methods including manual visual interpretation [37] and automatic and semi-automatic classification [41] of digital photographs. Alternatively, the FVC of quadrats can be measured using the formula: …”

Section: Field Datamentioning

confidence: 99%

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Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Wei

Zeng

et al. 2014

Remote Sensing

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Abstract:Linear spectral mixture analysis (SMA) is commonly used to infer fractional vegetation cover (FVC), especially for pixel dichotomy models. However, several sources of uncertainty including normalized difference vegetation index (NDVI) saturation and selection of endmembers inhibit the effectiveness of SMA for the estimation of FVC. In this study, Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat 8/Operational Land Imager (OLI) remote sensing data for the early growing season and in situ measurement of spectral reflectance are used to determine the value of endmembers including VI soil and VI veg , with equally weighted RVI and NDVI measures used in combination to minimize the inherent biases in pure NDVI-based FVC. Their ability to improve estimates of grassland FVC is analyzed at different resolutions. These are shown to improve FVC estimates over NDVI-based SMA models using fixed values for the endmembers. Grassland FVC changes for Inner Mongolia, China from 2000 to 2013 are then monitored using the MODIS data. The results show that changes in most grassland areas are not significant, but in parts of Hulunbeier, south Tongliao, middle Xilin Gol and Erdos, grassland FVC has increased significantly.

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Section: Field Datamentioning

confidence: 99%

“…For example, NDVI based SMA has been used to estimate FVC in a large number of landscapes with various remote sensing data sources [26,[33][34][35]. However, NDVI has been criticized because FVC tends to be overestimated as it approaches certain proportions [36], especially in moderately vegetated areas [1,37].…”

Section: Introductionmentioning

confidence: 99%

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Wei

Zeng

et al. 2014

Remote Sensing

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“…However, some research has shown that the EM is susceptible to vegetation type and the quality and quantity of measured data, that is, this strategy provides ideal results in homogeneous areas at regional scales (e.g., simple steppe regions or forest regions) [12,24,25]. Furthermore, it is still not clear how to extend the scope of an EM in relatively complex regions with multiple vegetation types [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Fractional Vegetation Cover Estimation of Different Vegetation Types in the Qaidam Basin

et al. 2019

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The estimation of fractional vegetation cover (FVC) by using remote sensing images has become feasible. Based on Landsat8-OLI images and field data obtained from an unmanned aerial vehicle, we established an empirical model (EM) and a pixel decomposition model (PDM) of FVC in the desert vegetation region, steppe vegetation region, meadow vegetation region and mixed vegetation region (the three vegetation region types) of the Qaidam Basin, and the inversion accuracies of the models were compared. The results show the following: (1) Vegetation classification inversion (VCI) provides a promising approach for FVC estimation. The accuracy of FVC by VCI was obviously better than that achieved using vegetation mixed inversion (VMI); (2) Differences were observed in the FVC estimation between VCI and VMI by the EM in areas with relatively high-density vegetation cover (FVC > 60%). The FVC in some parts of steppe region in the basin was slightly overestimated by VMI of the EM; 3) VCI estimated by the PDM resulted in lower inversion values for extremely low-density vegetation cover (FVC ≤ 10%) and higher inversion values for high-density vegetation cover (FVC > 80%). The FVC inversion was underestimated by the PDM in steppe and meadow regions with FVC > 15% in the basin. The application of VCI in different models can provide new ideas for the sustainable study of vegetation in arid regions.

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“…These conditions are defined through variables such as the amount of vegetation, surface temperature, surface moisture, air temperature (AT), relative humidity (RH), solar radiation, evapotranspiration, wind speed and direction, rainfall, etc. Indicators such as percent vegetation cover (PVC) and leaf area index (LAI) [8,9], duration of leaf wetness [2,10], thermal units [11], degree days, vapor pressure deficit [7,11,12], potential evapotranspiration [13], water stress indices [12,14,15], drought indices [16], precipitation indices [17], etc., are related to these variables, and they are used to quantify and monitor agrometeorological and microclimate conditions on a given territory. They are also used to identify appropriate times in the management of various agricultural practices like sowing, irrigation, disease and pest screening, applying manure and pesticides, and harvesting.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to point data acquired in fields, they are less costly in time and money [34]. Vegetation indices (VIs) derived from satellite images are used to estimate indicators of the amount of vegetation like percent vegetation cover (PVC) [8,9,47,48] and leaf area index (LAI) [28,[49][50][51]. The normalized difference vegetation index (NDVI) is the best known and most widely used VI [11,28,34,45,46,51,52].…”

Section: Introductionmentioning

confidence: 99%

Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

Kotchi¹,

Barrette²,

Viau³

et al. 2016

Geospatial Technology - Environmental and Social Applications

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A precise estimation and the characterization of the spatial variability of microclimate conditions (MCCs) are essential for risk assessment and site-specific management of vector-borne diseases and crop pests. The objective of this study was to estimate at local scale, and assess the uncertainties of Surface Microclimate Indicators (SMIs) derived from airborne infrared thermography and multispectral imaging. SMIs including Surface Temperature (ST) were estimated in southern Quebec, Canada. The formulation of their uncertainties was based on in-situ observations and the law of propagation of uncertainty. SMIs showed strong local variability and intra-plot variability of MCCs in the study area. The ST values ranged from 290 K to 331 K. They varied more than 17 K on vegetable crop fields. The correlation between ST and in-situ observations was very high (r = 0.99, p = 0.010). The uncertainty and the bias of ST compared to in-situ observations were 0.73 K and ±1.42 K respectively. This study demonstrated that very high spatial resolution multispectral imaging and infrared thermography present a good potential for the characterization of the MCCs that govern the abundance and the behavior of disease vectors and crop pests in a given area.

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A hybrid visual estimation method for the collection of ground truth fractional coverage data in a humid tropical environment

Cited by 18 publications

References 58 publications

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Fractional Vegetation Cover Estimation of Different Vegetation Types in the Qaidam Basin

Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

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