Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors

Guerschman, Juan Pablo; Hill, Michael J.; Renzullo, Luigi J.; Barrett, D. J.; Marks, Alan; Botha, Elizabeth C.

doi:10.1016/j.rse.2009.01.006

Cited by 335 publications

(235 citation statements)

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“…The MODIS-derived Fractional Cover metrics product [31] was obtained from the AusCover data archive [36] (note this is not the MOD44A product of the same name). The 8-day fractional cover (FC) product has a 500 × 500 m 2 resolution and covers the entire Australian continent and has been validated with in-situ measurements throughout the BDT [37].…”

Section: Fractional Covermentioning

confidence: 99%

“…In some cases separate elements of NPV cover (e.g., standing live material, standing senescent material, or litter) are distinguished, but not in the Guerschman et al [31] data product. This may explain the weak relationship between NPV cover and scaled NPP (Table 4) and PV cover (Table 7).…”

Section: Interpretation Of Non-photosynthetic Vegetationmentioning

confidence: 99%

“…The objective of this study was to evaluate the fractional cover of NPV, along with PV and BS, for degraded and non-degraded conditions using the dataset developed by Guerschman et al [31]. The region used was the extensive rangelands (>10,000 km 2 ) of the Burdekin Dry tropics (BDT) in Queensland, Australia, where Jackson and Prince [23] used the Local NPP Scaling (LNS) to measure and monitor long-term degradation.…”

Section: Introductionmentioning

confidence: 99%

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Degradation of Non-Photosynthetic Vegetation in a Semi-Arid Rangeland

Jackson

Prince

2016

Remote Sensing

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Land degradation in drylands is the process in which undesirable conditions emerge due to human and natural causes. Despite the particularly deleterious effects of degradation, and it's potentially irreversible nature, regional assessments have provided conflicting extents, rates, and severities of degradation, both globally and regionally. Current monitoring of degradation relies upon the detection of green, photosynthetically active parts of vegetation (e.g., leaves). Less is known, however, about the effect of degradation on the non-photosynthetic components of vegetation (e.g., wood, stems, leaf litter) and the relationship between photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil under degraded conditions (BS). The major objective of the study was to evaluate regional patterns of fractional cover (i.e., PV, NPV, BS) under degraded and non-degraded NPP conditions in a managed rangeland in north Queensland, Australia. Homogenous environmental conditions were identified and each of NPP, PV, NPV, and BS were scaled according to their potential, reference values. We found a strong spatial and temporal correlation between scaled NPP with both scaled PV and scaled BS. Drastic differences were also found for PV and BS between degraded and non-degraded conditions. NPV displayed similarity to both PV and BS, however no clear relationship was found for NPV in all areas, irrespective of degradation conditions.

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Section: Fractional Covermentioning

confidence: 99%

Section: Interpretation Of Non-photosynthetic Vegetationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Degradation of Non-Photosynthetic Vegetation in a Semi-Arid Rangeland

Jackson

Prince

2016

Remote Sensing

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“…Generally, from the methodological point of view, there are three main FVC estimation methods using remotely sensed data: empirical methods, pixel un-mixing modeling, and physical model-based methods [9][10][11]. The empirical methods are based on the statistical relationships between FVC and spectral band reflectance or vegetation indices from airborne or satellite spectra [8,12].…”

Section: Introductionmentioning

confidence: 99%

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

et al. 2017

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Abstract:Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R 2 = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R 2 = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R 2 = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data.

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“…The method extracts information in sub-pixel scale instead of pixel scale. Many researches showed that this method is much better than the normalized difference vegetation index method [4,5] use in estimating vegetation coverage. Although the components of end-members in each pixel are the same, with the impact of natural background, the same component between the pixels is different.…”

Section: Introductionmentioning

confidence: 99%

Quantitative inversion of sparse vegetation coverage in desertification area

Li¹,

Zhang²,

Zhang³

et al. 2015

Proceedings of the 2015 International Conference on Applied Science and Engineering Innovation

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Abstract. Based on unmixing model and using the Thematic Mapper (TM) image as well as obtaining end-members by ground spectral measurements, quantitative retrieval of information on sparse vegetation coverage in oasis-desert transitional area in Minqin, Gansu was done. The results showed that a wide band of TM images can be used in extracting sparse vegetation coverage of arid regions. Three components were needed to build unmixing model and there includes light, vegetation -not light and vegetation -bare soil. And the unmixing model has a high correlation with measured vegetation coverage. Methods used to choose the end-members had certain influence on estimating the vegetation coverage. Monte Carlo method recorded 0.64 as coefficient of determination (R2) 3.8 as root mean square error (RMSE) and therefore was more accurate than the root mean square error minimization method.

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Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors

Cited by 335 publications

References 50 publications

Degradation of Non-Photosynthetic Vegetation in a Semi-Arid Rangeland

Degradation of Non-Photosynthetic Vegetation in a Semi-Arid Rangeland

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

Quantitative inversion of sparse vegetation coverage in desertification area

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