Comparing the Dry Season In-Situ Leaf Area Index (LAI) Derived from High-Resolution RapidEye Imagery with MODIS LAI in a Namibian Savanna

Mayr, Manuel; Samimi, Cyrus

doi:10.3390/rs70404834

Cited by 24 publications

(17 citation statements)

References 64 publications

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“…This underestimation by MODIS LAI data product cannot be explained by the high spatial heterogeneity in the grazing grasslands and the different footprint sizes between ground measurements and MODIS observations. This finding corroborates the reports on the under-and over-estimates of MODIS LAI in grasslands and other grazing ecosystems (e.g., savannas) in previous studies (Fu and Wu, 2017;Mayr and Samimi, 2015). The S1/S2/LC8 methods developed in this paper have the potential for higher accuracy estimation of LAI at a higher spatial resolution, which would greatly increase their utility for land management applications.…”

Section: Estimates Of Grassland Lai From High Resolution Images (S1 supporting

confidence: 90%

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Wang

Xiao

Starks

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

218

126

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Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10-30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI < 2 m 2 /m 2 , AGB < 500 g/m 2) and optical data of LC8 and S2 at high vegetation cover (LAI > 2 m 2 /m 2 , AGB > 500 g/m 2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management.

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Section: Estimates Of Grassland Lai From High Resolution Images (S1 supporting

confidence: 90%

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Wang

Xiao

Starks

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

218

126

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“…The slight overestimation in C6 relative to C5 is due to scale effects and refinements to surface reflectances [5]. C5 shows the most obvious underestimation in savanna, which is in agreement with [28]. This issue has been mitigated by C6 to some extent.…”

Section: Characteristics Of Measurementssupporting

confidence: 73%

Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation and Intercomparison

et al. 2016

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Abstract:The aim of this paper is to assess the latest version of the MODIS LAI/FPAR product (MOD15A2H), namely Collection 6 (C6). We comprehensively evaluate this product through three approaches: validation with field measurements, intercomparison with other LAI/FPAR products and comparison with climate variables. Comparisons between ground measurements and C6, as well as C5 LAI/FPAR indicate: (1) MODIS LAI is closer to true LAI than effective LAI; (2) the C6 product is considerably better than C5 with RMSE decreasing from 0.80 down to 0.66; (3) both C5 and C6 products overestimate FPAR over sparsely-vegetated areas. Intercomparisons with three existing global LAI/FPAR products (GLASS, CYCLOPES and GEOV1) are carried out at site, continental and global scales. MODIS and GLASS (CYCLOPES and GEOV1) agree better with each other. This is expected because the surface reflectances, from which these products were derived, were obtained from the same instrument. Considering all biome types, the RMSE of LAI (FPAR) derived from any two products ranges between 0.36 (0.05) and 0.56 (0.09). Temporal comparisons over seven sites for the [2001][2002][2003][2004] period indicate that all products properly capture the seasonality in different biomes, except evergreen broadleaf forests, where infrequent observations due to cloud contamination induce unrealistic variations. Thirteen years of C6 LAI, temperature and precipitation time series data are used to assess the degree of correspondence between their variations. The statistically-significant associations between C6 LAI and climate variables indicate that C6 LAI has the potential to provide reliable biophysical information about the land surface when diagnosing climate-driven vegetation responses.

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“…The two woody species in this study generally had LAI < 5.0 and, therefore, stem material did play a role in the NIR reflectance, which could explain the lack of significant difference in above canopy NIR reflectance (captured on the SPOT 6 NAOMI image) between the species. In general, however, the LAI values derived from this study are much higher than those determined in-situ by Mayr & Samimi (2015) in the savannahs of Namibia, due to the larger tree and canopy structures of the woody vegetation at this study's sites. This further indicates the potential role of LAI in indicating woody vegetation content of savannahs.…”

Section: Discussioncontrasting

confidence: 75%

“…LAI is the more reliable leaf characteristic in aid of this analysis, using LAI retrieval algorithms (e.g. Mayr & Samimi, 2015;Ribeiro et al, 2008). Asner (1998) showed that stem material played a small but significant role in determining canopy reflectance in woody plant canopies, especially those with LAI < 5.0.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the effects of leaf characteristics on spectral signatures of savannah woody species on remotely sensed imagery

Munyati¹,

Malomane²,

Malahlela³

2019

SA J of Geomatics

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Woody species on savannahs provide nutrition to wildlife and livestock ungulates. Understanding of woody leaf reflectance would benefit the application of remote sensing in analysis of these rangelands. This study sought to establish the influence of the leaf characteristics of size (leaf form) and chlorophyll content on the spectral reflectance characteristics of savannah woody species. The ability of optical remotely-sensed imagery to identify these two leaf characteristics and use them to determine the woody content of savannahs was then assessed. Two species that represented narrow leaf and broad leaf savannah woody species were studied: Acacia tortilis (renamed Vachellia tortilis) and Ziziphus mucronata, respectively. Forty seven woody individuals representing these species were sampled in northwestern South Africa. Chlorophyll content, leaf area index (LAI) and spectral reflectance were determined in-situ using a chlorophyll meter, a canopy analyser and spectroradiometer, respectively. A SPOT 6 NAOMI image acquired at a time of year when grass reflectance was excluded from the spectral signature of non-senescent vegetation was used. The image data were converted to reflectance (%), and the green and near infrared (NIR) reflectance of the field-sampled trees on the image were correlated with the in-situ data. The results showed that the woody species differed significantly in their chlorophyll content and green reflectance, but only the NIR reflectance of the broad leaf species correlated strongly with a leaf characteristic, LAI. From the results it is concluded that LAI is the more reliable leaf characteristic for analysing the characteristics of savannahs in terms of woody content.

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Comparing the Dry Season In-Situ Leaf Area Index (LAI) Derived from High-Resolution RapidEye Imagery with MODIS LAI in a Namibian Savanna

Cited by 24 publications

References 64 publications

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation and Intercomparison

Evaluating the effects of leaf characteristics on spectral signatures of savannah woody species on remotely sensed imagery

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