Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US

Pu, Ruiliang; Cheng, Jun

doi:10.1016/j.jag.2015.05.004

Cited by 63 publications

(45 citation statements)

References 46 publications

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“…This result is in consistency with other studies that demonstrated the utility of WV-2 in predicting LAI at different spatial scales of a landscape [14,19,77]. These findings therefore support the assertion that the potential utility of WV-2 spectral variables is improved predictions accuracy of vegetation biophysical traits such as LAI in indigenous ecosystems [14,19,30,77,115].…”

Section: Worldview-2 Image Potential In Predicting Lai Of Endangered supporting

confidence: 91%

“…SVIs based on absorption and reflectance in the visible and NIR regions (e.g., NDVI) have been widely used for predicting biophysical traits (e.g., LAI) of agricultural and natural ecosystems [19,30,34,35,77]. In this study, after the WV-2 image was processed, 24 SVIs were computed (Table 1) and utilized to predict the LAI of the six endangered tree species.…”

Section: Spectral Vegetation Indices (Svis)mentioning

confidence: 99%

“…In this study, after the WV-2 image was processed, 24 SVIs were computed (Table 1) and utilized to predict the LAI of the six endangered tree species. These indices were selected based on previous studies that predicted forest biophysical traits like LAI and biomass [30,[77][78][79][80][81]. We investigated the use of all SVIs combined together (n = 24) for predicting the LAI of the endangered tree species in each forest stratum (i.e., intact and fragmented forests).…”

Section: Spectral Vegetation Indices (Svis)mentioning

confidence: 99%

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Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

et al. 2016

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Leaf area index (LAI) is an important biophysical trait for forest ecosystem and ecological modeling, as it plays a key role for the forest productivity and structural characteristics. The groundbased methods like the handheld optical instruments for predicting LAI are subjective, pricy and time-consuming. The advent of very high spatial resolutions multispectral data and robust machine learning regression algorithms like support vector machines (SVM) and artificial neural networks (ANN) has provided an opportunity to estimate LAI at tree species level. The objective of the this study was therefore to test the utility of spectral vegetation indices (SVI) calculated from the multispectral WorldView-2 (WV-2) data in predicting LAI at tree species level using the SVM and ANN machine learning regression algorithms. We further tested whether there are significant differences between LAI of intact and fragmented (open) indigenous forest ecosystems at tree species level. The study shows that LAI at tree species level could accurately be estimated using the fragmented stratum data compared with the intact stratum data. Specifically, our study shows that the accurate LAI predictions were achieved for Hymenocardia ulmoides using the fragmented stratum data and SVM regression model based on a validation dataset (R 2 Val = 0.75, RMSE Val = 0.05 (1.37% of the mean)). Our study further showed that SVM regression approach achieved more accurate models for predicting the LAI of the six endangered tree species compared with ANN regression method. It is concluded that the successful application of the WV-2 data, SVM and ANN methods in predicting LAI of six endangered tree species in the Dukuduku indigenous forest could help in making informed decisions and policies regarding management, protection and conservation of these endangered tree species.

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Section: Worldview-2 Image Potential In Predicting Lai Of Endangered supporting

confidence: 91%

Section: Spectral Vegetation Indices (Svis)mentioning

confidence: 99%

Section: Spectral Vegetation Indices (Svis)mentioning

confidence: 99%

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Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

et al. 2016

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“…The reasons for choosing Band 4 to test the effects of the window size and direction on classifying forest health conditions include (1) the workload was too heavy to test all window sizes and directions for all four MS bands; (2) per statistics of training samples of Robinia pseudoacacia health conditions extracted from MS bands Band 4 was the most effective to discriminate among three health levels; and (3) Pu and Cheng (2015) supported that TM NIR band was the most important to correlate with LAI (note that TM NIR band has the same wavelength as IKONOS Band 4). Thus the optimal window size and direction would be determined based on IKONOS Band 4, and then the determined window size and direction would be applied to all other IKONOS MS bands.…”

Section: Glcm Textures and Local Spatial Statisticsmentioning

confidence: 99%

Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial and Textureal Information Extracted From Ikonos Imagery

Wang

Zhao

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: GLCM, Getis statistic, random forest, forest health condition, Robinia pseudoacacia ABSTRACT:In this study grey-level co-occurrence matrix (GLCM) textures and a local statistical analysis Getis statistic (Gi), computed from IKONOS multispectral (MS) imagery acquired from the Yellow River Delta in China, along with a random forest (RF) classifier, were used to discriminate Robina pseudoacacia tree health levels. The different RF classification results of the three forest health conditions were created: (1) an overall accuracy (OA) of 79.5% produced using the four MS band reflectances only; (2) an OA of 97.1% created with the eight GLCM features calculated from IKONOS Band 4 with the optimal window size of 13 × 13 and direction 45°; (3) an OA of 94.0% created using the four Gi features calculated from the four IKONOS MS bands with the optimal distance value of 5 and Queen's neighborhood rule; and (4) an OA of 96.9% created with the combined 16 spectral (four), spatial (four), and textural (eight) features. The experimental results demonstrate that (a) both textural and spatial information was more useful than spectral information in determining the Robina pseudoacacia forest health conditions; and (b) IKONOS NIR band was more powerful than visible bands in quantifying varying degree of forest crown dieback.

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“…On the other hand, it has been relatively rare to find quantitative applications for extracting biophysical parameters from HSR images.Although there have been a few cases of using HSR images to extract information regarding the physical conditions of vegetation (Imukova et al, 2015;Pu and Cheng, 2015;Tillack et al, 2014;Sprintsin et al, 2007) and water quality (Choe et al, 2015; Chang et al, 2009) …”

mentioning

confidence: 99%

Atmospheric Correction Problems with Multi-Temporal High Spatial Resolution Images from Different Satellite Sensors

Lee¹,

Lee²

2015

Korean Journal of Remote Sensing

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Since the launch of the IKONOS-1 in 1999 and several other following commercial satellites, High Spatial Resolution (HSR) satellite images have been widely used for topographic mapping, Digital Elevation Model (DEM) data generation, and urban and land cover classifications. On the other hand, it has been relatively rare to find quantitative applications for extracting biophysical parameters from HSR images.Although there have been a few cases of using HSR images to extract information regarding the physical conditions of vegetation (Imukova et al., 2015;Pu and Cheng, 2015;Tillack et al., 2014;Sprintsin et al., 2007) and water quality (Choe et al., 2015; Chang et al., 2009) Department of Geoinformatic Engineering, Inha UniversityAbstract : Atmospheric correction is an essential part in time-series analysis on biophysical parameters of surface features. In this study, we tried to examine possible problems in atmospheric correction of multitemporal High Spatial Resolution (HSR) images obtained from two different sensor systems. Three KOMPSAT-2 and two IKONOS-2 multispectral images were used. Three atmospheric correction methods were applied to derive surface reflectance: (1) Radiative Transfer (RT) -based absolute atmospheric correction method, (2) the Dark Object Subtraction (DOS) method, and (3) the Cosine Of the Uun zeniTh angle (COST) method. Atmospheric correction results were evaluated by comparing spectral reflectance values extracted from invariant targets and vegetation cover types. In overall, multi-temporal reflectance from five images obtained from January to December did not show consistent pattern in invariant targets and did not follow a typical profile of vegetation growth in forests and rice field. The multi-temporal reflectance values were different by sensor type and atmospheric correction methods. The inconsistent atmospheric correction results from these multi-temporal HSR images may be explained by several factors including unstable radiometric calibration coefficients for each sensor and wide range of sun and sensor geometry with the off-nadir viewing HSR images.Key Words : Atmospheric correction, high spatial resolution, calibration coefficients, multi-temporal, surface reflectance, KOMPSAT, IKONOS Atmospheric Correction MethodsAtmospheric correction of optical imagery is mainly to derive purely reflected portion of energy from the atsensor radiance (Lt) by removing the amount of radiance influenced by atmospheric effects. The radiance (Lt) received on-board sensor can be expressed in a simple form of equation (Eq. 1) and atmospheric correction is the process of extracting surface reflectance (ρ) from Lt.where ρ = surface reflectance, E = solar and diffuse irradiance on surface, T = transmittance of atmosphere, and Lp = path radiance.Atmospheric correction methods of optical imagery can be divided into two major categories by means of obtaining the unknown variables of T and Lp in (Eq. 1).Absolute atmospheric correction is based on the calculation of physical processes of radiative trans...

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Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US

Cited by 63 publications

References 46 publications

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial and Textureal Information Extracted From Ikonos Imagery

Atmospheric Correction Problems with Multi-Temporal High Spatial Resolution Images from Different Satellite Sensors

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