Mapping forest aboveground biomass in the reforested Buffelsdraai landfill site using texture combinations computed from SPOT-6 pan-sharpened imagery

Hlatshwayo, Sizwe Thamsanqa; Mutanga, Onisimo; Lottering, Romano; Kiala, Zolo; Ismail, Riyad

doi:10.1016/j.jag.2018.09.005

Cited by 50 publications

(28 citation statements)

References 44 publications

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“…To date, texture analysis on satellite imagery was mostly used for forest AGB [14][15][16]. Our findings provide a strong reference on crop N status with texture analysis of satellite images equipped with the RE band.…”

Section: Potentials For Other Platformsmentioning

confidence: 70%

“…The directional effect of texture analysis was rarely investigated in the existing literature, since the majority of previous studies executed texture analysis with the default direction (45 • ) [7,15]. Some studies calculated texture metrics with different directions but did not explicitly explain the reason [10,16].…”

Section: Directional Effect Of Texture Analysis On Row-planted Cropsmentioning

confidence: 99%

“…While the former three factors were well studied for vegetation monitoring, the effect of texture direction has rarely been investigated. Previous studies often conducted texture analysis with the default direction (45 • ), because most of them focused on naturally grown forests with disorderly distributed tress [7,15]. Others exercised with different directions for naturally grown trees, but they did not explicitly provide the mechanism underlying the direction choices [10,14,16].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Zheng

Zhou

et al. 2020

Remote Sensing

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This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season.

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Section: Potentials For Other Platformsmentioning

confidence: 70%

Section: Directional Effect Of Texture Analysis On Row-planted Cropsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Zheng

Zhou

et al. 2020

Remote Sensing

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“…Barati et al [35] found that the DVI index with a value of 0.668 for the coefficient of determination (R 2 ) showed the best fractional vegetation cover estimation in sparse vegetated areas. Remotely sensed image texture may be a good proxy of vegetation structures [36], and shows sensitivity to AGB variations [37]. The mean texture parameter values for the NIR1 and NIR2 bands provided higher significant correlations with AGB of 0.760 and 0.748 at p = 0.01, respectively.…”

Section: Relationship Of the Measured Agb With Remote Sensing Variablesmentioning

confidence: 95%

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Bao

et al. 2019

Remote Sensing

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The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R 2 = 0.79 and RMSE = 22.82 g/m 2 ) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m −2 on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management.

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“…Using the RF classifier, results revealed improved overall classification accuracies as well as better kappa, user's and producer's accuracies when compared to excluding textural variables (Table 4; Figure 5). The GLCM textural measure was computed using a specified angle (45 • ) with window sizes for each band calculated [47,48] using the minimum variance approach [42,43]. A 5 × 5 window was used for the SPOT 5 m dataset, where as a window size of 3 × 3 was used for the 1.5 m dataset.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Trade-Offs of SPOT7 Imagery for Monitoring Natural Forest Canopy Intactness

Bhugeloo

Peerbhay

Ramdhani

et al. 2018

Forests

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Natural and human-induced disturbances influence the biodiversity and functionality of forest ecosystems. Regular, repeated assessments of canopy intactness are essential to map site-specific forest disturbance and recovery patterns, an essential requirement for forest monitoring and management. However, accessibility to images required for this practice, uncertainty around the levels of accuracy achieved with images of different resolution, and the affordability of the practice challenges its application in many developing regions. This study aimed to compare the accuracy of forest gap detection (in subtropical forests) achieved with lower-resolution (SPOT7 5 m) and higher-resolution (SPOT7 1.5 m) pan-sharpened imagery. Additionally, the Normalised Difference Vegetation Index (NDVI) and Synthetic Aperture Radar (SAR) were compared in terms of their ability to increase the accuracy of this detection when used in conjunction with both high and low resolution imagery. Results indicate that the SPOT7 1.5 m imagery produced an overall accuracy of 77.78% and a ϰ coefficient of 0.66 compared with the 69.44% accuracy and the 0.59 ϰ coefficient achieved with the SPOT7 5 m imagery. Computing image texture analysis within the Random Forest classifier (RF) framework increased classification accuracies to 75.00% for the SPOT 5 m and 86.11% for the SPOT7 1.5 m imagery, validating the usefulness of texture analysis. Variable importance was used to identify wavebands and texture-derived variables that were the most effective in discriminating canopy gaps from intact canopy. In this regard, near infrared, NDVI, SAR, contrast, mean, entropy and second moment were the most important. Collectively the results indicate that the approach adopted in this study, i.e., the use of SPOT7 1.5 m imagery in conjunction with image texture analysis and variable importance, can be used to accurately discriminate between canopy gaps and intact canopy, making it a cost-effective spatial approach for monitoring and managing natural forests.

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Mapping forest aboveground biomass in the reforested Buffelsdraai landfill site using texture combinations computed from SPOT-6 pan-sharpened imagery

Cited by 50 publications

References 44 publications

Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Assessing the Trade-Offs of SPOT7 Imagery for Monitoring Natural Forest Canopy Intactness

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