Estimation of tropical forest structure from SPOT-5 satellite images

Castillo-Santiago, Miguel Ángel; Ricker, Martin; Jong, Bernardus Hendricus jozeph De

doi:10.1080/01431160903095460

Cited by 80 publications

(60 citation statements)

References 35 publications

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“…Similar pattern was also observed for DDI (Figure 2). The window size of 9ˆ9 pixels was therefore determined to be the optimum size to calculate the texture statistics, which was consistent with the findings reported by Shaban and Dikshit [53] and Castillo-santiago et al [35].…”

Section: Correlation Analysessupporting

confidence: 79%

“…In addition to the spatial resolution, the value of the textural variables depends on window size. To determine the optimum window size, Shaban and Dikshit [53] and Castillo-santiago et al [35] calculated and compared the Pearson correlation coefficient of texture statistics with the dependent variables (forest stand variables) at different window sizes and concluded that a 9ˆ9 pixel window represented a trade-off between a desirable high correlation coefficient and a desirable minimum window size. Following them, we calculated the Pearson correlation coefficient of texture statistics with SHI and DDI, at seven window sizes (3ˆ3, 5ˆ5, 7ˆ7, 9ˆ9, 11ˆ11, 13ˆ13 and 15ˆ15 pixels).…”

Section: Textural Measuresmentioning

confidence: 99%

“…In the present study, we used all possible subsets. Following Castillo-Santiago et al [35], Ozdemir and Karnieli [13] and Wallner et al [55], we employed a cut-off value for variance inflation factor (VIF) of less than four, and restricted the number of independent variables to four to avoid multicollinearity.…”

Section: Model Construction and Validationmentioning

confidence: 99%

“…Atmospheric correction was carried out using the improved Dark Object Subtraction suggested by Castillo-santiago et al [35]. The processing of these images was performed by the Survey & Planning Institute of State Forestry Administration, China prior to the present study.…”

Section: Remote Sensing Datamentioning

confidence: 99%

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Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Meng

Wang

et al. 2016

Remote Sensing

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Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson's correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination (R 2 adj ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models (R 2 adj > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.

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Section: Correlation Analysessupporting

confidence: 79%

Section: Textural Measuresmentioning

confidence: 99%

Section: Model Construction and Validationmentioning

confidence: 99%

Section: Remote Sensing Datamentioning

confidence: 99%

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Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Meng

Wang

et al. 2016

Remote Sensing

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“…A gray level co-occurrence matrix (GLCM) method was applied here to obtain the textural features to ensure good performance in texture analysis [52][53][54][55][56]. These features include the mean, variance, homogeneity, contrast, dissimilarity, entropy, angular second moment, and correlation calculated in a window of 3 × 3 pixels.…”

Section: Textural Featuresmentioning

confidence: 99%

Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data

Hasituya¹,

Chen

2017

Remote Sensing

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Using plastic mulching for farmland is booming around the world. Despite its benefit of protecting crops from unfavorable conditions and increasing crop yield, the massive use of the plastic-mulching technique causes many environmental problems. Therefore, timely and effective mapping of plastic-mulched farmland (PMF) is of great interest to policy-makers to leverage the trade-off between economic profit and adverse environmental impacts. However, it is still challenging to implement remote-sensing-based PMF mapping due to its changing spectral characteristics with the growing seasons of crops and geographic regions. In this study, we examined the potential of multi-temporal Landsat-8 imagery for mapping PMF. To this end, we gathered the information of spectra, textures, indices, and thermal features into random forest (RF) and support vector machine (SVM) algorithms in order to select the common characteristics for distinguishing PMF from other land cover types. The experiment was conducted in Jizhou, Hebei Province. The results demonstrated that the spectral features and indices features of NDVI (normalized difference vegetation index), GI (greenness index), and textural features of mean are more important than the other features for mapping PMF in Jizhou. With that, the optimal period for mapping PMF is in April, followed by May. A combination of these two times (April and May) is better than later in the season. The highest overall, producer's, and user's accuracies achieved were 97.01%, 92.48%, and 96.40% in Jizhou, respectively.

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