Uncovering the spatio-temporal dynamics of land cover change and fragmentation of mangroves in the Ca Mau peninsula, Vietnam using multi-temporal SPOT satellite imagery (2004–2013)

Hauser, Leon T.; Vu, Giang Nguyen; Nguyen, Binh An; Dade, E; Nguyen, Hieu Minh; Nguyen, Trang Thi Quynh; Le, Toan Quang; Vu, Long Huu; Tong, Ai Thi Huyen; Pham, Hoa Viet

doi:10.1016/j.apgeog.2017.06.019

Cited by 62 publications

(35 citation statements)

References 35 publications

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“…The data fusion process was carried out on Landsat 8 data on the Red-Near Infrared (NIR)-Short Wave Infrared-1 (SWIR-1) bands (30 x 30 m resolution) with band 8 (panchromatic with a resolution of 15 x 15 m). The result of fusion will make the display visually clear (Clark et al, 2011), where the color pattern is obtained from 30 x 30 m resolution images (Hauser et al, 2017), while the panchromatic band will sharpen the impression on the texture (Wang et al, 2018). Based on this, a training area was made in the Landsat 8 data to obtain reflectance values (Adam et al, 2010).…”

Section: Optimum Index Factor (Oif)mentioning

confidence: 99%

Detection of True Mangroves in Indonesia Using Satellite Remote Sensing

Julzarika

Anggraini

Adawiah

2019

J. Env. Anal. Progr.

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Mangrove existence is necessary to protect the coastal. One method that can be used to keep mangrove existence were using satellite imagery monitoring. The number of bands in the imagery led to the selection for the RGB composite bands was difficult because of many combinations to try. One technique that can be done to get the best RGB combination of an object is to use Optimum Index Factor (OIF). OIF is a statistical technique for selecting three combinations of imagery bands to visualize the image display to the fullest. It is based on the value of total variance and the correlation coefficient between the bands. Landsat 8 has seven bands with 30 m resolution, one panchromatic band with 15 m resolution, and two bands with 100 m resolution. The purpose of this study was to detect true mangrove using three bands from OIF value of Landsat 8. The results of the processing from 6 bands (2-3-4-5-6-7) obtained 20 bands combinations with the highest value of OIF is 0,168, i.e., bands 2-5-6 (Blue, NIR, SWIR-1). Based on the combination, the next step was the unsupervised classification process for true mangrove identification (Rizhopora, Brugueira, Avicennia and Soneratia). The best classification using band combination 2-3-4-5-6-7 with true mangrove reached 4.041 ha.

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Section: Optimum Index Factor (Oif)mentioning

confidence: 99%

Detection of True Mangroves in Indonesia Using Satellite Remote Sensing

Julzarika

Anggraini

Adawiah

2019

J. Env. Anal. Progr.

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“…The launch and availability of sensors with improved spatial, spectral, and radiometric characteristics can further contribute to improving the accuracy of forest measuring variables. SPOT-6/7 series sensors with a high spatial resolution (1.5 m in panchromatic and 6.0 m in multispectral bands) have been used in forest variable prediction and achieved encouraging results in estimating forest variables [16]. The Gaofen-2(GF-2) satellite, which launched in 2014 and is configured with two panchromatic and multispectral charge coupled device (CCD) camera sensors, can achieve a spatial resolution of 1 m in panchromatic mode and a resolution of 4 m in four spectral bands in multispectral mode [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Comparison of GF2 and SPOT6 Imagery on Canopy Cover Estimating in Northern Subtropics Forest in China

Zhou

Dian

Wang

et al. 2020

Forests

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Canopy cover is an important vegetation attribute used for many environmental applications such as defining management objectives, thinning and ecological modeling. However, the estimation of canopy cover from high spatial resolution imagery is still a difficult task due to limited spectral information and the heterogeneous pixel values of the same canopy. In this paper, we compared the capacity of two high spatial resolution sensors (SPOT6 and GF2) using three ensemble learning models (Adaptive Boosting (AdaBoost), Gradient Boosting (GDBoost), and random forest (RF)), to estimate canopy cover (CC) in a Chinese northern subtropics forest. Canopy cover across 97 plots was measured across 41 needle forest plots, 24 broadleaf forest plots, and 32 mixed forest plots. Results showed that (1) the textural features performed more importantly than spectral variables according to the number of variables in the top ten predictors in estimating canopy cover (CC) in both SPOT6 and GF2. Moreover, the vegetation indices in spectral variables had a lower relative importance value than the band reflectance variables. (2) GF2 imagery outperformed SPOT6 imagery in estimating CC when using the ensemble learning model in our data. On average across the models, the R2 was almost 0.08 higher for GF2 over SPOT6. Likewise, the average RMSE and average MAE were 0.002 and 0.01 lower in GF2 than in SPOT6. (3) The ensemble learning model showed good results in estimating CC, yet the different models performed a little differently in the results. Additionally, the GDBoost model performed the best of all the ensemble learning models with R2 = 0.92, root mean square error (RMSE) = 0.001 and mean absolute error (MAE) = 0.022.

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“…Due to rapid, large-scale and cost-effective monitoring capacities, remote sensing techniques have been increasingly adopted to survey and evaluate mangrove resources during the past several decades [10]. Medium-resolution multispectral imagery, such as that from Landsat [11,12], SPOT [13], and Sentinel-2 [8] are often used to map the distribution of mangrove forests at regional or even national or global scales. Due to the advantage of their having superb spatial and textural features and high-resolution multispectral images, satellites such as Quickbird [14], IKONOS [15], Worldview [16,17], and Pléiades-1 [18] have been widely employed to classify mangrove species at landscape or regional scales.…”

Section: Introductionmentioning

confidence: 99%

Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

Wang

Xia

et al. 2019

Remote Sensing

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Due to continuous degradation of mangrove forests, the accurate monitoring of spatial distribution and species composition of mangroves is essential for restoration, conservation and management of coastal ecosystems. With leaf hyperspectral reflectance, this study aimed to explore the potential of continuous wavelet analysis (CWA) combined with different sample subset partition (stratified random sampling (STRAT), Kennard-Stone sampling algorithm (KS), and sample subset partition based on joint X-Y distances (SPXY)) and feature extraction methods (principal component analysis (PCA), successive projections algorithm (SPA), and vegetation index (VI)) in mangrove species classification. A total of 301 mangrove leaf samples with four species (Avicennia marina, Bruguiera gymnorrhiza, Kandelia obovate and Aegiceras corniculatum) were collected across six different regions. The smoothed reflectance (Smth) and first derivative reflectance (Der) spectra were subjected to CWA using different wavelet scales, and a total of 270 random forest classification models were established and compared. Among the 120 models with CWA of Smth, 88.3% of models increased the overall accuracy (OA) values with an improvement of 0.2–28.6% compared to the model with the Smth spectra; among the 120 models with CWA of Der, 25.8% of models increased the OA values with an improvement of 0.1–11.4% compared to the model with the Der spectra. The model with CWA of Der at the scale of 23 coupling with STRAT and SPA achieved the best classification result (OA = 98.0%), while the best model with Smth and Der alone had OA values of 86.3% and 93.0%, respectively. Moreover, the models using STRAT outperformed those using KS and SPXY, and the models using PCA and SPA had better performances than those using VIs. We have concluded that CWA with suitable scales holds great potential in improving the classification accuracy of mangrove species, and that STRAT combined with the PCA or SPA method is also recommended to improve classification performance. These results may lay the foundation for further studies with UAV-acquired or satellite hyperspectral data, and the encouraging performance of CWA for mangrove species classification can also be extended to other plant species.

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Uncovering the spatio-temporal dynamics of land cover change and fragmentation of mangroves in the Ca Mau peninsula, Vietnam using multi-temporal SPOT satellite imagery (2004–2013)

Cited by 62 publications

References 35 publications

Detection of True Mangroves in Indonesia Using Satellite Remote Sensing

Detection of True Mangroves in Indonesia Using Satellite Remote Sensing

Comparison of GF2 and SPOT6 Imagery on Canopy Cover Estimating in Northern Subtropics Forest in China

Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

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