Tien Dat Pham

Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks

This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model’s performance using root-mean-square error, mean absolute error, coefficient of determination (R2), and leave-one-out cross-validation. We also compared the model’s usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95 Mg ha−1 (average = 55.8 Mg ha−1); below-ground biomass ranged between 4.06 and 436.47 Mg ha−1 (average = 81.47 Mg ha−1), and total carbon stock ranged between 3.22 and 345.65 Mg C ha−1 (average = 64.52 Mg C ha−1). We conclude that ALOS-2 PALSAR data can be accurately used with MLPNN models for estimating mangrove forest biomass in tropical areas.

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R2), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R2 = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R2 = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R2 = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R2 = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only.

Aboveground biomass estimation of mangrove species using ALOS-2 PALSAR imagery in Hai Phong City, Vietnam

Abstract. This study examined the potential of using the HH and HV backscatter from the Advanced Land Observing Satellite 2 (ALOS-2) with enhanced phased array L-band synthetic aperture radar (PALSAR) in high sensitive mode to estimate the above-ground biomass (AGB) of the two mangrove species of Hai Phong city, Vietnam. A positive correlation was observed between the mean backscattering coefficients of the dominant mangrove species at dual polarizations HH and HV and various biophysical parameters. In contrast, low correlations were observed between those coefficients and the tree densities for the two mangrove species. The AGB of the mangrove species were estimated at between 2.8 and 161.5  Mg ha−1 with an average of about 39  Mg ha−1 for Sonneratia caseolaris and between 27.6 and 209.2  Mg ha−1 with an average of ∼100  Mg ha−1 for Kandelia obovata. The main indicators used for the selection of the best potential models in estimating the AGB of different species were R2 and the root-mean-square error (RMSE). The results showed a satisfactory correlation between model estimation and field-based measurements with R2=0.51, RMSE=35.5  Mg ha−1 for S. caseolaris and R2=0.64, RMSE=41.3  Mg ha−1 for K. obovata. This research has illustrated the potential use of ALOS-2 PALSAR data in estimating the AGB of mangrove species in the tropics.

Mangrove analysis using ALOS imagery in Hai Phong City, Vietnam

Mangroves that appear in the inter-tidal zones along the coast in most tropical and semi-tropical countries play a vital role in coastal zones and can defend against the impacts of tsunamis. Nevertheless, these forests are under severe threat because of high population growth, weak governance, poor planning, as well as uncoordinated economic development. Hai Phong city is located on the Northern coast of Vietnam where the mangroves are distributed between zone I and zone II among the four mangrove zones in Vietnam. This city is vulnerable to rising sea levels and tropical cyclones, which are forecasted to become more severe in coming next decades. The objectives of this research were to analyze the current status of mangroves using different ALOS sensors in Hai Phong, Vietnam in 2010 and compare the accuracy of the post satellite image processing of ALOS imagery in mapping mangroves. A combination of object-based and supervised classification was used to generate the land cover maps. The results of this research indicate that the total area of mangrove was approximately 2,549 hectares and mangrove is present in the five coastal districts in Hai Phong. The findings of this research showed that ALOS AVIR-2 provides better accuracy than ALOS PALSAR. This research indicates the potential of utilizing image segmentation associated with supervised method for both optical and SAR images to map mangrove forests in coastal zones

Optimized rule-based logistic model tree algorithm for mapping mangrove species using ALOS PALSAR imagery and GIS in the tropical region

Abstract The main objective of this study is to map the spatial distribution of mangrove species and assess their changes from 2010 to 2015 in Hai Phong City of Vietnam located on the tropical region using the ALOS PALSAR data and an optimized rule-based decision tree technique. For this purpose, ALOS PALSAR imagery for the above period and GIS data were collected and used, and then, spatial distributions of mangrove species were derived using logistic model tree (LMT) classifier. The LMT is current state-of-the-art machine learning method that has not been used for mapping of mangrove species. The results showed that incorporation of ALOS PALSAR imagery and GIS in the LMT algorithm provides satisfactory overall accuracy and kappa coefficient. The ALOS-2 PALSAR for 2015 achieved better overall accuracy, with an increment of 3.6% in mapping mangrove species than that of the ALOS PALSAR for 2010. The ALOS-2 PALSAR-derived model yielded the overall accuracy of 83.8% and the kappa coefficient of 0.81, compared with those of the ALOS PALSAR-derived model, 80.2% and 0.78, respectively. The results of classification for 2010 and 2015 were significantly different using the McNemar test. This research demonstrates the potential use of ALOS PALSAR together with machine learning techniques in monitoring mangrove species in tropical areas.

Monitoring Mangrove Forest Changes in Cat Ba Biosphere Reserve Using ALOS PALSAR Imagery and a GIS-Based Support Vector Machine Algorithm

Cat Ba is one of the most well-known islands located in North Vietnam, which has been recognized as a biosphere reserve by United Nations Educational, Scientific and Cultural Organization (UNESCO) since 2004. Despite the large potential carbon stocks in mangrove forests of Cat Ba, the mangrove ecosystem of this island has suffered severe deforestation and forest degradation due to the conversion to shrimp aquaculture. Monitoring mangrove forest changes plays an important role for effective mangrove conservation and management. The objectives of this study were to map the spatial distribution of mangrove forest and to assess their changes between 2010 and 2015 in Cat Ba Biosphere Reserve, Hai Phong city of Vietnam using ALOS PALSAR data and a GIS-based support vector machine algorithm. For this purpose, ALOS PALSAR imagery for the above period and GIS data were collected. Then, spatial distributions of mangroves were derived using the support vector machine classifier. The results showed that the ALOS-2 PALSAR for 2015 achieves the overall accuracy of 85% and the kappa coefficient of 0.81, compared with those of 81% and 0.77, respectively from the ALOS PALSAR for 2010. The mangrove forest areas in the Cat Ba Biosphere Reserve, Vietnam decreased by 15% from 2010 to 2015. This research shows the potential use of ALOS PALSAR data combined with machine learning techniques in monitoring mangrove forest changes in tropical and semi-tropical climates.

Characterization of mangrove species using ALOS-2 PALSAR in Hai Phong city, Vietnam

Hai Phong city is located in the Northern coast of Vietnam where the mangroves are distributed between zone I and zone II among the four mangrove zones in Vietnam. This city is vulnerable to rising sea levels associated with climate change and tropical cyclones, which are forecasted to become more severe due to the impact of climate change. In the past, mangrove forests in this city have decreased markedly because of over expansion of shrimp farming. Thus, identification of mangrove forests is important to monitor and support mangrove conservation and management in the coastal zone. The objectives of this research were to identify the locations of mangrove and characterize mangrove species in Hai Phong using HH and HV backscatters of the Advanced Land Observing Satellite 2 (ALOS-2) with enhanced Phased Array L-band Synthetic Aperture Radar (PALSAR). Image segmentation was used to identify the locations of mangrove forests. Moreover, Geographic Information System (GIS) was applied to update current status of mangrove species in 2015. The results showed that the means of HH and HV backscatter coefficients of K. obovata are lower than S. caseolaris. K. obovata has HH value around -13.9 dB until -10.3 dB and HV value around -20.6 dB until -16.2 dB. Higher HH values between about -14.9 dB and -6.8 dB and HV values between roughly -20.6 dB and -14.3 dB have observed by S. caseolaris. The total area of mangrove forest in Hai Phong in the year 2015 was around 4084 hectares, of which S. caseolaris occupied over 68% and mixed mangrove species was approximately 25.6%. This research indicates the potential for the use of L-band ALOS-2 PALSAR in characterizing mangrove forest species in the coastal zone.

Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data

ABSTRACT Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R2 = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha−1 (average = 87.67 Mg ha−1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.

Identification of irrigated olive growing farms using remote sensing techniques

In spite of water scarcity problem in Tunisia, irrigation strategy was adopted and supported by the government. In addition, the introduction of the irrigation in olive sector was also encouraged because it will be the way for maintaining the production of this strategic product. Therefore, the main objective of this work is to detect irrigated and non-irrigated olive growing farms using remote sensing techniques which can be useful to establish efficient and sustainable strategy of water resource management. This study was conducted in olive growing farms of 04 regions (Bouhajla, Nasrallah, Chebika and Oueslatia) in Kairouan prefecture. To monitor the irrigated olive growing farms, we used multi-temporal Landsat 8 imagery from June to September 2015 which were processed to obtain specific vegetation indices such as RVI (ratio vegetation index) and NDVI (Normalized Difference Vegetation Index). Moreover, LST (Land Surface Temperature) and BT (Brightness Temperature) are produced to assess temperature. To drought monitoring, VCI (Vegetation Condition Index), TCI (Temperature Condition Index), and VHI (Vegetation Health Index) were applied as indicators. The validation approach was based on the field survey and data collection. The main results of the study are the detection of irrigated and rainfed olive growing farms using specific vegetation indexes and indicators. The findings are validated by the field observations for checking the accuracy of the results. This study can be useful for stakeholders to detect the olive growing farms which can be helpful to estimate the olive production.