Near infrared band of Landsat 8 as water index: a case study around Cordova and Lapu-Lapu City, Cebu, Philippines

Mondejar, Jeremy P.; Tongco, Alejandro F.

doi:10.1186/s42834-019-0016-5

Cited by 59 publications

(38 citation statements)

References 47 publications

(168 reference statements)

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“…Multispectral image analysis requires conversion of its "quantized and calibrated scaled digital numbers" [23] to top of atmosphere reflectance in order to achieve clear Landsat scenes [28] which is packaged in SCP. Preprocessing of Landsat 8 image was discussed in details by Congedo [27] and in the study by Mondejar and Tongco [29].…”

Section: Preprocessing Of Remotely Sensed Datamentioning

confidence: 99%

Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines

Mondejar

Tongco

2019

Sustain Environ Res

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Digital soil mapping for soil texture is mostly an understanding of how soil texture fractions vary in space as influenced by environmental variables mainly derived from the digital elevation model (DEM). In this study, topsoil texture models were generated and evaluated by multiple linear regression (MLR), ordinary kriging (OK), simple kriging (SK) and universal kriging (UK) using free and open-source R, System for Automated Geoscientific Analyses, and QGIS software. Comparing these models is the main objective of the study. The study site covers an area of 124 km 2 of the Municipality of Barili, Cebu. A total of 177 soil samples were gathered and analyzed from irregular sample points. DEM derivatives and remote sensing data (Landsat 8) were used as environmental variables. Exploratory analyses revealed no outlier in the data. Skewness and kurtosis values of the untransformed data vary greatly between -3.85 to 7.20 and 1.8 to 70.7, respectively; an indication that variables are highly skewed with heavy tails. Thus, Tukey's ladder of powers transformation was applied that resulted to normal or nearly normal distribution having skewness values close to zero and kurtosis values have lighter tails. All data analysis from MLR modeling, variography, kriging, and crossvalidations of models were implemented using the transformed data. Forward selection, backward elimination, and stepwise selection methods were adapted for predictors selection in MLR. The MLR, OK, SK, and UK were applied and cross validated for topsoil texture prediction. Likewise, exponential, Gaussian, and spherical models were fitted for the experimental variograms. Backward elimination method for clay, sand, and silt have the lowest MAE and highest R 2 in MLR. The UK fitted with exponential variogram model has the highest R 2 of 0.878, 0.821, and 0.893 for clay, sand, and silt, respectively. These models can be adapted as a decision support for agricultural land use planning and crop suitability development in the area.

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Section: Preprocessing Of Remotely Sensed Datamentioning

confidence: 99%

Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines

Mondejar

Tongco

2019

Sustain Environ Res

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“…Since remote sensing image acquisition information is less affected by natural conditions, large area images can be acquired in a short time and at a low cost. Therefore, a large number of remote sensing images have been used for sea-land segmentation [6,7], water extraction [8][9][10][11][12][13][14][15][16][17][18][19], and other applications. In the recent years of research, numerous water extraction methods have been proposed, such as the threshold method [8,9,[16][17][18], machine learning method [10,11,19], and deep learning method [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing images use different bands and contain different information. The threshold method is widely used in the field of water extraction, which involves with the number of bands used, mainly single band [16] and multi-band [8,17]. The difference between the water bodies and non-water objects in the NIR band is the largest, and a single NIR band as the water index can be used to obtain satisfactory results for water bodies' extraction [16].…”

Section: Introductionmentioning

confidence: 99%

“…The threshold method is widely used in the field of water extraction, which involves with the number of bands used, mainly single band [16] and multi-band [8,17]. The difference between the water bodies and non-water objects in the NIR band is the largest, and a single NIR band as the water index can be used to obtain satisfactory results for water bodies' extraction [16]. The use of multi-band water index thresholding to extract water can make use of spectral information from different bands as much as possible, but can also result in redundancy.…”

Section: Introductionmentioning

confidence: 99%

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MSLWENet: A Novel Deep Learning Network for Lake Water Body Extraction of Google Remote Sensing Images

et al. 2020

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Lake water body extraction from remote sensing images is a key technique for spatial geographic analysis. It plays an important role in the prevention of natural disasters, resource utilization, and water quality monitoring. Inspired by the recent years of research in computer vision on fully convolutional neural networks (FCN), an end-to-end trainable model named the multi-scale lake water extraction network (MSLWENet) is proposed. We use ResNet-101 with depthwise separable convolution as an encoder to obtain the high-level feature information of the input image and design a multi-scale densely connected module to expand the receptive field of feature points by different dilation rates without increasing the computation. In the decoder, the residual convolution is used to abstract the features and fuse the features at different levels, which can obtain the final lake water body extraction map. Through visual interpretation of the experimental results and the calculation of the evaluation indicators, we can see that our model extracts the water bodies of small lakes well and solves the problem of large intra-class variance and small inter-class variance in the lakes’ water bodies. The overall accuracy of our model is up to 98.53% based on the evaluation indicators. Experimental results demonstrate that the MSLWENet, which benefits from the convolutional neural network, is an excellent lake water body extraction network.

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“…Existing methods to extract surface water from optical imagery are mostly based on the fact that water has strong absorption in near infrared (NIR) and shortwave infrared (SWIR) spectral regions. Individual NIR and SWIR bands have consequently been used for water delineation (e.g., Mondejar and Tongco 2019;Wolski et al 2017). Spectral indices, combining two (e.g., Gao 1996;McFeeters 1996;Xiao et al 2002a;Xu 2006) or more multispectral bands (e.g., Crist 1985;Feyisa et al 2014;Fisher et al 2016;Wang et al 2018a) from NIR/SWIR and visible spectral regions can enhance the information on surface water presence.…”

Section: The Potential and Limitations Of Spectral Information For Momentioning

confidence: 99%

Satellite-based monitoring of surface water dynamics

Li¹

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PhD journey is truly a marathon event full of challenges. Alongside the challenges, there is also the joy of the journey itself and the many inspiring people encountered along the way. I would like to thank everyone who was involved in this journey. First and foremost, I would like to express my sincere thanks to my promoter, Prof. dr. Andrew K. Skidmore, who provided me the opportunity to undertake this PhD research at ITC and led me to the scientific world in the first place. It has been an honour to be his PhD student. He always encouraged me to keep learning new things and seeking answers to "why". He has taught me, both consciously and unconsciously, how to think critically and defend my views, and how to work smarter instead of harder. I am very grateful for his continuous guidance and support. This thesis would not have been possible without the support and guidance of my daily supervisor A/Prof. dr. Anton Vrieling. I am so lucky to have him as my supervisor for this PhD research. During all these years, he guided me with infinite patience and full support, providing useful discussions, constructive criticisms, enlightened ideas, and excellent English editing. He was always the first person helping me out whenever I got in the coming years (Prigent et al. 2012; Vörösmarty et al. 2000). Natural factors affecting water bodies include anomalous high-rainfall-driven flood events (Cian et al. 2018), drought events due to rainfall deficits (van Dijk et al. 2013), seasonal thawing and snowmelt in spring (Watts et al. 2012), and longer-term environmental changes (Lutz et al. 2014; Street and Grove 1976). Many human activities directly affect the availability of water resources. Examples are groundwater pumping, drainage of wetlands, irrigation schemes, and construction of new dams. Anthropogenic changes in land surfaces such as urbanization, agriculture and deforestation also lead to changes in surface water. These changes strongly affect ecosystem functioning, which further results in shifting species distributions and composition (Koning 2005; Robledano et al. 2010), especially for species that are sensitive to hydroperiod variability (Baldwin et al. 2006; Roshier et al. 2002). It may also affect other ecosystem functions including ground water recharge and nutrient cycling (Leibowitz 2003). Globally, the biodiversity of water-related ecosystems continues to decline at an alarming rate (Collen et al. 2014). In addition to these direct threats, the changes of surface water further influence climate change (Degu

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Near infrared band of Landsat 8 as water index: a case study around Cordova and Lapu-Lapu City, Cebu, Philippines

Cited by 59 publications

References 47 publications

Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines

Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines

MSLWENet: A Novel Deep Learning Network for Lake Water Body Extraction of Google Remote Sensing Images

Satellite-based monitoring of surface water dynamics

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