Monitoring Desertification in Biskra, Algeria Using Landsat 8 and Sentinel-1A Images

Azzouzi, Soufiane Abdelaziz; Pantaleoni, Ana Vidal; Bentounes, H.A.

doi:10.1109/access.2018.2837081

Cited by 16 publications

(6 citation statements)

References 27 publications

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“…5 To reduce the negative effects of desertification on the environment and human beings, the treatment of desertification is attracting increasing attention. 6,7 Water eutrophication is another important and widely concerning environmental issue worldwide that results in frequent algal blooms. 8−10 In summer, many aquatic cyanobacteria (AC) grow rapidly in water, causing an increase in chemical oxygen demand (COD) and a decrease in water quality.…”

Section: ■ Introductionmentioning

confidence: 99%

“…During the past several decades, because of natural factors such as climate change and human activities, including excessive logging, grazing, reclamation, and mining, desertification has become one of the most severe environmental problems worldwide. , Desertification poses serious threats to human beings, such as living area reduction, farmland degradation, and sandstorms . To reduce the negative effects of desertification on the environment and human beings, the treatment of desertification is attracting increasing attention. , …”

Section: Introductionmentioning

confidence: 99%

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Inhibiting Desertification Using Aquatic Cyanobacteria Assisted by a Nanocomposite

Zhang

et al. 2020

ACS Sustainable Chem. Eng.

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Desertification and water body eutrophication are two severe environmental problems worldwide. To alleviate these two problems simultaneously, a new idea, introducing aquatic cyanobacteria (AC) from Lake Chaohu to the surface of the Tengger Desert to encourage the growth of biological soil crusts, was proposed. A networkstructured nanocomposite (SXA) consisting of sodium polyacrylate (SP), xanthan gum (XG), and attapulgite (ATP) was also fabricated. The SXA showed high water-retention ability, viscosity, and biosafety and could provide a suitable microenvironment for AC and desert cyanobacteria (DC) growth. It was found that the combination of AC and SXA effectively increased sand nutrient levels, facilitating the growth of DC and biological soil crusts. Importantly, the resultant biological soil crusts maintained stability for at least 210 days and played a key role in stabilizing the sand surface. Therefore, this work provides a novel technology not only for desert immobilization but also for the utilization of aquatic blooms.

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Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inhibiting Desertification Using Aquatic Cyanobacteria Assisted by a Nanocomposite

Zhang

et al. 2020

ACS Sustainable Chem. Eng.

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“…Guo et al [19] presented a new method based on satellite products and a decision tree (DT) to monitor the spatiotemporal dynamics of desertification in the Ordos Plateau, China from 2000 to 2015 using TM, ETM+, and OLI remote sensing data. In [20], multi-source remote sensing data were used to monitor desertification in Biskra, Ageria. The results showed that the improved method, including radar images, provided excellent results and clearly outperformed other methods based only on optical remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the trends of water-erosion desertification on the Yunnan-Guizhou Plateau, China from 1989 to 2016 using time-series Landsat images

2020

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The Yunnan-Guizhou Plateau (YGP) is a typical ecologically fragile region in southwest China. Water-erosion desertification (WED) is one of the most significant environmental and socioeconomic issues on the YGP and has seriously restricted the socioeconomic development of this region. However, the research on monitoring of the desertification trends in this region has been limited to long time-series Landsat imagery. The objectives of this research were to monitor the WED trends on the YGP using time-series Landsat imagery data from 1989 to 2016. In this paper, we present a multi-indicator rule-based method, which was used to map the WED on the YGP during this period. The results show that the addition of multiple indicators improved the WED classification accuracy to 90.61%. Overall, the following results were obtained by using the proposed method. (1) The slight desertification area on the YGP increased from 89,617.09 km 2 in 1989 to 100,976.47 km 2 in 2016 with an annual growth ratio (AGR) of 0.48%, the moderate desertification area increased from 80,276.65 km 2 in 1989 to 90,768.39 km 2 in 2016 with an AGR of 0.50%, and the severe desertification area increased from 8149.3 km 2 in 1989 to 13,220.16 km 2 in 2016 with an AGR of 2.39%. (2) The WED expansion on the YGP can be divided into three stages. Firstly, the total WED area increased slowly from 17.80×10 4 km 2 in 1989 to 17.98×10 4 km 2 in 2010 with an AGR of 0.05%. Then, the WED rapidly expanded from 17.98×10 4 km 2 in 2010 to 20.28×10 4 km 2 in 2013 with an AGR of 4.26%. Finally, the WED increased slightly from 20.28×10 4 km 2 in 2013 to 20.50×10 4 km 2 in 2016 with an AGR of 0.36%. The total areas of the different degrees of WED decreased in 1992, 1998, 2001, and 2004. (3) The driving factors of WED were analyzed based on the Geographically Weighted Regression (GWR) model. We found that precipitation, vegetation area, and gross domestic product have key roles in the processes of desertification reversion and development. However, the regression coefficients between WED and these factors exhibited considerable spatial variations. The regression coefficients of the key driving factors showed different spatial distributions

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“…Remote sensing and LANDSAT applications [7]- [9] capture images of geographical areas, whereby object recognition is easier in HR satellite images [10]. Due to the increasing demand for HR images, there is a need to develop efficient coders for HR images on low-cost visual sensors.…”

Section: Introduction a Motivationmentioning

confidence: 99%

SMFrWF: Segmented Modified Fractional Wavelet Filter: Fast Low-Memory Discrete Wavelet Transform (DWT)

et al. 2019

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This paper proposes a novel algorithm to compute the 2-D discrete wavelet transform (DWT) of high-resolution (HR) images on low-cost visual sensor and Internet of Things (IoT) nodes. The main advantages of the proposed segmented modified fractional wavelet filter (SMFrWF) are reduced computation (time) complexity and energy consumption compared to the state-of-the-art low-memory 2-D DWT computation methods. In particular, the conventional convolution-based DWT is very fast but requires large random access memory (RAM), as the entire image needs to be in the system memory. The fractional wavelet filter (FrWF) requires only a small RAM but has high complexity due to multiple readings of image lines. The proposed SMFrWF avoids the multiple readings of image lines, thus reducing the memory read access time and, thereby, the complexity. We evaluated the proposed SMFrWF through MATLAB simulations with 70 popular gray-scale test images of dimensions ranging from 256×256 up to 8192×8192 pixels. The results show that for images of size 2048 × 2048 pixels, the proposed SMFrWF (with four segments per line) has 16.8% and 53.6% lower time complexities than the conventional DWT and FrWF, respectively. The proposed SMFrWF has also been modeled in a hardware description language (HDL) and implemented on an Artix-7 field-programmable gate array (FPGA) platform to evaluate the hardware performance. We observed that the proposed SMFrWF has 65% lower energy consumption than the FrWF (both implemented on the same board). Thus, the proposed SMFrWF appears suitable for computing the wavelet transform coefficients of HR images on low-cost visual sensors and IoT platforms.INDEX TERMS Discrete wavelet transform (DWT), low memory, low complexity, low-cost portable devices, visual sensors.

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Monitoring Desertification in Biskra, Algeria Using Landsat 8 and Sentinel-1A Images

Cited by 16 publications

References 27 publications

Inhibiting Desertification Using Aquatic Cyanobacteria Assisted by a Nanocomposite

Inhibiting Desertification Using Aquatic Cyanobacteria Assisted by a Nanocomposite

Monitoring the trends of water-erosion desertification on the Yunnan-Guizhou Plateau, China from 1989 to 2016 using time-series Landsat images

SMFrWF: Segmented Modified Fractional Wavelet Filter: Fast Low-Memory Discrete Wavelet Transform (DWT)

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