Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images

Luo, Yanmin; Ouyang, Yi; Zhang, Rencheng; Feng, Hao

doi:10.3390/ijgi6060177

Cited by 16 publications

(10 citation statements)

References 24 publications

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“…There are several different methods of imagery data classification; however, unsupervised and supervised techniques are the two main approaches commonly used for mapping land cover [41]. Most researchers prefer using the supervised technique because it produces more accurate classification than the unsupervised technique [42][43][44].…”

Section: Image Classificationmentioning

confidence: 99%

Detecting and Analyzing Land Use and Land Cover Changes in the Region of Al-Jabal Al-Akhdar, Libya Using Time-Series Landsat Data from 1985 to 2017

et al. 2020

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The region of Al-Jabal Al-Akhdar in northeastern Libya has undergone rapid, wide-ranging changes in the land use and land cover (LULC) intensified by the conversion of natural resources for food purpose, urbanization, and other socioeconomic benefits. This study examined the use of geographic information system (GIS) and remote sensing techniques to gain a quantitative understanding of the spatiotemporal dynamics of LULC. In addition, the major factors behind LULC changes and decline of natural vegetation in the region were analyzed. A post-classification comparison approach was used to detect LULC changes in the study area between 1985 and 2017 using four Landsat images from 1985, 2000, 2010, and 2017. The observed changes were indicative of a decrease in the expanse of the natural Mediterranean forest which lost 9018 ha over 32 years, 39% of its total area, with the highest deforestation rate registered between 2010 and 2017 estimated at 513 ha. year−1. Orchards and rain-fed agriculture lands gained 4095 ha, which matches 55% of initial area, whereas the land under irrigated crops increased by 2266 ha, about 85% of the original area. The area of urban and built-up land in 2017 was more than double in 1985 and achieved the highest urbanization rate between 2010 and 2017 at 203 ha.year−1. Results indicate an unstable trend of bare and low vegetation lands which generally increased by about 50%. From the outcomes of this research, it is strongly recommended that urgent measures be taken to conserve the natural forest and to achieve a rational use of agricultural land in the region of Al-Jabal Al-Akhdar.

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Section: Image Classificationmentioning

confidence: 99%

Detecting and Analyzing Land Use and Land Cover Changes in the Region of Al-Jabal Al-Akhdar, Libya Using Time-Series Landsat Data from 1985 to 2017

et al. 2020

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“…Built-up areas contain urban settlements, roads, industries, and others infrastructures. Mangroves are identified by spectral values which are similar to plantations, but they are located near beach or river estuaries [24,25]. The multi-temporal LULC information would be analyzed for changes and comparing using the overlay method.…”

Section: Data For Lulc and Driversmentioning

confidence: 99%

Spatial dynamics model of land use and land cover changes: A comparison of CA, ANN, and ANN-CA

Dede

Asdak

Setiawan

2021

regist. j. ilm. teknol. sist. inf.

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Land use and land cover (LULC) changes through built-up area expansion always increases linearly with land demand as a consequence of population growth and urbanization. Cirebon City is a center for Ciayumajakuning Region that continues to grow and exceeds its administrative boundaries. This phenomenon has led to peri-urban regions which show urban and rural interactions. This study aims to analyze (1) the dynamics of LULC changes using cellular automata (CA), artificial neural network (ANN), and ANN-CA; (2) the influential factors (drivers); and (3) change probability in the period 2030 and 2045 for Cirebon’s peri-urban. We used logistic regression as quantitative approach to analyze the interaction of drivers and LULC changes. The LULC data derived from Landsat series satellite imagery in 1999-2009 and 2009-2019, validation of dynamic spatial model refers to 100 LULC samples. This research shows that LULC changes are dominated by built-up area expansion which causes plantations and agricultural land to decrease. The drivers have a simultaneous effect on LULC changes with r-square of 0.43, where land slope, distance from existing built-up area, distance from CBD, and accessibility are significant triggers. LULC simulation of CA algorithm is the best model than ANN and ANN-CA based on overall accuracy and overall accuracy (0.96, 0.75, 0.73 and 0.95, 0.66, 0.66 respectively), it reveals urban sprawl through the ribbon and compact development. The average probability of built-up area expansion is 0.18 (2030) and 0.19 (2045). If there is no intervention in spatial planning, this phenomenon will decrease productive agricultural lands in Cirebon's peri-urban.

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“…The first research direction is the accurate extraction of mangrove distribution assisted by remote sensing technology. The traditional method is based on the idea of threshold; a mangrove index is established using Sentinel-2 image to achieve accurate extraction of submerged mangroves in view of the fact that mangroves will be flooded by tidal water, rainy climate, and other adverse factors [9,10]. Additionally, the maximum likelihood classification technology of remote sensing images is introduced to accurately distinguish mangroves [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data

Chen¹,

Yang

Zhang

et al. 2023

Forests

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Mangrove is the key vegetation in the transitional zone between land and sea, and its health assessment can indicate the deep-level ecological information. The LAI and six key nutrients of mangrove were selected as quantitative evaluation indicators, and the decisive evaluation method of mangrove growth was expected. The mangrove reserve of Dongzhai Port National Nature Reserve in Hainan Province, China, was selected as the study area, with an area of 17.71 km2. The study area was divided into adjacent urban areas, aquaculture areas, and agricultural production areas, and key indicators are extracted from satellite hyperspectral data. The extraction process includes spectral data preprocessing, spectral transformation, spectral combination, spectral modeling, and precision inspection. The spatial distribution of LAI and six key nutrient components of mangrove in the study area were obtained. LAI and Chla need to calculate the index after high-order differentiation of the spectrum; MSTR and Chlb need to calculate the envelope after the second-order differential of the spectrum; TN and TP are directly changed by original or exponential spectrum; the spectral transformation method adopted by TK was homogenization after first-order differential. The results of the strength of nutrient content along the three regions show that there was no significant difference in the retrieval index of mangroves in the three regions, and the overall health level of mangroves was consistent. Chla was the key identification component of mangrove growth and health. The contents of nutrient elements with correlation coefficient exceeding 0.80 include MSTR and TK (0.98), Chla and TP (0.96), Chla and TK (0.87), MSTR and Chla (0.86), MSTR and TK (0.83), and MSTR and TP (0.81). The study quantifies the relationship between different LAI and nutrient content of mangrove leaves from the perspectives of water, leaf biology, and chemical elements, which improved our understanding of the relationship between key components during mangrove growth for the first time.

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Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images

Cited by 16 publications

References 24 publications

Detecting and Analyzing Land Use and Land Cover Changes in the Region of Al-Jabal Al-Akhdar, Libya Using Time-Series Landsat Data from 1985 to 2017

Detecting and Analyzing Land Use and Land Cover Changes in the Region of Al-Jabal Al-Akhdar, Libya Using Time-Series Landsat Data from 1985 to 2017

Spatial dynamics model of land use and land cover changes: A comparison of CA, ANN, and ANN-CA

Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data

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