Detection of change in vegetation in the surrounding Desert areas of Northwest China and Mongolia with multi-temporal satellite images

2015 ◽  
Vol 51 (2) ◽  
pp. 173-181 ◽  
Author(s):  
Kyung-Soo Han ◽  
Youn-Young Park ◽  
Jong-Min Yeom
2019 ◽  
Vol 11 (17) ◽  
pp. 1984 ◽  
Author(s):  
Yang ◽  
Gao ◽  
Li ◽  
Jia ◽  
Jiang

The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively.


2021 ◽  
Vol 13 (9) ◽  
pp. 5274
Author(s):  
Xinyang Yu ◽  
Younggu Her ◽  
Xicun Zhu ◽  
Changhe Lu ◽  
Xuefei Li

Development of a high-accuracy method to extract arable land using effective data sources is crucial to detect and monitor arable land dynamics, servicing land protection and sustainable development. In this study, a new arable land extraction index (ALEI) based on spectral analysis was proposed, examined by ground truth data, and then applied to the Hexi Corridor in northwest China. The arable land and its change patterns during 1990–2020 were extracted and identified using 40 Landsat TM/OLI images acquired in 1990, 2000, 2010, and 2020. The results demonstrated that the proposed method can distinguish arable land areas accurately, with the User’s (Producer’s) accuracy and overall accuracy (kappa coefficient) exceeding 0.90 (0.88) and 0.89 (0.87), respectively. The mean relative error calculated using field survey data obtained in 2012 and 2020 was 0.169 and 0.191, respectively, indicating the feasibility of the ALEI method in arable land extracting. The study found that arable land area in the Hexi Corridor was 13217.58 km2 in 2020, significantly increased by 25.33% compared to that in 1990. At 10-year intervals, the arable land experienced different change patterns. The study results indicate that ALEI index is a promising tool used to effectively extract arable land in the arid area.


2011 ◽  
Vol 24 ◽  
pp. 252-256 ◽  
Author(s):  
Wei Cui ◽  
Zhenhong Jia ◽  
Xizhong Qin ◽  
Jie Yang ◽  
Yingjie Hu

2018 ◽  
Vol 21 (2) ◽  
pp. 97
Author(s):  
Nurul Latifah ◽  
Sigit Febrianto ◽  
Hadi Endrawati ◽  
Muhammad Zainuri

Mapping of Classification and Analysis of Changes in Mangrove Ecosystem Using Multi-Temporal Satellite Images in Karimunjawa, Jepara, Indonesia  Mangrove ecosystem is one of the three ecosystem in the coastal area which has important ecological role in supporting marine life and fisheries resources. These important roles include spawning ground and nursery ground for various marine organisms. However, in the last decades, mangrove ecosystem has been undergoing significant degradation. The aim of this research is to quantify the changes of mangrove coverage and density in Karimunjawa as well as key-factors leading to the changes. Supervised classification method (83% accuracy and Kappa coefficient 0.73%) was applied to satellite images to identify the temporal changes in mangrove coverage. Mangrove density was quantified using NDVI algorithm and NIR-RED wavelength. The result shows that during three periods of observed data, changes in mangrove coverage were significant: 126.81 ha increase (1992 – 2003); 82.37 ha decrease (1992 – 2017); and 209.18 ha decrease (2003 – 2017). Mangrove density in most part of Karimunjawa belongs to the category of ‘low’ (NDVI value: -1 – 0.33). Key factors contributing to the decrease mangrove coverage are deforestation, natural phenomena, land conversion into fish ponds and hotels. The only increase in the year 1992 – 2003 was caused by high sedimentation level that allows more mangroves to grow. Overall, the methods in this research could be used as an approach to describe to effectively monitor the changes of mangrove coverage in an area as basic data for sustainable environmental management. Ekosistem mangrove merupakan salah satu dari tiga ekosistem pesisir yang memiliki peranan ekologis penting dalam mendukung kehidupan dan keberlangsungan dari sumberdaya perikanan.  Hal tersebut dikarenakan fungsi mangrove sebagai tempat memijah dan asuhan bagi banyak biota air. Beberapa dekade terakhir keberadaan dari ekosisitem mangrove mengalami degradasi yang sangat signifikan. Tujuan dari penelitian ini adalah untuk mengetahui perubahan luasan dan kerapatan mangrove dan mengidentifikasi faktor penyebabnya.  Metode analisa perubahan luasan mangrove menggunakan citra satelit multi temporal dengan dilakukan pembuatan klasifikasi menggunakan metode supervised classification dengan nilai akurasi total 83% dengan Kappa koefisien 0,73%.  Setelah terseleksi antara mangrove dan non mangrove maka dilakukan perhitungan kerapatan tajuk menggunakan algoritma NDVI dengan memanfaatkan panjang gelombang NIR dan RED.  Hasil analisa spasial dengan rentang 3 tahun berbeda didapatkan perubahan penurunan dan penambahan luasan mangrove terjadi secara signifikan: tahun 1992 – 2003 telah terjadi penambahan luasan sebesar 126,81 ha; tahun 1992–2017 terjadi perubahan luasan sebesar 82,37 ha;  tahun 2003–2017 terjadi perubahan luasan sebesar 209,18 ha.  Kerapatan mangrove di Karimunjawa sebagian besar tergolong kategori kerapatan jarang dengan nilai NDVI antara -1 – 0,33. Faktor utama penyebab penurunan luasan mangrove antara lain penebangan liar, faktor alam, perubahan fungsi lahan menjadi pertambakan dan perhotelan.  Penambahan luasan mangrove terjadi pada antara tahun1992 sampai 2003 hal tersebut disebabkan sedimentasi yang menumpuk di pantai dan sudah ditumbuhi oleh mangrove.  Secara keseluruhan metode ini dapat menggambarkan perubahan secara efektif serta hasilnya dapat dipergunakan untuk pemantauan dan perencanaan ekosistem mangrove di suatu wilayah. 


2021 ◽  
pp. 407-417
Author(s):  
S. T. Khan ◽  
S. Alam ◽  
N. Azam ◽  
M. Debnath ◽  
A. K. Mojlish ◽  
...  

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