Zobrazeno 1 - 10
of 183
pro vyhledávání: '"multi-temporal remote sensing"'
Autor:
Endre Hansen, Julius Wold, Michele Dalponte, Terje Gobakken, Lennart Noordermeer, Hans Ole Ørka
Publikováno v:
European Journal of Remote Sensing, Vol 56, Iss 1 (2023)
ABSTRACTRot in commercial timber reduces the value of the wood substantially and estimating the occurrence, severity, and volume of heartwood rot would be a useful tool in decision-making to minimize economic losses. Remotely sensed data has recently
Externí odkaz:
https://doaj.org/article/3910cab07e294716a850e73afb4bca4d
Autor:
Zhangxin Liu, Haoran Ju, Qiyun Ma, Chengming Sun, Yuping Lv, Kaihua Liu, Tianao Wu, Minghan Cheng
Publikováno v:
Agriculture, Vol 14, Iss 4, p 638 (2024)
Effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. However, traditional ground-based measurement techniques suffer from
Externí odkaz:
https://doaj.org/article/176366cf72d94683aff5a825bea1626d
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 6, Pp 3343-3355 (2022)
The ubiquitous deep learning (DL) in remote sensing (RS) motivates the most challenging problem of crop classification. To perpetrate such an exigent task, an attempt is made to prepare a novel dataset, the CaneSat dataset, in two formats: RGB color
Externí odkaz:
https://doaj.org/article/f38cb355a40745839e755bd57f706f83
Publikováno v:
Remote Sensing, Vol 15, Iss 13, p 3285 (2023)
The timely and accurate mapping of crops over large areas is essential for alleviating food crises and formulating agricultural policies. However, most existing classical crop mapping methods usually require the whole-year historical time-series data
Externí odkaz:
https://doaj.org/article/0c41805651444fe9a006ecb0050ac443
Autor:
Haiyi Ma, Changkun Wang, Jie Liu, Xinyi Wang, Fangfang Zhang, Ziran Yuan, Chengshuo Yao, Xianzhang Pan
Publikováno v:
Remote Sensing, Vol 15, Iss 12, p 3191 (2023)
Soil organic matter (SOM) is an important soil property for agricultural production. Rising grain demand has increased the intensity of cultivated land development in the Sanjiang Plain of China, and there is a strong demand for SOM monitoring in thi
Externí odkaz:
https://doaj.org/article/910880c12e9842bcb1cff299944fa090
Publikováno v:
Remote Sensing, Vol 15, Iss 5, p 1230 (2023)
Clouds often contaminate remote sensing images, which leads to missing land feature information and subsequent application degradation. Low-rank tensor completion has shown great potential in the reconstruction of multi-temporal remote sensing images
Externí odkaz:
https://doaj.org/article/91870a1e7cd24c9dae243ae74b003826
Akademický článek
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Publikováno v:
Remote Sensing, Vol 14, Iss 22, p 5803 (2022)
There is increasing demand for more detailed soil maps to support fine-scale land use planning, soil carbon management, and precision agriculture in Saskatchewan. Predictive soil mapping that incorporates a combination of environmental covariates pro
Externí odkaz:
https://doaj.org/article/c16053dd7bf24587a84970711615c3f2
Publikováno v:
Revista de la Facultad de Ciencias Agrarias, Vol 52, Iss 1 (2020)
Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current stat
Externí odkaz:
https://doaj.org/article/b87614a6f2074e60956736c322630ca4
Publikováno v:
Remote Sensing, Vol 14, Iss 3, p 573 (2022)
For multi-temporal high resolution remote sensing images, the image registration is important but difficult due to the high resolution and low-stability land-cover. Especially, the changing of land-cover, solar altitude angle, radiation intensity, an
Externí odkaz:
https://doaj.org/article/71b734f07ded4b929745f3e4164d87dc