JOURNAL OF FORESTRY RESEARCH ›› 2023, Vol. 34 ›› Issue (6): 1855-1867.DOI: 10.1007/s11676-023-01639-w
• Original Paper • Previous Articles Next Articles
José Luis Gallardo-Salazar1, Marcela Rosas-Chavoya2,b, Marín Pompa-García3, Pablito Marcelo López-Serrano4, Emily García-Montiel3, Arnulfo Meléndez-Soto3, Sergio Iván Jiménez-Jiménez5
Received:
2022-07-04
Accepted:
2022-09-03
Online:
2024-10-16
Contact:
Marcela Rosas-Chavoya
José Luis Gallardo-Salazar, Marcela Rosas-Chavoya, Marín Pompa-García, Pablito Marcelo López-Serrano, Emily García-Montiel, Arnulfo Meléndez-Soto, Sergio Iván Jiménez-Jiménez. Multi-temporal NDVI analysis using UAV images of tree crowns in a northern Mexican pine-oak forest[J]. JOURNAL OF FORESTRY RESEARCH, 2023, 34(6): 1855-1867.
José Luis Gallardo-Salazar, Marcela Rosas-Chavoya, Marín Pompa-García, Pablito Marcelo López-Serrano, Emily García-Montiel, Arnulfo Meléndez-Soto, Sergio Iván Jiménez-Jiménez. [J]. 林业研究(英文版), 2023, 34(6): 1855-1867.
1 |
Abdalla A, Elmahal A (2016) Augmentation of vertical accuracy of digital elevation models using Gaussian linear convolution filter. In: 2016 Conference of Basic Sciences and Engineering Studies SGCAC. Khartoum, Sudan, pp 206–210
|
2 |
DOI |
3 |
DOI |
4 |
DOI |
5 |
DOI |
6 |
DOI |
7 |
DOI |
8 |
DOI |
9 |
DOI |
10 |
DOI |
11 |
DOI |
12 |
DOI |
13 |
DOI |
14 |
Choudhry H, O’Kelly G (2018) Precision forestry: a revolution in the woods. https://www.mckinsey.com/industries/paper-forest-products-and-packaging/our-insights/precision-forestry-a-revolution-in-the-woods# /. [Accessed on 06.01.2021]
|
15 |
DOI |
16 |
|
17 |
DOI |
18 |
DOI |
19 |
DOI |
20 |
DOI |
21 |
DOI |
22 |
DOI |
23 |
DOI |
24 |
DOI |
25 |
DOI |
26 |
DOI |
27 |
DOI |
28 |
DOI |
29 |
DOI |
30 |
DOI |
31 |
DOI |
32 |
DOI |
33 |
DOI |
34 |
DOI |
35 |
DOI |
36 |
DOI |
37 |
DOI |
38 |
DOI |
39 |
DOI |
40 |
DOI |
41 |
DOI |
42 |
DOI |
43 |
DOI |
44 |
DOI |
45 |
DOI |
46 |
DOI |
47 |
|
48 |
DOI |
49 |
Mitchell JJ, Glenn NF, Anderson MO, Hruska RC, Halford A, Baun C, Nydegger N (2012) Unmanned aerial vehicle (UAV) hyperspectral remote sensing for dryland vegetation monitoring. In: 2012 4th workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS). pp 1–10
|
50 |
DOI |
51 |
DOI |
52 |
Open Drone Map (2021) Awesome. Drone. Software. https://opendronemap.org/. [Accessed on 02.08.2021]
|
53 |
DOI |
54 |
Parrot (2021) Support−Parrot Sequoia. https://support.parrot.com/us/support/products/parrot-sequoia. [Accessed on 06.01.2021]
|
55 |
DOI |
56 |
DOI |
57 |
DOI |
58 |
Plowright, A, Roussel J (2020) ForestTools: analyzing remotely sensed forest data. R package version 0.2.5. https://cran.r-project.org/web/packages/ForestTools/index.html. [Accessed on 06.01.2021]
|
59 |
DOI |
60 |
DOI |
61 |
DOI |
62 |
DOI |
63 |
QGIS Core Team (2021) A free open source. Geogr. Inf. Syst. https://qgis.org/. [Accessed on 06.01.2021]
|
64 |
DOI |
65 |
R Core Team (2021) The R project for statistical computing. https://www.r-project.org/. [Accessed on 06.01.2021]
|
66 |
DOI |
67 |
DOI |
68 |
DOI |
69 |
DOI |
70 |
DOI |
71 |
Stackhouse P, Westberg D, Hoell J, Chandler WS, Zhang T (2015) Surface meteorology and Solar Energy (SSE) Release 6.0 Methodology Version 3.2.0 June 2, 2016. https://dokumen.tips/download/link/sse-release-60-methodology.html. [Accessed on 06.01.2021]
|
72 |
DOI |
73 |
DOI |
74 |
|
75 |
DOI |
76 |
DOI |
77 |
Zhang J, You S, Gruenwald L (2015) Efficient parallel zonal statistics on large-scale global biodiversity data on GPUs. In: BigSpatial’15: Proceedings of the 4th international ACM SIGSPATIAL WORKSHOP on analytics for big geospatial data. pp 35–44
|
[1] | Kobra Shojaeizadeh, Mahmoud Ahmadi, Abbasali Dadashi-Roudbari. Contribution of biophysical and climate variables to the spatial distribution of wildfires in Iran [J]. JOURNAL OF FORESTRY RESEARCH, 2023, 34(6): 1763-1775. |
[2] | Siyuan Chen, Liangyun Liu, Lichun Sui, Xinjie Liu. Improving GPP estimates by partitioning green APAR from total APAR in two deciduous forest sites [J]. JOURNAL OF FORESTRY RESEARCH, 2023, 34(4): 915-927. |
[3] | Fabrina Bolzan Martins, Mábele de Cássia Ferreira, Flávia Fernanda Azevedo Fagundes, Gabriel Wilson Lorena Florêncio. Thermal and photoperiodic requirements of the seedling stage of three tropical forest species [J]. JOURNAL OF FORESTRY RESEARCH, 2023, 34(1): 209-220. |
[4] | Marín Pompa-García, J. Julio Camarero, Michele Colangelo. Different xylogenesis responses to atmospheric water demand contribute to species coexistence in a mixed pine–oak forest [J]. JOURNAL OF FORESTRY RESEARCH, 2023, 34(1): 51-62. |
[5] | Yanbo Hu, Raul Antonio Sperotto, Georgios Koubouris, Srđan Stojnić, Nacer Bellaloui. Tree ecophysiology in the context of climate change [J]. JOURNAL OF FORESTRY RESEARCH, 2023, 34(1): 1-5. |
[6] | Tetsuto Sugai, Wataru Ishizuka, Toshihiro Watanabe. Landscape gradient of autumn photosynthetic decline in Abies sachalinensis seedlings [J]. JOURNAL OF FORESTRY RESEARCH, 2023, 34(1): 187-195. |
[7] | Rafaela Lanças Gomes, Marília Caixeta Sousa, Felipe Girotto Campos, Carmen Sílvia Fernandes Boaro, José Raimundo de Souza Passos, Gisela Ferreira. Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration [J]. JOURNAL OF FORESTRY RESEARCH, 2023, 34(1): 269-282. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||