Published: Dec. 15, 2023
Language: Английский
Open Geosciences, Journal Year: 2024, Volume and Issue: 16(1)
Published: Jan. 1, 2024
Abstract Normalized difference vegetation index (NDVI) and land surface temperature (LST) are important indicators of ecological changes, their spatial temporal variations coupling can provide a theoretical basis for the sustainable development environment. Based on MOD13A1 MOD11A2 datasets, distribution characteristics NDVI LST from 2000 to 2020 were analyzed, trend change slope method model used calculate significant changes. Finally, was degree between LST. The study shows that: (1) From 2020, annual value Mu Us Sandy Land 0.25 0.43, showing stable upward overall, with an increase rate 0.074/(10a). proportion improvement areas in area is 81.48%. (2) There differences Land, overall decreasing northwest southeast higher west than east. greatly affected by changes use types. spatiotemporal variation different gradual warming global climate change. main reason that human activities have changed types increased local coverage. (3) negative correlation R 2 0.5073 passing significance test at 0.01 level. This indicates engineering policies effectively reduce area, thereby achieving effect improving very high level, average 0.895 area. two mainly exhibit state mutual antagonism space, reflecting importance green regulating regional result joint influence change, dominated 2020.
Language: Английский
Citations
3
Journal of Forestry Research, Journal Year: 2024, Volume and Issue: 35(1)
Published: April 9, 2024
Language: Английский
Citations
1
Remote Sensing, Journal Year: 2023, Volume and Issue: 15(18), P. 4468 - 4468
Published: Sept. 11, 2023
Investigating vegetation changes, especially plant phenology, can yield valuable information about global warming and climate change. Time series satellite observations remote sensing methods offer a great source of on distinctions changing aspects vegetation. The current study aimed to determine the trend rate changes in some phenological components Iran’s In this regard, employed daily NDVI (Normalized Difference Vegetation Index) product AVHRR sensor with spatial resolution 0.05° × 0.05°, named AVH13C1. Then, using HANTS algorithm, images amplitude zero, annual amplitude, phase were prepared annually from 1982 2019. Using TIMESAT software, starting, end, length time growing season calculated for each pixel prepare maps. Mann–Kendall statistical test was used investigate significance during period. On average entire area Iran, declining −0.6° per year, start end by −0.3 −0.65 days respectively. Major noticed northeast, west, northwest regions where declined −0.9° year. Since growth cycle (equivalent 356 days) form sinusoidal signal, angular sine wave between zero 360°, degree change equivalent 1.01 Therefore, reduction −0.9 degrees almost means (due earlier negative phase) signal −0.6 −1.33 differences starting 2019 indicate acceleration initiation various processes area.
Language: Английский
Citations
1
Remote Sensing, Journal Year: 2024, Volume and Issue: 16(23), P. 4509 - 4509
Published: Dec. 1, 2024
The Arctic, characterised by severe climatic conditions and sparse vegetation, is experiencing rapid warming, with temperatures increasing up to four times the global rate since 1979. Extensive impacts from these changes have far-reaching consequences for climate energy balance. Satellite remote sensing a valuable tool monitoring Arctic vegetation dynamics, particularly in regions limited ground observations. To investigate ongoing impact of change on sub-Arctic review 162 studies published between 2000 November 2024 was conducted. This analyses research objectives, spatial distribution study areas, methods, temporal resolution utilised satellite data. key findings reveal circumpolar tendencies, including greening, lichen decline, shrub increase, positive primary productivity trends. These carbon balance tundra affect specialised fauna local communities. A large majority conducted their analysis based multispectral data, primarily using AVHRR, MODIS, Landsat sensors. Although warming linked greening trends, increased productivity, expansion, diverse localised ecological shifts are influenced multitude complex factors. Furthermore, can be challenging observe due difficult cloud cover illumination when acquiring optical Additionally, difficulty validating compounded scarcity situ fusion data different spatial–temporal characteristics sensor types, combined methodological advancements, may help mitigate gaps. crucial assessing Arctic’s potential role as future source or sink.
Language: Английский
Citations
0
Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 3 - 13
Published: Jan. 1, 2024
Language: Английский
Citations
0
International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 136, P. 104341 - 104341
Published: Dec. 25, 2024
Language: Английский
Citations
0
Published: Dec. 15, 2023
Language: Английский
Citations
0