Satellite remote sensing analysis to monitor revegetation in the Yangtze River basin, China

Wang, Yunchen; Wang, Wei; Wang, Chao; Lin, Aiming

doi:10.1002/ldr.4129

Cited by 3 publications

(2 citation statements)

References 40 publications

(39 reference statements)

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“…To obtain an annual consistent NDVI data with high spatiotemporal resolution from 1982 to 2011, we downscaled the coarse‐resolution (8000 m × 8000 m) to medium spatial resolution (250 m × 250 m) (hereafter, NDVI‐ESTARFM) using ESTARFM algorithm (Zhu et al, 2010). It has been validated by the example of Yangtze River basin (Li, Wang, et al, 2021), which showed that the NDVI‐ESTARFM datasets calculated by this method are consistent with the actual NDVI data. The equation is expressed as follows:

(Fx, y, t_{p}, B) = (Fx, y, t_{0}, B) + v (x, y) \times [C (x, y, t_{p}, B) - C (x, y, t_{0}, B)]

Where :

F

and

C

represent the fine‐resolution reflectance at base date

t_{0}

for band

B

and coarse‐ resolution reflectance at prediction data

t_{p}

for band

B

, respectively, (

x, y

) is the location for both fine‐ and coarse‐resolution images,

t_{0}

and

t_{p}

is the acquisition date,

v (x, y)

stands for the conversion coefficient by linearly regressing the reflectance changes of fine‐resolution pixels of the same endmember and coarse‐ resolution pixel.…”

Section: Methodsmentioning

confidence: 75%

“…The equation is expressed as follows:

(Fx, y, t_{p}, B) = (Fx, y, t_{0}, B) + v (x, y) \times [C (x, y, t_{p}, B) - C (x, y, t_{0}, B)]

Where :

F

and

C

represent the fine‐resolution reflectance at base date

t_{0}

for band

B

and coarse‐ resolution reflectance at prediction data

t_{p}

for band

B

, respectively, (

x, y

) is the location for both fine‐ and coarse‐resolution images,

t_{0}

and

t_{p}

is the acquisition date,

v (x, y)

stands for the conversion coefficient by linearly regressing the reflectance changes of fine‐resolution pixels of the same endmember and coarse‐ resolution pixel. Details of the derivation of ESTARFM can be found in Zhu et al (2010) and Li, Wang, et al (2021).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Dynamics arising from the impact of large‐scale afforestation on ecosystem services

Wang

2022

Land Degrad Dev

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Understanding how multiple ecosystem services interact, how trade-offs emerge, and how interactions among ecosystem services may change after the Grain-for-Green-Program (GFGP) and is critical for managing multiple ecosystem services.Here, we conducted a spatiotemporal approach to quantify the impact of large-scale afforestation on four key ecosystem services (i.e., crop production, soil conservation, carbon sequestration, and water conservation). We observed that three satellite datasets (i.e., GIMMS-LAI3g, GIMMS-NDVI3g, and NDVI-ESTARFM) time-series show a slightly improved and significantly improved trends, and crop production, carbon sequestration, soil conservation, and water conservation have improved after the implementation of GFGP. Simultaneously, almost 49% of the low levels and 58% of the high levels for all four of the ecosystem services considered changed after the implementation of GFGP. The results also show that relationships among ecosystem services have shifted from a trade-off in 1988 and 1999 to an apparent synergistic relationship in 2011.We conclude that GFGP is a successful programme for addressing trade-offs between services. Our study provides experiential support for largescale restoration programme in humid area around the World.

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(Fx, y, t_{p}, B) = (Fx, y, t_{0}, B) + v (x, y) \times [C (x, y, t_{p}, B) - C (x, y, t_{0}, B)]

Where :

F

and

C

represent the fine‐resolution reflectance at base date

t_{0}

for band

B

and coarse‐ resolution reflectance at prediction data

t_{p}

for band

B

, respectively, (

x, y

) is the location for both fine‐ and coarse‐resolution images,

t_{0}

and

t_{p}

is the acquisition date,

v (x, y)

stands for the conversion coefficient by linearly regressing the reflectance changes of fine‐resolution pixels of the same endmember and coarse‐ resolution pixel.…”

Section: Methodsmentioning

confidence: 75%

“…The equation is expressed as follows: