Retrieving vegetation biophysical parameters and GPP using satellite-driven LUE model in a National Park

Marandi, Moumita; Parida, Bikash Ranjan; Ghosh, Surajit

doi:10.1007/s10668-021-01815-0

Cited by 12 publications

(2 citation statements)

References 66 publications

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“…The aforementioned biophysical and biochemical attributes are strongly connected to the overall health conditions of vegetation (Marandi et al 2021;Phan et al 2020), including tea plantations and productivity. However, other environmental variables (rainfall, temperature, relative humidity, solar radiation, and soil moisture), extreme weather conditions (i.e., drought, floods, and heatwaves), and climate variability (e.g., ENSO) also determine the productivity of tea (Ahmed et al 2014;Raj et al 2020).…”

Section: Discussionmentioning

confidence: 99%

Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

Parida

Kumari

2021

Arab J Geosci

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Tea is an important cash crop, and it becomes necessary to map the spatial distribution of tea plantations. The tea industry has been expanding rapidly in the Northeast region of India, and consequently, the area under tea plantations is changing rapidly which needs periodic monitoring. In this study, satellite data such as Landsat-5 Thematic Mapper (TM) and Sentinel-2A were deployed for tea plantation identification and to analyze the dynamics of tea extent in the North Bengal district during 2000 and 2020. The result showed that the tea plantation area has increased from 1097 km 2 (2000) to 1377 km 2 (2020), depicting an increase in tea plantation area by 280 km 2 (or 25%) during the last 20 years. The highest increase in tea plantations was in Darjeeling (+137 km 2 ) and Jalpaiguri (+ 83 km 2 ) districts due to the expansion of tea estates along with the increasing population. The accuracy of tea plantation was evaluated using recall and precision with F1 score of 0.90 in 2000 and 0.92 in 2020. The tea plantation health condition was also assessed using both biophysical (LAI, EVI, FPAR) and biochemical parameters (leaf chlorophyll content). The results revealed that tea plants leaf area index (LAI) varied from 1 to 3, with healthy tea plants showed LAI > 2. Similarly, healthy tea plants depicted leaf chlorophyll content of 40 to 70 μg/cm 2 . Assessment of dynamics of tea plantation is required for promoting sustainable tea practices and safeguarding economic and social importance of people living in North Bengal.

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Section: Discussionmentioning

confidence: 99%

Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

Parida

Kumari

2021

Arab J Geosci

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“…The GOSIF GPP dataset, which was created using the linear relationship between GPP and GOSIF, is a more refined SIF product based on the Orbiting Carbon Observatory-2 (OCO-2) derivation [67]. Many studies have successfully applied the GOSIF GPP to characterize space and time changes in vegetation productivity and have shown great potential for monitoring vegetation responses to climate [43,68,69]. In this study, monthly GOSIF GPP data, which have a spatial resolution of 0.05 • , for the vegetation growing seasons (March to October in Central Asia) in the period 2000 to 2018 were used to analyze the effects of drought on vegetation GPP (https://globalecology.unh.edu/ (accessed on 22 March 2022)).…”

Section: Gpp Datamentioning

confidence: 99%

Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia

Yang,

Zou,

Ding

et al. 2023

Forests

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In the context of global warming, the strength and frequency of drought events are projected to grow in the future, and the onset of drought can have dramatic effects on vegetation growth in terrestrial ecosystems. Central Asia is the largest non-territorial drought area in the world, and the response of vegetation to drought events is extremely sensitive in the area. However, few studies have quantified and compared the vegetation gross primary productivity (GPP) response to the lagged and cumulative effects of drought. In this research, the solar-induced chlorophyll fluorescence GPP and Standardized Precipitation Evaporation Index (SPEI) were used to analyze the time and space patterns of vegetation GPP and the SPEI in Central Asia and to quantify and compare the lagged and cumulative effects of drought on the GPP of various vegetation types. During the period from 2000 to 2018, the general trends of vegetation GPP showed a slight increase in Central Asia, with the ratio of variation being 1.35 g C m−2 y−1 and a spatially decreasing distribution from north to south. SPEI showed a trend of decreasing and then increasing over a period of 19 years, with a slight decreasing (drying) trend and a rate of change of −0.02 y−1, and the overall spatial pattern was drying out from north to south. In 13 months, 72.44% of regional droughts had lagged impacts on vegetation. The maximum correlation coefficients of vegetation and the lagged effectiveness of drought were concentrated in the range of 0.15–0.35, and the high correlation was distributed in southern and northwestern Kazakhstan, which are prairie regions. Of the regions in Central Asia, 75.86% showed cumulative drought effects concentrated at 9–12 months. The maximum correlation coefficients were concentrated in the range of 0.20–0.50, and the high correlation regions were primarily situated in south Kazakhstan and east Uzbekistan. Comparing the correlation coefficients of the lagged effect of vegetation GPP and SPEI with the cumulative effect shows that the cumulative rather than lagged impacts of drought on vegetation cover were found in 86.75% of the regions in Central Asia. This research enhances our comprehension of the influence of drought events on ecosystems in arid regions and has a certain reference value for helping arid region ecosystems to cope with global climate change.

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Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data

Mahato,

Parida,

Bar

2024

Environ Monit Assess

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Retrieving vegetation biophysical parameters and GPP using satellite-driven LUE model in a National Park

Cited by 12 publications

References 66 publications

Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

Mapping tea plantations dynamics during 2000–2020 and monitoring biophysical attributes using multi-temporal satellite data in North Bengal (India)

Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia

Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data

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