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Resilience of the central indian forest ecosystem to rainfall variability in the context of a changing climate

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Resilience of the central indian forest ecosystem to rainfall variability in the context of a changing climate. / Singh, B.; Jeganathan, C.; Rathore, V.S.; Behera, M.D.; Singh, C.P.; Roy, P.S.; Atkinson, P.M.

In: Remote Sensing, Vol. 13, No. 21, 4474, 08.11.2021.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Singh, B, Jeganathan, C, Rathore, VS, Behera, MD, Singh, CP, Roy, PS & Atkinson, PM 2021, 'Resilience of the central indian forest ecosystem to rainfall variability in the context of a changing climate', Remote Sensing, vol. 13, no. 21, 4474. https://doi.org/10.3390/rs13214474

APA

Singh, B., Jeganathan, C., Rathore, V. S., Behera, M. D., Singh, C. P., Roy, P. S., & Atkinson, P. M. (2021). Resilience of the central indian forest ecosystem to rainfall variability in the context of a changing climate. Remote Sensing, 13(21), [4474]. https://doi.org/10.3390/rs13214474

Vancouver

Singh B, Jeganathan C, Rathore VS, Behera MD, Singh CP, Roy PS et al. Resilience of the central indian forest ecosystem to rainfall variability in the context of a changing climate. Remote Sensing. 2021 Nov 8;13(21). 4474. https://doi.org/10.3390/rs13214474

Author

Singh, B. ; Jeganathan, C. ; Rathore, V.S. ; Behera, M.D. ; Singh, C.P. ; Roy, P.S. ; Atkinson, P.M. / Resilience of the central indian forest ecosystem to rainfall variability in the context of a changing climate. In: Remote Sensing. 2021 ; Vol. 13, No. 21.

Bibtex

@article{8fa86b6dbb5f4cdea27d8adf858a69a2,
title = "Resilience of the central indian forest ecosystem to rainfall variability in the context of a changing climate",
abstract = "Understanding the spatio-temporal pattern of natural vegetation helps decoding the responses to climate change and interpretation on forest resilience. Satellite remote sensing based data products, by virtue of their synoptic and repetitive coverage, offer to study the correlation and lag effects of rainfall on forest growth in a relatively longer time scale. We selected central India as the study site. It accommodates tropical natural vegetation of varied forest types such as moist and dry deciduous and evergreen and semi-evergreen forests that largely depend on the southwest monsoon. We used the MODIS derived NDVI and CHIRPS based rainfall datasets from 2001 to 2018 in order to analyze NDVI and rainfall trend by using Sen{\textquoteright}s slope and standard anomalies. The study observed a decreasing rainfall trend over 41% of the forests, while the rest of the forest area (59%) demonstrated an increase in rainfall. Furthermore, the study estimated drought conditions during 2002, 2004, 2009, 2014 and 2015 for 98.2%, 92.8%, 89.6%, 90.1% and 95.8% of the forest area, respectively; and surplus rainfall during 2003, 2005, 2007, 2011, 2013 and 2016 for 69.5%, 63.9%, 71.97%, 70.35%, 94.79% and 69.86% of the forest area, respectively. Hence, in the extreme dry year (2002), 93% of the forest area showed a negative anomaly, while in the extreme wet year (2013), 89% of forest cover demonstrated a positive anomaly in central India. The long-term vegetation trend analysis revealed that most of the forested area (>80%) has a greening trend in central India. When we considered annual mean NDVI, the greening and browning trends were observed over at 88.65% and 11.35% of the forested area at 250 m resolution and over 93.01% and 6.99% of the area at 5 km resolution. When we considered the peak-growth period mean NDVI, the greening and browning trends were as follows: 81.97% and 18.03% at 250 m and 88.90% and 11.10% at 5 km, respectively. The relative variability in rainfall and vegetation growth at five yearly epochs revealed that the first epoch (2001–2005) was the driest, while the third epoch (2011–2015) was the wettest, corresponding to the lowest vegetation vigour in the first epoch and the highest in the third epoch during the past two decades. The study reaffirms that rainfall is the key climate variable in the tropics regulating the growth of natural vegetation, and the central Indian forests are dominantly resilient to rainfall variation. ",
keywords = "Anomalies, India, NDVI, Rainfall, Resilient, Tropical forest, Vegetation, Climate change, Ecosystems, Forestry, Remote sensing, Tropics, 'Dry' [, Anomaly, Central India, Forest area, Natural vegetation, Rainfall trends, Rain",
author = "B. Singh and C. Jeganathan and V.S. Rathore and M.D. Behera and C.P. Singh and P.S. Roy and P.M. Atkinson",
year = "2021",
month = nov,
day = "8",
doi = "10.3390/rs13214474",
language = "English",
volume = "13",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "MDPI AG",
number = "21",

}

RIS

TY - JOUR

T1 - Resilience of the central indian forest ecosystem to rainfall variability in the context of a changing climate

AU - Singh, B.

AU - Jeganathan, C.

AU - Rathore, V.S.

AU - Behera, M.D.

AU - Singh, C.P.

AU - Roy, P.S.

AU - Atkinson, P.M.

PY - 2021/11/8

Y1 - 2021/11/8

N2 - Understanding the spatio-temporal pattern of natural vegetation helps decoding the responses to climate change and interpretation on forest resilience. Satellite remote sensing based data products, by virtue of their synoptic and repetitive coverage, offer to study the correlation and lag effects of rainfall on forest growth in a relatively longer time scale. We selected central India as the study site. It accommodates tropical natural vegetation of varied forest types such as moist and dry deciduous and evergreen and semi-evergreen forests that largely depend on the southwest monsoon. We used the MODIS derived NDVI and CHIRPS based rainfall datasets from 2001 to 2018 in order to analyze NDVI and rainfall trend by using Sen’s slope and standard anomalies. The study observed a decreasing rainfall trend over 41% of the forests, while the rest of the forest area (59%) demonstrated an increase in rainfall. Furthermore, the study estimated drought conditions during 2002, 2004, 2009, 2014 and 2015 for 98.2%, 92.8%, 89.6%, 90.1% and 95.8% of the forest area, respectively; and surplus rainfall during 2003, 2005, 2007, 2011, 2013 and 2016 for 69.5%, 63.9%, 71.97%, 70.35%, 94.79% and 69.86% of the forest area, respectively. Hence, in the extreme dry year (2002), 93% of the forest area showed a negative anomaly, while in the extreme wet year (2013), 89% of forest cover demonstrated a positive anomaly in central India. The long-term vegetation trend analysis revealed that most of the forested area (>80%) has a greening trend in central India. When we considered annual mean NDVI, the greening and browning trends were observed over at 88.65% and 11.35% of the forested area at 250 m resolution and over 93.01% and 6.99% of the area at 5 km resolution. When we considered the peak-growth period mean NDVI, the greening and browning trends were as follows: 81.97% and 18.03% at 250 m and 88.90% and 11.10% at 5 km, respectively. The relative variability in rainfall and vegetation growth at five yearly epochs revealed that the first epoch (2001–2005) was the driest, while the third epoch (2011–2015) was the wettest, corresponding to the lowest vegetation vigour in the first epoch and the highest in the third epoch during the past two decades. The study reaffirms that rainfall is the key climate variable in the tropics regulating the growth of natural vegetation, and the central Indian forests are dominantly resilient to rainfall variation. 

AB - Understanding the spatio-temporal pattern of natural vegetation helps decoding the responses to climate change and interpretation on forest resilience. Satellite remote sensing based data products, by virtue of their synoptic and repetitive coverage, offer to study the correlation and lag effects of rainfall on forest growth in a relatively longer time scale. We selected central India as the study site. It accommodates tropical natural vegetation of varied forest types such as moist and dry deciduous and evergreen and semi-evergreen forests that largely depend on the southwest monsoon. We used the MODIS derived NDVI and CHIRPS based rainfall datasets from 2001 to 2018 in order to analyze NDVI and rainfall trend by using Sen’s slope and standard anomalies. The study observed a decreasing rainfall trend over 41% of the forests, while the rest of the forest area (59%) demonstrated an increase in rainfall. Furthermore, the study estimated drought conditions during 2002, 2004, 2009, 2014 and 2015 for 98.2%, 92.8%, 89.6%, 90.1% and 95.8% of the forest area, respectively; and surplus rainfall during 2003, 2005, 2007, 2011, 2013 and 2016 for 69.5%, 63.9%, 71.97%, 70.35%, 94.79% and 69.86% of the forest area, respectively. Hence, in the extreme dry year (2002), 93% of the forest area showed a negative anomaly, while in the extreme wet year (2013), 89% of forest cover demonstrated a positive anomaly in central India. The long-term vegetation trend analysis revealed that most of the forested area (>80%) has a greening trend in central India. When we considered annual mean NDVI, the greening and browning trends were observed over at 88.65% and 11.35% of the forested area at 250 m resolution and over 93.01% and 6.99% of the area at 5 km resolution. When we considered the peak-growth period mean NDVI, the greening and browning trends were as follows: 81.97% and 18.03% at 250 m and 88.90% and 11.10% at 5 km, respectively. The relative variability in rainfall and vegetation growth at five yearly epochs revealed that the first epoch (2001–2005) was the driest, while the third epoch (2011–2015) was the wettest, corresponding to the lowest vegetation vigour in the first epoch and the highest in the third epoch during the past two decades. The study reaffirms that rainfall is the key climate variable in the tropics regulating the growth of natural vegetation, and the central Indian forests are dominantly resilient to rainfall variation. 

KW - Anomalies

KW - India

KW - NDVI

KW - Rainfall

KW - Resilient

KW - Tropical forest

KW - Vegetation

KW - Climate change

KW - Ecosystems

KW - Forestry

KW - Remote sensing

KW - Tropics

KW - 'Dry' [

KW - Anomaly

KW - Central India

KW - Forest area

KW - Natural vegetation

KW - Rainfall trends

KW - Rain

U2 - 10.3390/rs13214474

DO - 10.3390/rs13214474

M3 - Journal article

VL - 13

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 21

M1 - 4474

ER -