Assessing Impacts of Climate Change on Teak and Sal Landscape Using Modis Time Series Data

Abstract

<p><strong>Abstract.</strong> Climate change poses a severe threat to the forest ecosystems by impacting its productivity, species composition and forest biodiversity at global and regional level. The scientific community all over the world is using remote sensing techniques to monitor and assess the impact of climate change on forest ecosystems. The consistent time series data provided by MODIS is immensely used for developing a different type of Vegetation indices like NDVI (Normalized difference vegetation indices) products at different spatial and temporal resolution. These vegetation indices have significant potential to detect forest growth and health, vegetation seasonality and different phenological events like budding and flowering. The current study aims to understand the impact of climate change on Teak and Sal forest of STR (Satpura tiger reserve) in central India by using Landsat and MODIS time series data. The rationale for taking STR as study site was to attribute the changes exclusively to climate change as there is no anthropogenic disturbance in STR. A change detection analysis was carried out to detect changes between the period 2017 and 1990 using Landsat data of October month. To understand the inter-annual and seasonal variation of Teak and Sal forests, freely available MOD13Q1 product (250<span class="thinspace"></span>m, 16 days’ interval) was used to extract NDVI values for each month and four seasons (DJF, JJAS, ON, MAM) for the period 2000 to 2015. The climatic data (rainfall and temperature) was sourced from IMD (India Meteorological Department) at different resolutions (1, 0.5 and 0.25 degree) for the given period of the study. A correlation analysis was done to establish a causal relationship between climate variable (temperature and rainfall) and vegetation health (NDVI) on a different temporal scale of annual, seasonal and month. The study found an increasing trend in annual mean temperature and no consistent trend in total annual rainfall over the period 2000 to 2015. The maximum percentage change was observed in minimum temperature over the period 2000 to 2015. The average annual NDVI of Teak and Sal forests showed an increasing trend however, no trend was observed in seasonal and monthly NDVI over the same period. The maximum and minimum NDVI was found in the post-monsoon months (ON) and summer months (MAM) respectively. As STR is a Teak and Sal dominated landscape, the findings of the current study can also be applied in developing silvicultural and adaptation strategies for other Teak and Sal dominated landscapes of central India.</p>

Multidecadal Trend Analysis of Armenian Mountainous Grassland and Its Relationship to Climate Change Using Multi-Sensor NDVI Time-Series

“…The response of NDVI to climate factors may differ according to vegetation type and growth phase [157] with obvious time-lag effects [13,22,104]. In some regions, NDVI variation is due to variation in precipitation [25,66,102,[158][159][160]; in other regions, temperature is a major influencing factor in the variation of NDVI [34,46,92,93]. However, for the main cases of NDVI changes are dependent on both temperature and precipitation climatic factors [30,93,161,162].…”

“…Many previous studies have reported strong correlations between NDVI and main climatic factors of precipitation and temperature [89][90][91][92][93]. Furthermore, the relationships between climatic factors and NDVI/vegetation are different in various geographical regions and types of land cover [41,88,94].…”

Multidecadal Trend Analysis of Armenian Mountainous Grassland and Its Relationship to Climate Change Using Multisensor NDVI Time-Series

“…The response of NDVI to climate factors may differ according to vegetation type and growth phase [164] with obvious lag-time effect [15], [26], [104]. In some regions, NDVI variation is due to variation in precipitation [29], [102], [165]- [167] in other regions, temperature is a major influencing factor in the variation of NDVI [34], [46], [92], [93]. However, for the main cases of NDVI changes are dependent on both temperature and precipitation climatic factors [34], [59], [93], [168].…”