Improved NDVI based proxy leaf-fall indicator to assess rainfall sensitivity of deciduousness in the central Indian forests through remote sensing

Abstract

Quantifying the leaf-fall dynamics in the tropical deciduous forest will help in modeling regional energy balance and nutrient recycle pattern, but the traditional ground-based leaf-fall enumeration is a tedious and geographically limited approach. Therefore, there is a need for a reliable spatial proxy leaf-fall (i.e., deciduousness) indicator. In this context, this study attempted to improve the existing deciduousness metric using time-series NDVI data (MOD13Q1; 250 m; 16 days interval) and investigated its spatio-temporal variability and sensitivity to rainfall anomalies across the central Indian tropical forest over 18 years (2001–2018). The study also analysed the magnitude of deciduousness during extreme (i.e., dry and wet) and normal rainfall years, and compared its variability with the old metric. The improved NDVI based deciduousness metric performed satisfactorily, as its observed variations were in tandem with ground observations in different forest types, and for different pheno-classes. This is the first kind of study in India revealing the spatio-temporal character of leaf-fall in different ecoregions, elevation gradients and vegetation fraction.

Remote-Sensing Monitoring of Postfire Vegetation Dynamics in the Greater Hinggan Mountain Range Based on Long Time-Series Data: Analysis of the Effects of Six Topographic and Climatic Factors

“…Regional climate change can interfere with the forest ecosystem [42,43], especially the interaction with fire, which can cause serious damage to the forest [44]. In the process of studying vegetation recovery after wildfires, climate change, seasonal change, and topographic factors may affect the postfire response [45][46][47]; there was a positive correlation between NDVI and precipitation during the postfire recovery period [48]. Drought may reduce ecosystem resilience; i.e., the ability to recover the predisturbance state [49].…”

Resilience of the Central Indian Forest Ecosystem to Rainfall Variability in the Context of a Changing Climate

“…The smoothed NDVI data were upscaled from 250 m to 0.05 • (~5 km) by using mean aggregation to match the spatial resolution of the rainfall data. To strengthen the analysis, we considered only the forested pixels based on a forest mask at 5 km (see Singh et al [29] for further details). A methodology representing the entire procedure involved in this study is shown in Figure 2.…”

“…First, we considered the smoothed NDVI data at 250 m resolution and then estimated mean NDVI over the entire annual cycle (NDVI-A250) and mean NDVI during the peak growth period (NDVI-P250). Generally, peak growth period refers to the period where NDVI values are greater than 50% of the maximum annual NDVI (see Singh et al [29] and Rajan and Jeganathan [67] for details). In central India, the major vegetation vigour is observed during July, August, September and October.…”

“…Satellite data-based vegetation indices, including the Normalized Difference Vegetation Index (NDVI), and climatic parameters have been used to reveal vegetation dynamics and their responses on a monthly, inter-annual basis, capturing any heterogeneity and shifts in biodiversity [25]. NDVI coupled with climatic data has also been used widely for studying climate impacts on vegetation, forest dynamics and vegetation vigour [26][27][28][29], as temperature and rainfall are important factors affecting photosynthesis in plants [30]. In recent years, an increase in the number of extreme events (warming and precipitation with different intensity and magnitudes) has been experienced during both summer and winter [31][32][33].…”

Detecting the Complex Relationships and Driving Mechanisms of Key Ecosystem Services in the Central Urban Area Chongqing Municipality, China

“…WC was also influenced by other factors, once the resistance of the controlling ability of precipitation for WC was broken, WC decreased significantly. Singh et al [84] also corroborated a high rainfall did not guarantee a high WC in a region as it depends on variety of factors. WC increased linearly with elevation and nonlinearly with TNI.…”