Analysing and denser vegetation (Peckham and Jordan 2007).

Analysing the vegetation dynamics is important in the context of their
sustainability. Vegetation structure changes rapidly due to various reasons
influenced by both anthropogenic and natural factors, potentially resulting in the
degradation and destruction. Vegetation phenology is impacted by lifecycle
patterns, season and climatic conditions resulting in changes of their spectral
reflectance patterns (Rußwurm and Korner 2017). Spatio-temporal mapping and monitoring of vegetation
helps in assessing the health of vegetation and imply better management practices
to safeguard them. The availability of the spatio-temporal satellite data aids
in visualizing changing vegetation dynamics in due course of time. Further,
development of vegetation indices using remote sensing satellite data provides
better insights about the growth patterns, seasonal changes and health
conditions of the vegetation (Wang et al 2016).

 

Normalised Difference Vegetation Index (NDVI) is one of the most widely
acknowledged indices for vegetation related studies (Xue and Su 2017). NDVI is
a numerical indicator of the greenness of vegetation derived using the visible
and near infrared (NIR) bands (Jeevalakshmi et al 2016, Eslamian and Eslamian
2017). Typically, the NDVI values are normalized between +1 and -1, and a
higher NDVI value indicates greener and denser vegetation (Peckham and Jordan
2007). The negative NDVI value denotes other non-vegetated classes like water, urban
areas, barren lands, snow, etc (Anonymous 2000). The generalized annual NDVI
profile for vegetation rises with the increase in the plant growth and reaches
a peak or plateau (Jose et al. 2002). Later the profile falls off eventually
with plant death or leaf senescence. Thus, NDVI series provides a means to
describe plant phenology (Viovy et al. 1992,
Wang et al 2016). However, the phenological changes in concurrence with
time appear to be very minimal or obscure, as observed in the vegetation of
tropical rainforest due to subtle climatic variations (Valtonen et al 2013).

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The analysis of time
series is widely used approach in many studies as they work with data of high
temporal resolution and low spatial resolution (Petitjean et al 2014). The
availability of time series NDVI data enhances information about the vegetation
conditions as well as in assessing and monitoring its changes (Ivits et l 2013).
Interim changes are better inferred by time series analyses which cannot be
accounted using low temporal satellite data. Time
series includes any temporary changes and thus provides better analysis than
any other datasets, as the analysis is based on observations of particular time
frame. Major seasonal changes and inter-annual patterns can be well defined by
analyzing the time series data of vegetation and surface water bodies (Haas et al. 2009; Tulbure and Broich 2013; Cordeiro et al. 2016; Rembold
et al. 2015).  NDVI
time series analysis offers more accurate and efficient results in detecting
the change in vegetation cover (Lyu and Mou
2016; Agone and Bhamare 2012), land use and land cover (LULC)
classification (Gómez et al. 2016; Anderson
1976), estimation and prediction of vegetation, mapping forest
disturbance (Kennedy et al 2010), etc.
Time series analysis also enables to estimate and model biomass (Gómez et al. 2014), analyze forest
degradation (Shimabukuro et al. 2014) and
assess forest carbon sinks (Gómez et al. 2012).

 

Various types of remotely sensed imagery and processing methods have
been introduced and used to predict NDVI time series. Autoregressive integrated
moving average (ARIMA) models are used for forecasting NDVI time series (Stepchenko 2016; 
Manobavan et al.  2002).
These models use the adjoining data to predict the next values in the time
series, but as these models are parametric and assumption of the data to be
linear and stationary, makes them inappropriate for precise prediction of time
series. Markov chain model simply constructs a probability mass function
incrementally across the possible next states
(Stepchenko and Chizhov 2015). It is memory-less as it
considers only the present state of the process to predict future. It is a
simple and effective method for prediction, but it uses a fixed window (Kriminger and Latchman 2011).

 

Neural networks have become popular in the analysis of remote sensing
data with the increase in availability of satellite data, as they are
non-parametric unlike most of the statistical methods (Foody 2006; Mas and
Flores 2008).  Many studies have shown
that neural networks work well as they are non-linear models and perform well
with noisy data as well (Atkinson and Tatnall 1997). Some
of the applications of neural networks in remote sensing are land cover mapping (Zhou and Yang 2008), forest change detection (Gopal and Woodcock 1996), and predicting vegetation
changes (Kang et al. 2016). Different
neural networks from the Multiple Layer Perceptron (MLP) (Atkinson and Tatnall 1997), Artificial Neural Network (ANN) (Silva et al. 2014; Miller et al. 1995) to
advanced neural networks like Back-propagation (BP) neural networks (Zhang and Chang 2015) and Convolution Neural
Networks (Maggiori et al. 2016) are used
for classification of remote sensing satellite imagery (Atkinson and Tatnall 1997).

 

ANNs can be used for forecasting NDVI index (Kang et al. 2016; Nay et al. 2016), but as ANNs have no memory to
store the information of the past data in the time series, results are less efficient.
Recurrent neural network (RNN), which has memory can also be used for
predicting the time series (Stepchenko and Chizhov 2015; Stepchenko 2016), by training the RNN with some past NDVI values
but vanishing gradient problem of RNNs makes them less suitable. Further, they
require a substantial amount of preceding values as input to the network for
prediction of the succeeding values and thus involves lot of computational overhead
compared to Long Short Term Memory (LSTM) network. The LSTM is a variant of RNN, which has an internal
memory to store the information received till time t, for a long time in the model (Skymind
2016; Budama 2015). This property of LSTM makes them very much preferable
for predicting the time series. LSTM is a deep learning neural network which is
highly preferred in predicting time series due to its long-term memory (Gamboa
2017).

 

Monitoring vegetation
changes is inevitable in the context of current climatic change conditions and
rapid human interventions. Vegetation, especially island ecosystems are
vulnerable to both human induced disturbances and natural forces like tsunami
and volcanic eruptions. Seasonal changes are common in vegetation. However, it
is also effected by climate change and may lead to drastic change due to
catastrophes (Verbesselt et al. 2009). Change detection methods
help in assessing the change in vegetation (de Beurs and Henebry 2005) and the
methods available so far are able to identify changes using low temporal
resolution or decadal gap remote sensing data (Coppin et al. 2004). They are
able to track the extent of areal changes but are incapable of identifying the
minor seasonal or any gradual changes.

 

In view of this drawback,
time series data with higher temporal resolution depict better changes in
vegetation (seasonal, gradual or sudden abrupt changes) and is adopted widely
in assessing minor/shorter phenomenal changes. Further the use of index derived
from time series data (such as NDVI) is beneficial in identifying the changes
compared to typical satellite data analysis, where only areal extent is
calculated and compared. Time series data can be analysed using varied ANN
methods and the best one proposed in recent past is the use of LSTM.  Though LSTM gained specific significance in
different themes (like LULC, soil moisture studies, predictions / forecasting
an event), its application in vegetation studies is still in nascent stage. The
research of Rußwurm and Korner (2017) can be cited as one example of LSTM study adopted for
crop identification using Sentinel data. However, no specific studies have been
carried out with reference to use of LSTM in forest vegetation and the current
study is first of its kind to be cited as an example of LSTM study in
prediction of forest dynamics as well as for the study area too.

 

In the view of above
context, the main objective of current research is to predict vegetation
dynamics using MODIS (Moderate Resolution Imaging
Spectroradiometer) NDVI time series data. Time series is a sequence of
correlated scalars or vectors which vary with time. In the present study, the
sequence consists of the scalar NDVI values. Time series can be predicted, if one
knows the past values in the series up to time t, and then estimating the value
at time t+s, s = 1, 2… The study considered s = 1. LSTM network is trained to
predict the NDVI value at t+1.