Figure 10 - uploaded by Siddhartha Khare
Content may be subject to copyright.
Rainfall and LST variations for the Lachhiwala forest range. Black line indicates mean LST values whereas blue line indicates rainfall variation from 2013 to 2014.
Source publication
The health (or greenness) of the mountainous vegetation varies with seasons depending on its type and
local topographic and climatic conditions. The forests in the Western Himalayas are influenced by
variables such as precipitation and temperatures through seasons with considerable inter-annual variability. This study presents the phenological be...
Citations
... Nonparametric approaches were used to analyze the temperature-rainfall time series for significant patterns. The Mann-Kendall and Sen slope estimators, known for their computational efficiency, were used to ascertain the trend and slope, respectively, due to the non-normal distribution of the temperature-rainfall data [31]. ...
To accurately forecast the future development trend of vegetation in dry areas, it is crucial to continuously monitor phenology, vegetation health indices, and vegetation drought indices over an extended period. This is because drought caused by high temperatures significantly affects vegetation. This study thoroughly investigated the spatial and temporal variations in phenological characteristics and vegetation health indices in the abdominal part of Maowusu Sandland in China over the past 20 years. Additionally, it established a linear correlation between vegetation health and temperature indices in the arid zone. To address the issue of predicting long-term trends in vegetation drought changes, we have developed a method that combines the Informer deep learning model with seasonal and Seasonal Trend decomposition using Loess (STL) and empirical mode decomposition (EMD). Additionally, we have utilized the linearly correlated indices of vegetation health and meteorological data spanning 20 years to predict the Normalized Difference Vegetation Index (NDVI) and Temperature Vegetation Dryness Index (TVDI). The study’s findings indicate that over the 20-year observation period, there was an upward trend in NDVI, accompanied by a decrease in both the frequency and severity of droughts. Additionally, the STL-EMD-Informer model successfully predicted the mean absolute percentage error (MAPE = 1.16%) of the future trend in vegetation drought changes for the next decade. This suggests that the overall health of vegetation is expected to continue improving during that time. This work examined the plant growth circumstances in dry locations from several angles and developed a complete analytical method for predicting long-term droughts. The findings provide a strong scientific basis for ecological conservation and vegetation management in arid regions.
... In conclusion, environmental factors and topographical variables on forest phenology may have both positive or negative implications on various ecosystems and geographical locations. The Temporal Normalised Phenology Index (TNPI), which was suggested as a better alternative for examining the temporal phenology cycle between two time stages of the maximum and lowest plant growth phase, was created to deal with this quantitative constraint of NDVI (Khare et al., 2017). Additionally, using time series Landsat-8 data, the sensitivity of varying NDVI to specific topography factors derived from remote sensing and land surface temperature was successfully tested using TNPI (Khare et al., 2017). ...
... The Temporal Normalised Phenology Index (TNPI), which was suggested as a better alternative for examining the temporal phenology cycle between two time stages of the maximum and lowest plant growth phase, was created to deal with this quantitative constraint of NDVI (Khare et al., 2017). Additionally, using time series Landsat-8 data, the sensitivity of varying NDVI to specific topography factors derived from remote sensing and land surface temperature was successfully tested using TNPI (Khare et al., 2017). ...
... The shift between two phenological phases may be quantified using the Temporal Normalised Phenology Index (TNPI), a temporal index (Khare et al., 2017(Khare et al., , 2021. It measures the growth between the beginning and peak of a plant's growth when applied to those two points of measurement. ...
The Himalayan range is considered to include the highest mountains on earth, with a widely recognized assortment of flora and fauna. The Indian Himalayan range spread over 10 states, of which being Uttarakhand, has the largest forest cover of 48.5% of the total area forest. In Doon Valley, the phenological behavior and variations of several forest types have been studied using the Normalized Difference Vegetation Index (NDVI) and Temporal Normalized Phenology Index (TNPI) along with elevation, surface temperature, slope, and aspect data into consideration. A two-scale method has been employed to study forest phenology, using Sentinel-2 data at 10 m spatial resolution for local-scale studies and MODIS NDVI data at 250 m spatial resolution to find large-scale phenological patterns. The framework used in this study is based on Google Earth Engine (GEE) which has potential applications at various spatial and temporal scales. Multiple phenological phases and phenological metrics have been identified and examined within the duration from December 2018 to May 2023. The investigation concluded that phenological behaviors were considerably affected by environmental and topographic variables such as elevation, surface temperature, slope, and aspect. Significant changes in phenology were recorded at low altitudes; however, less fluctuation was reported at medium to higher elevations due to remoteness at greater elevations. Using NDVI from open-source MODIS and Sentinel-2 datasets, TNPI has been successfully tested for this forest region. The findings showed that this study opens new opportunities for trend analysis of forest health and productivity.
... There are several tools for identifying crop phenology, with the most commonly used method being the Normalized Difference Vegetation Index (NDVI) threshold [12]. However, the Temporal Normalized Phenology Index (TNPI) is predicted to provide a better comprehension of phenological changes at two phases than utilizing single NDVI data [13]. In India, Vaghela et al. [1] used the TNPI derived from Sentinel-2 to produce the crop growth profiles of wheat and mustard crops. ...
... Unlike other phenological indices, the TNPI only requires two time steps rather than the complete temporal sequence of the vegetation period, resulting in a reduction in the amount of time-series data to be analyzed. The proposed temporal index is predicted to improve knowledge of phenological changes at two phases when compared with utilizing single NDVI data [13]. The TNPI can be computed as follows: ...
Crop monitoring is critical for sustaining agriculture, preserving natural resources, and dealing with the effects of population growth and climate change. The Sentinel missions, Sentinel-1 and Sentinel-2, provide open imagery at a high spatial and temporal resolution. This research aimed (1) to evaluate the temporal profiles derived from Sentinel-1 and Sentinel-2 time series data in deducing the dates of the phenological stages of wheat from germination to the fully mature plant using the Google Earth Engine (GEE) JavaScript interface and (2) to assess the relationship between phenological stages and optical/ SAR remote sensing indices for developing an accurate phenology estimation model of wheat and extrapolate it to the regional scale. Firstly, the temporal profiles derived from Sentinel-1 and Sentinel-2 remote sensing indices were evaluated in terms of deducing the dates of the phenological stages of wheat. Secondly, the remote sensing indices were used to assess their relationship with phenological stages using the linear regression (LR) technique. Thirdly, the best performing optical and radar remote sensing indices were selected for phenologi-cal stage prediction. Fourthly, the spatial distribution of wheat in the TIP region was mapped by performing a Random Forest (RF) classification of the fusion of Sentinel-1 and Sentinel 2 images, with an overall accuracy of 95.02%. These results were used to characterize the growth of wheat on the TIP regional scale using the Temporal Normalized Phenology Index (TNPI) and the predicted models. The obtained results revealed that (1) the temporal profiles of the dense time series of Sen-tinel-1 and Sentinel-2 indices allowed the dates of the germination, tillering, jointing heading, maturity , and harvesting stages to be determined with the support of the crop calendar. (2) The TNPI-increase and TNPIdecrease revealed that the declining part of the NDVI profile from NDVIMax, to NDVIMin2 revealed higher TNPI values (from 0.58 to 1) than the rising part (from 0.08 to 0.58). (3) The most accurate models for predicting phenological stages were generated from the WDVI and VH-VV remote sensing indices, having an R 2 equal to 0.70 from germination to jointing and an R 2 equal to 0.84 from heading to maturity.
... MODIS vegetation indices products such as the normalized difference vegetation index (NDVI) [7,8] and the enhanced vegetation index (EVI) [9] have been widely used for understanding the temporal behaviour of land surface vegetation phenology [10][11][12]. Previous studies have used the potential of these time-series vegetation indices as a robust metric for estimating the photosynthetic activity, developmental status, and productivity of vegetation and retrieved land surface phenology metrics such as the beginning of onset, end of senescence, and length of growing season for different vegetation types [13][14][15][16]. However, validation of satellite-derived phenology metrics remains uncertain and challenging due to the limited availability of field observations at high temporal and spatial resolutions, mainly for remote locations or areas with difficult accessibility. ...
Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R2 from 0.66 to 0.85) than NDVI (R2 from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather.
... Optical satellite remote sensing provides information for studying large-scale forest changes in near real-time with a comparable spatial and higher temporal resolutions compared with field surveys, which are often constrained by logistics and difficult-toaccess terrain. Among the available methods, those based on broadband vegetation indices (VIs) derived from multispectral data have been widely used [5][6][7] for vegetation classification [8], phenological monitoring [9], change detection [10], and the retrieval of forest biophysical and structural attributes [11]. Since 2015, the Sentinel-2 archive has provided continuous imaging information on the Earth's surface, with which researchers have lately begun to investigate ecosystem changes, inter alia using approaches based on common broadband VIs. ...
... NDVI provides information about vegetation greenness, which records photosynthesis activity of a plant or tree leaves [16]. To understand change in photosynthesis activity between different seasons or vegetation growth cycle, change in NDVI values may not be sufficient for the entire temporal sequence of a vegetation growth period [9,17,18]. To solve this numerical limitation of NDVI, a new measure called the temporal normalized phenology index (TNPI) was proposed and recommended as a superior option for analyzing the temporal phenology cycle between two time steps of the maximum and minimum plant growth period [9]. ...
... To understand change in photosynthesis activity between different seasons or vegetation growth cycle, change in NDVI values may not be sufficient for the entire temporal sequence of a vegetation growth period [9,17,18]. To solve this numerical limitation of NDVI, a new measure called the temporal normalized phenology index (TNPI) was proposed and recommended as a superior option for analyzing the temporal phenology cycle between two time steps of the maximum and minimum plant growth period [9]. Furthermore, with time series Landsat-8 data, the sensitivity of fluctuating NDVI to individual remote sensing-derived topography components and land surface temperature was effectively tested using TNPI, with the main benefit that it reduced the need for long-term monthly records to understand the forest phenology [9]. ...
Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility.
... A positive but weak relation of NDVI observed with altitude in our study draws some support from different studies. The north western Himalayan mountain ecosystems are predominantly influenced by altitude (Khare et al. 2017). While studying the relationship between NDVI and terrain factors in Chongqing (China), Zhan et al. (2012) reported increased NDVI with rise in altitude to a certain limit. ...
Assessing biophysical variables are essential for evaluation of carbon dynamics due to anthropogenic activities. Biomass carbon is an important biophysical parameter of forest ecosystems that indicates carbon mitigation and human–forest interactions. Spectral modeling approach was used to assess the relation of Normalized Difference Vegetation Index (NDVI) with biomass carbon, crown density, tree density, slope, altitude, aspect, species, and forest division in temperate conifer region of Himalaya. Field inventory was recorded from 188 biomass plots of 0.1 ha each across the study area. NDVI was observed to have a positive relation with aboveground biomass carbon, crown density, tree density, and altitude. The NDVI and ABC values ranged from (0.11 to 0.43) and (1.54 to 276.82 t ha−1), respectively. Among the aspects, highest and lowest average NDVI was observed for south east (0.289) and north (0.258), respectively. Similarly highest and lowest average aboveground biomass carbon was observed for north east (72.63 t ha−1) and east (44.30 t ha−1), respectively. NDVI expressed a fairly good relation with biophysical parameters including altitude, aspect, crown density, tree density, species, and location (forest division). NDVI using principal tree species composition (forest type) revealed a relation with aboveground biomass carbon for Cedrus deodara (R2 = 0.63), Mixed I (R2 = 0.61), Pinus wallichiana (R2 = 0.57), and Mixed-II (R2 = 0.48). NDVI demonstrates potential to understand biomass carbon variability through establishment of relations with forest biophysical parameters using spectral modeling approach. Varying NDVI can be ascribed to vegetation canopy density, number of stems, species, and altitude. The database and established relations would help indicate biomass carbon dynamics and enable to adopt site-specific management. The study further helps draw inferences on mitigation and adaptation perspectives in view of varying biophysical conditions that occur in a forest.
... Vegetation indices suggested in the literature such as Fig. 7 Classification results for the fuzzy-based MPCM algorithm and learning-based 1D-CNN algorithm. The red circle clearly depicts that transplanted paddy fields have been correctly classified Journal of the Indian Society of Remote Sensing temporal normalized phenology index (TNPI) (Khare et al. 2017) which especially deals with temporal data can be incorporated in future studies and their effect on classification results can be studied. The use of 1D-CNN-based model seems promising in the fields of temporal image processing as it could provide the outputs with very high accuracies while handling heterogeneity within class. ...
With increasing availability of satellite data of high temporal resolution, a more robust classifier is needed which can exploit the temporal information along with the spectral information of the remote sensing images. Specific fuzzy-based and learning-based algorithms are two broad categories and have the potential to perform well in spectral–temporal domain. In the present study, for mapping paddy fields as a specific class two classification algorithms, viz. fuzzy-based modified possibilistic c-mean (MPCM) algorithm and learning-based 1D-convolutional neural networks (CNN), were tested using Sentinel-2A/2B temporal data. The overall accuracy for learning-based 1D-CNN and fuzzy-based MPCM classifiers was found to be 96% and 93%, respectively. The F-measure values were found to be 0.95 and 0.92 for 1D-CNN- and MPCM-based classifier, respectively. Thus, it can be inferred from this study that the 1D-CNN classifier performed better than the traditional fuzzy-based classifier and can handle heterogeneity within class.
... Temporal Normalized Phenology Index (TNPI) proposed by Khare et al. (2017)to quantify the change in trajectories of Normalized Difference Vegetation Index (NDVI) during two time steps of the vegetation growth cycle was analysed to compute changes between two phenological growth stages of wheat crop. This concept quantifies the growth between two phenological stages. ...
In the present study on winter crop growth monitoring in Bhuj Taluka of Kachchh district was carried out using multi-temporal Sentinel-2 multi-spectral data (spatial resolution 10-m). Sentinel-2 multi-spectral data for the period from October-2018 to March-2019 was analysed in this study. The major objective of this study was to generate crop growth profiles of different crops and identify the relationships between crop phenology and crop growth stages by generating Normalized Difference Vegetation Index (NDVI) profiles of wheat and mustard crops.
The results of spectral behavior of wheat (normal & late sown) and mustard during active growth stages indicated that these winter crops have distinct spectral signatures in Sentinel-2 spectral bands. The NDVI growth profiles of wheat and mustard indicate the temporal variations during different growth stages of these crops. The maximum temporal variations were observed during early growth stages to flowering & grain filling stages. The difference in sowing dates of wheat and mustard crops result in different phenological stages of these winter crops. The NDVI curve of Rabi Season-2018-19 was divided into different phenological growth phases namely, i) Phase-I: G0 (Spectral emergence) to G1(Flag leaf emergence), Phase-II: G1 (Flag leaf emergence) to Gmax (Flowering and Grain filling), Phase-III: Gmax (Flowering and Grain filling) to G2 (Dough Stage) and Phase-IV: G2 (Dough Stage) to G3 (Physiological Maturity). The temporal changes in the NDVI was analysed by computing the Temporal Normalized Phenology Index (TNPI) between different phenological phases from G0 to G3.
Mean TNPI values of wheat crop for different phenological phases were computed which indicated that rising part of NDVI profile of growth phases from G0 to Gmax higher TNPI values whereas the declining part of NDVI profile of growth phases from Gmax to G3 showed lower TNPI values. The difference NDVI images were generated using the NDVI images of two different phenological phases of wheat crop which represent the temporal changes in wheat crop greenness, where “0” represents no temporal change and “1” represents the maximum temporal change in vegetation greenness across wheat crop areas in the study area.
... To understand the temporal variation in biodiversity from 2000 to 2015, mean values for NDVI, MSAVI, Q NDVI and Q MSAVI were computed for each raster image (i.e. 144 images) and parameters for a total of 576 images, using the routines implemented in R (Khare et al., 2017). Monthly averages of these data were plotted along with rainfall data to reflect the relationship of phenology with spectral heterogeneity derived β-diversity. ...
... Time series Rao's Q diversities from (Q NDVI and Q MSAVI ) were modelled by multiple regressions as a function of vegetation indices (VIs) NDVI and MSAVI, and environmental factors including temperature (T mean ) and precipitation (PPT), according to their influence on vegetation response previously shown by Khare et al. (2017). A sensitivity analysis was performed using the standardized coefficients (Std. ...
... Parameter estimation suggests that the likelihood of Rao's Q diversity is more sensitive to temperature compared to precipitation for both indices. A former analysis in a similar area showed that the temperature and precipitation were strongly correlated with NDVI and played major role in changes in vegetation greenness in MDF (Khare et al., 2017). Furthermore, an analysis in water conservation area showed that the Landsat RGB based β-diversity are strongly correlated with changes in vegetation composition and the average annual rainfall of each season (Convertino et al., 2012). ...
Understanding temporal dynamics of plant biodiversity is crucial for conservation strategies at regional and local levels. The mostly applied hitherto methods are based on field observations of the plant communities and the related taxa. Satellite earth observation time series offer continuous and wider coverage for the assessment of plant diversity, especially in remote areas. Theoretical basis and large-scale solutions for assessing beta-diversity have been recently presented. Yet landscape-scale and context-based analysis are missing. We assessed temporal β-diversity using Raoś Q diversity derived from Landsat-based vegetation indices by considering the effect of ERA-5 monthly aggregates environmental factors (temperature and precipitation) extracted using Google Earth Engine (GEE), land use classes, and two common vegetation indices. We derived 15-year Rao's Q diversity using Landsat-7 based normalized difference vegetation index (NDVI) and modified soil-adjusted vegetation index (MSAVI). We evaluated the temporal turnover in Rao's Q on multiple land use classes, including agriculture, intact forest and areas affected by and invasive species. Vegetation index and Rao's Q diverged between pre-and post-monsoon seasons. Rao's Q had higher temporal turnover with NDVI than MSAVI for all vegetation classes, however the latter showed higher sensitivity towards temperature and precipitation. Moreover, agriculture generally showed higher variability than forest and invasive species. The temporal turnover was correlated between NDVI and MSAVI for all vegetation classes, which indicated that the variability among vegetation types was directly related to spectral heterogeneity. Furthermore, MSAVI was less sensitive to the effect of soil in assessing the vegetation indices, which resulted in higher global sensitivity of Q MSAVI. Near infrared and red spectra used in vegetation indices are able to capture a small variation in leaf traits reflectance for vegetation types. Here, the β-diversities and their temporal dynamics derived from the vegetation indices differed based on their sensitivity to soil, vegetation density and seasonality. This approach and its open source implementation can be tested for different forest ecosystems at varying spatial scales.
... The study region experiences a subtropical climate with temperatures ranging between 16.7 o C and 36° C during summers and between 5.2°C and 23.4°C during winters 18 with a mean annual rainfall of 2,025 mm 19 . It receives most of its rainfall during monsoon and some during winters. ...
... KBDI value the previous day, was assumed to be '0'. Based on the mean annual rainfall of the study area i.e. 79 inches (2025 mm) 19 , the suitable Drought Factor (DF) table i.e. Table-5 was selected for this study. ...
Limited fore-knowledge of the impending ignition potential is one of the main causes of not-so-effective forest-fire management in India. The present study is an approach to link forest fire trends and the Keetch–Byram Drought Index (KBDI) for assessing forest fire forewarning. Fire trends were analyzed based on forest fire incidents detected by MODIS remotely-sensed data in the Shivalik forest tracts of Uttarakhand State of India over a period of 16 years to identify the vulnerable window of most-prone weeks. The study further uses KBDI to calculate the drought build-up in an area based on which the severity of impending forest fires can be predicted. This is one-of the first attempts for the inclusion of KBDI in understanding forest fire regime in Indian conditions. Based on the weekly-trends obtained from forest fire points during 2001-2016 affirm the most vulnerable time period for the occurrence of forest fire in the study region is from week 14 to week 22. The performance of KBDI proved to be a valid parameter in forecasting the severity of forest fire as its pattern over three years of study period (2014-2016) showed the same dynamics. The minimum value of KBDI i.e. the ‘dip’ value is significant for its use as a predictor to the severity of the fire season in the ensuing summer a full 10 weeks in advance. The findings of the present study pave the path for identifying the high-risk areas and severity of possible fires in the following fire season by correlating remote sensing and GIS techniques with Fire trends, peak period and Keetch Byram Drought Index. This study has a potential for adoption in countries like India for modern forest fire preparedness and management in a cost-effective manner.
