фотосинтетического активного периода роста, появляется возможность оценивать сезонные и межгодовые изменения экосистемы и улучшить понимание динамических взаимодействий между атмосферой и биосферой . Разработка методов дистанционного определения нача- ла и конца вегетации, смены фаз развития культур открывает новые возможности в развитии методов точного земледелия. Использование данных дистанционного зондирования позволя- ет оценивать состояние культур на отдельных участках в пределах одного поля, определять их неоднородность, оперативно оценивать состояние посевов на территориально удаленных участках. Данные о стадиях фенологического развития растительности посевов являются клю- чевой информацией, необходимой при оценке их состояния и прогнозирования урожайности. Использование спутниковой информации предоставляет возможность дистанционно определять время наступления и окончания периода вегетации как в региональном, так и в гло- бальном масштабах. Выполнение этой задачи возможно при использовании ежедневной спут- никовой информации NOAA-AVHRR, SPOT-Vegetation или Terra/Aqua-MODIS. Первые данные о продолжительности периода вегетации были получены в результате обработки спутниковой информации NOAA-AVHRR . Более точные данные были получены с появлением прибо- ра Moderate Resolution Imaging Spectroradiometer (MODIS) на борту спутников Terra и Aqua . Высокое временное разрешение данных спектрорадиометра MODIS позволяет эффективно контролировать сезонную изменчивость сельскохозяйственных культур. Быстрое получение таких данных в глобальном масштабе также является необходимым предварительным усло- вием для точного измерения площади посевных площадей в крупных регионах и позволяет проводить их оценку и картографирование .
In terms of NDVI anomalies, median values are calculated from a sufficiently long data record to identify ‘normal’ vegetation conditions, but not so long that the land use or cropping system being observed have changed significantly. Figure 16 shows the timeline of the three remote sensing satellites imaging the Earth’s surface with an afternoon overpass, that will be used to form the land long term record: MODIS/Aqua, S-NPP and Joint Polar Satellite System (JPSS). It is expected that MODIS/Aqua will continue its nominal operations until 2022 (personal communication, Robert Wolfe, NASA Goddard Space Flight Center, June 2017) and JPSS-1 is planned to be launched at the end of 2017. At the time of writing, MODIS/Aqua has a 15 year data record which is used to calculate the median NDVI value. At the MODIS/Aqua end of life (2022), the data record would be 20 years and the VIIRS/S-NPP record would be 10 years. Inter-use of data products from these sensors is therefore likely to continue to be desirable; however, if the NDVI data records are combined, one should do so with an awareness of NDVI anomaly inter-consistency uncertainties of 0.033.
In the present study, we analyzed spatio-temporal vegetation dynamics to identify and delineate the vegetation stress zones in tropical arid ecosystem of Anantapuramu district, Andhra Pradesh, India, using Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Vegetation Anomaly Index (VAI) derived from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day products (MOD13Q1) at 250 m spatial resolution for the growing season (June to September) of 19 years during 2000 to 2018. The 1-month Standardized Precipitation Index (SPI) was computed for 30 years (1989 to 2018) to quantify the precipitation deficit/surplus regions and assess its influence on vegetation dynamics. The growing season mean NDVI and VCI were correlated with growing season mean 1-month SPI of dry (2003) and wet (2007) years to analyze the spatio-temporal vegetation dynamics. The correlation analysis between SPI and NDVI for dry year (2003) showed strong positive correlation (r = 0.89). Analysis of VAI for dry year (2003) indicates that the central, western, and south- western parts of the district reported high vegetation stress with VAI of less than − 2.0. This might be due to the fact that central and south-western parts of the district are more prone to droughts than the other parts of the district. The correlation analysis of SPI, NDVI, and VCI distinctly shows the impact of rainfall on vegetation dynamics. The study clearly demonstrates the robustness of NDVI, VCI, and VAI derived from time-seriesMODISdata in monitoring the spatio-temporal vegetation dynam- ics and delineate vegetation stress zones in tropical arid ecosystem of India.
2.3 Methods of image pre-processing
All the datasets used in this study were reprojected to the Universal Transverse Mercator (UTM) and subsets of the satellite imagery were created using the administrative boundary of Kwara State. Prior to the Seasonal Trend Anal- ysis (STA), the bi-weekly NDVI composite data were aggre- gated to a monthly composite using Maximum Value Com- posite (MVC) method which further reduces noise in the timeseries . The rainfall data was resampled to 8 km spatial resolution in order to match with the spatial reso- lution of using bilinear interpolation resampling method. The global land cover map of 2009 was resampled to 1km resolution so as to merge with the same spatial resolution of the 2000 global land cover. The land cover class for both maps was aggregated to six major classes, an overlay anal- ysis was applied to identify the change matrices for the time period of 2000 to 2009. Assessment (STA) was per- formed using three approaches as described below.
Rice is a major staple food for almost 50% of the world’s population (Kuenzer and Knauer, 2013) and China accounts for 28% of global paddy rice production (FAOSTAT, 2014). Chinese government policy is focused on improving yields through production methods that increase both food security and environmental sustainability (Peng et al., 2009). Efforts to promote sustainable intensification of production through effective monitoring and better farming practice are highly important in China (Ju et al., 2004; Li et al., 2012), but there is a major challenge to generate and convey locally accurate information and advice to the very large number of farmers in the country. In particular, understanding the dynamics of the specific phenological stages of rice crops is key to informing strategic (pre-growing season) and tactical (within-growing season) management practices that can maximise production sustainably. Likewise, inter-annual variations of crop seasonality parameters can be controlled by factors such as climatic conditions and agricultural policy (Li et al., 2012; Reed et al., 1994; Sakamoto et al., 2010), but the ability to correctly determine these parameters is crucial for optimising agronomic practice. In this context, satellite remote sensing could potentially aid sustainable intensification in China by giving researchers and extension officers the tools to characterise the spatial and temporal variability in crop growth and understand the reasons for this variability at field, village and province scales, in order to target the sharing of best practice.
Despite the wide use of MODIS sensors, many users reported that MODIStime-series can be subject to errors due to gaps, clouds and noise (e.g.   ). Therefore an adequate use of this product requires a correction of re- motely sensed time-seriesdata. Amongst the various approaches of time-series images smoothing, local and global fitting methods are widely used. Local fitting methods are based on surrounding value of data in a time-series determined by median smoothing approaches  or by Savitzky-Golay filter approach  . Global approaches fit the data to long time scale of observations such as Fourier transform used in frequency analysis of a signal (for an extended review of filtering methods, refer to ). The elimination of noise by filtering process allow san estimation of vegetation seasonal patterns such as the length of the season and the seasonal amplitude. Therefore, most of time-series smoothing algorithms are compared for their phonologic metric detection such as start and end of the season  . They also could bring valuable information on drought occurrence as addressed in  where Fourier transform was used on a time-series of NDVI to identify specific frequency components corresponding to dry years.
3. RESULTS 3.1 Analysing the interpolation methods
As explained in the chapter Methods & Materials, various interpolation methods are available to interpolate the existing signal with 23 NDVI values per year into a daily signal. Due to the fact that there is no data to validate the quality of the interpolation methods, they should be compared with each other. In a first step, the correlations are calculated in pairs between the interpolated timeseries. For the entire study period, these correlations range from 0.76 to 0.97 (see Figure 2, left) for a selected timeseries in the nature development zone of the study area. However, the different plots also show that there are a number of outliers whose course is partly opposite to the main correlation direction. This suggests, that there may be large local differences in the course of the resulting interpolations (see Figure 2, right).
𝐴 = (𝑎/𝑟(𝛺 𝑙 )) ∙ 𝑟 𝑙 (1)
Where 𝐴 is Landsat albedo to be calculated, 𝑟 𝑙 is observed Landsat reflectance and 𝛺 𝑙 is viewing 229
and solar geometry of Landsat data. 𝑎 is albedo and 𝑟(𝛺 𝑙 ) is the reflectance at Landsat sun view
Abstract: Afromontane forests are biodiversity hotspots and provide essential ecosystem services. However, they are under pressure as a result of an expanding human population and the impact of climate change. In many instances electric fencing has become a necessary management strategy to protect forest integrity and reduce human-wildlife conflict. The impact of confining hitherto migratory elephant populations within forests remains unknown, and monitoring largely inaccessible areas is challenging. We explore the application of remote sensing to monitor the impact of confinement, employing the Breaks For Additive Season and Trend (BFAST) time-series decomposition method over a 15-year period on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) datasets for two Kenyan forests. Results indicated that BFAST was able to identify disturbances from anthropogenic, fire and elephant damage. Sequential monitoring enabled the detection of gradual changes in the forest canopy, with degradation and regeneration being observed in both sites. Annual rates of forest loss in both areas were significantly lower than reported in other studies on Afromontane forests, suggesting that installing fences has reduced land-use conversion from human-related disturbances. Negative changes in EVI were predominantly gradual degradation rather than large-scale, abrupt clearings of the forest. Results presented here demonstrate that BFAST can be used to monitor biotic and abiotic drivers of change in Afromontane forests. Keywords: timeseriesanalysis; BFAST; montane forests; change detection; degradation; MODIS; fencing; elephant
This research had two primary objectives, which are initially briefly presented here and then followed by more detailed descriptions. The first was, to investigate the NDVI values between different compositing periods of time-seriesMODIS 250 m data for distinctive separability of crop types. This is based on the hypothesis that 8-day (and dual-8-day) composited NDVI, compared to 16-day composited NDVI, may show finer scale spectral- temporal variability that would facilitate improved crop separability and, ultimately, crop mapping. If evidence could be found to support this working hypothesis, it would improve our understanding of the behavior of crop development over a growing season that could lead us to better classification of crop classes using finer temporal resolution data; The second object was to investigate the differences in NDVI values between collections 4 and 5 of time-seriesMODIS 250 m data, again for distinctive separability of crop types. The working hypothesis was that collection 5 data, produced by an improved reprocessing algorithm, would provide greater intercrop separability and likely more accurate mapping of crop types. Further, if the results showed significant differences between the two collections/versions and the reasons for these differences could be identified, this would contribute to an understanding of how the vegetation index (VI) processing and compositing techniques between the two collections may affect LULC classification limitations ascribable to calibration and instrument characteristics, clouds and cloud shadows, atmospheric effects, etc.
Long-term NDVI timeseries is required to analyze the vegetationphenology. For that purpose, data from AVHRR (Advanced Very High Resolution Radiometer) sensors which have been archived since 1981 are commonly used in vegetationanalysis. Nevertheless, the datasets suffer from quality deficiencies due to instrumentation problems, changes in sensor angle, atmospheric conditions (e.g., clouds and haze), and ground conditions (e.g., snow cover), as has been reported by Bradley et al. (2007). They argued that the identification of phenology parameters via NDVI datasets is problematic. To overcome these problems, maximum value compositing (MVC), the best index slope extraction (BISE) and spatial and temporal smoothing methods have been introduced (Bradley et al. 2007). Among different datasets based on AVHRR, data from the NASA Global Inventory Monitoring and Modeling Systems (GIMMS) group at the Laboratory for Terrestrial Physics (Tucker et al. 2005) is the most known dataset (De beurs and Henebry 2010, Fensholt et al. 2009) . Moreover, due to enhanced and high quality of dataset which was achieved from applying several correction methods (Tucker et al. 2005), this dataset is widely used.
Drought is an extreme weather event which, according to FAO (2013), can be defined as deficit of precipitation compared to the inter-annual average and which results in water shortage. If as a result of drought, soil moisture is insufficient for specific agricultural crops, drought is called agricultural drought (FAO, 2013). It is stated in Peters et al. (2002) that droughts are hard to monitor, since droughts have varying length, spatial extent and magnitude. Another challenge is that the onset of drought is slow and often hard to determine. Thus, it is not sufficient to use climate data only for drought monitoring. Climate data must be supplemented by satellite data (Peters et al., 2002), which ―provides a synoptic view of the land and a spatial context for measuring drought impacts‖ (Gu et al., 2007). The Normalized Difference Vegetation Index (NDVI) is a comprehensive measure of photosynthetic activity, which has been widely used for monitoringvegetation response to climate variability in combination with weather data (Gesser et al., 2012; Propastin and Kappas, 2008; Runnström, 2000; Wang, et al., 2001; Aragón, et al., 2012; Ichii, et al., 2002; Peters et al., 2002; Gu, et al., 2007; Liu et al., 2011).
Four of the areas we selected for detailed sampling (areas “a”, “b”, “c” and “g”) are located in regions with high percentages of good quality pixels. Area “e” is situated in a transitional region, characterized by good quality occurring for approximately half of the months considered. Further interpretations for areas “d” and “f” should take into consideration the limited number of monthly composites labeled as having good quality. Reduced image quality can affect the analysis of timeseries and multiple methodologies have been proposed to deal with time-series reconstruction through gap filling and spike removal. However, no definitive method or procedural consensus exists involving those topics. We agree with  that the application of smoothing and filtering should be considered in a case-by-case basis. If judged necessary, these methodologies can be easily incorporated into our workflow as a pre- processing step. In particular, a spike removal approach could be used when identifying low- frequency/low-amplitude variations in phenology, often characterized by gradual transitions in vegetation response. In other situations, including the current work, the nature of the analyses requires the ability to identify abrupt changes in dataseries.
NDVI (Normalized Difference Vegetation Index) plays an important role in agriculture. Higher NDVI values usually represent greater vigor and photosynthetic capacity of vegetation canopy. Timeseriesanalysis of MODIS 250m surface reflectance 16 day composite data can be used to gain information on seasonal vegetation development. Here, the paddy phenological stages were detected. The main objectives of this study are extracting phonological parameters and preparing Seasonality Parameter maps for different agro ecological zones in Sri Lanka. To fulfill the agriculture application requirement, researchers are encouraging to make benefit from time-series remote sensing data 4 .
We have developed a Eucalyptus-specific spectral vegetation index called EucVI, designed for the easy retrieval of leaf area index (LAI) timeseries from MODIS reflectance data. The use of a synthetic database, created with a radiative transfer model (RTM) inversion procedure, to calibrate the spectral vegetation indices (SVIs) proved to be an efficient method. It aimed at increasing the number of data points for the calibration process, while still considering “real” cases, i.e., real stands with all the correlations between their biophysical and biochemical properties. The final vegetation index gives good results in most of the LAI range known for Eucalyptus stands in southeastern Brazil. The root mean square error (RMSE) calculated on an independent LAI dataset was 0.49, which is about 15% of the average measured LAI. However, high LAI values are still difficult to retrieve with such an index. The use of age and day of year as easily available additional information in the EucVI corr index
in latitude. This trend confirms results from other studies; however, we extended our analysis to 5000 sites compared to a previous study that analyzed altitudinal and latitudinal gradients of xylem phenology at only six sites black spruce sites [ 49 ]. The timings of bud break were observed from the beginning of May to the end of June and are related to a number of factors, including the fulfilment of the requirement in winter chilling, the lengthening of photoperiod and warming in spring temperature [ 66 , 67 ]. In contrast, bud set lasted longer, 87 days, and lacked a clear and constant spatial pattern. Bud set is an important phenological event and explains most of the variation in tree growth [ 68 , 69 ], while the bud set in black spruce is a typical photoperiod-dependent process [ 70 ]. In conifers, the cessation of shoot elongation and development of terminal buds indicates vegetative maturity. It therefore seems to result from exposure to the shortening days of late summer. During this time, the temperature remains warm so it could be a response to some endogenous signals triggered by the shortened photoperiod [ 66 , 71 ]. Overall, satellite remote sensing offers the possibility of modeling phenology by recording spectral information at regular time intervals due to their wider coverage area and high temporal resolution [ 22 , 72 ]. Our model utilized the potential of timeseries satellite data to provide the spatio-temporal patterns of bud phenology by calibrating NDVI across the Quebec region of Canada from 2009 to 2018. We expect that the presented calibration approach could be tested on a wider basis for other sites and tree species.
and product uncertainties are considered. Initial simulations using data processed with prototype C6 algorithms indicate no signi ﬁcant trends associated with sensor degradation for near-nadir VZAs. Thus, it is expected that C6 will largely remedy the issues described in this paper. Although MODISdata over the North American boreal forest and tundra were used in this study, the impacts of sensor degradation are not limited to this region because sensor degradation impacts all Terra/MODIS C5 TOA data and subsequent processing for land and at- mosphere products. In addition, degradation of Terra MODIS C5 data impacts ongoing efforts to inter-calibrate data from current and previ- ous generations of Earth observing satellites to generate long time se- ries of NDVI (e.g., Fensholt & Sandholt, 2005; van Leeuwen et al., 2006 ). Based on temporal trends in C5 Terra MODISdata identi ﬁed in this study, we recommend that users rely on Aqua MODISdata for timeseriesanalysis of vegetation changes and inter-calibration of MODIS, AVHRR, and the next generation of polar orbiting satellites (e.g., the NPOESS Preparatory Project —NPP) until C6 data are available for the Terra MODIStimeseries.
Dash, 2007; Goodin & Henebry, 1997; Tieszen, Reed, Bliss, Wylie, & DeJong, 1997 ).
Remote sensing has been successfully applied to extract global land use and land cover patterns. The MODIS Land Cover Product (MOD12Q1) ( Friedl et al., 2002, 2009 ), for example, is currently one of the most commonly adopted land cover data sets for global biogeochemical models ( Jung et al., 2006; Potter et al., 2007 ). With monthly AVHRR Normalized Difference Vegetation Index (NDVI) products, the U.S. Geological Survey (USGS) developed the 1-km land cover database and identi ﬁed 159 seasonal land cover classes in the conterminous United States ( Loveland et al., 1995 ). Using satellite imagery at much ﬁner resolutions, various nation-wide efforts have also been made to extract more speci ﬁc land surface covers. For example, the U. S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) uses medium-resolution satellite images to produce annual Cropland Data Layer (CDL) maps for various states ( Boryan, Yang, Mueller, & Craig, 2011 ). Landsat imagery was also applied to develop the 2001 U.S. National Land Cover Database at 30-m resolution ( Homer, Huang, Yang, Wylie, & Coan, 2004 ). Howev- er, these land cover products were not designed to delineate C 3 and C 4
To reduce the noise and generate a cloud-free NDVI timeseries for dataanalysis, a number of methods have been de- veloped and have been successfully applied in some cases. Some examples include the Best Index Slope Extraction algorithm (BISE) (Viovy et al., 1992; Lovell and Graetz, 2001) for analysis of daily NDVI data, the Asymmetric Gaussian function (Jonsson and Eklundh, 2002), a Savitzky- Golay filtering algorithm (Chen et al., 2004), the Mean-Value Iteration filter (MVI) (Ma and Veroustraete, 2006a), and the weighted least-squares linear regression to the temporal NDVI signal (Swets et al., 1999), among others. Even though these algorithms have been successfully used in many differ- ent applications, limitations need to be mentioned. For in- stance, the length of the sliding window and threshold value in BISE method may need to be adapted to different pheno- logical stages and for different plant species. The weighted least-squares linear regression and the Asymmetric Gaussian function could not capture the complex phenology of land cover characterized by two or more growth cycles in one year. The reliability of the Savitzky-Golay filtering algorithm strongly depends on the assumption that the envelope of the original data gives the best description of vegetation growth, while this concept may lead to overestimate of the NDVI values, which are in most cases generated using the MVC method. When two or more adjacent cloud contaminated NDVI values occur, many iterations with the MVI method are required to estimate a reliable NDVI value.
ited for such a protocol due to their spatial resolution, tem- poral resolution, availability, and/or cost.
Landsat TM/ETM+ data are appropriate for detailed crop mapping given the instruments’ multiple spectral bands, which cover the visible through middle infrared wavelength regions, and 30 m spatial resolution. However, most crop classification using Landsat data has been limited to local scales (i.e., sub-scene level) (Mosiman, 2003; Price et al., 1997; Van Niel and McVicar, 2004; and Van Niel et al., 2005). Most state/regional-scale LULC maps derived from Landsat TM and ETM+ data, such as the United States Geological Sur- vey’s (USGS) National Land Cover Dataset (NLCD) (Homer et al., 2004; Vogelmann et al., 2001) and the Gap Analysis Program (GAP) datasets (Eve & Merchant, 1998), have clas- sified cropland areas into a single or limited number of the- matic classes and are infrequently updated. The exception is the United States Department of Agriculture (USDA) Na- tional Agricultural Statistics Service (NASS) 30 m cropland data layer (CDL), which is a detailed, state-level crop classi- fication that is annually updated (Craig, 2001). However, the CDL is only produced for a variable and limited number of states (10 total states in 2004). The production of LULC da- tasets comparable to the CDL in other countries with large broad-scale farming systems is also lacking. The use of Land- sat data (and data from similar sensors such as SPOT) for re- petitive, large-area mapping has been limited primarily by the considerable costs and time associated with the acquisi- tion and processing of the large number of scenes that are re- quired. Data availability/quality issues (e.g., cloud cover) as- sociated with acquiring imagery at optimal times during the year are also a factor (DeFries & Belward, 2000).