MODIS NDVI Time Series Data Research Articles

Trend analysis of MODIS NDVI time series and its relationship to temperature and precipitation in Northeastern of Iran.

Vegetation plays a crucial role in providing organic matter and regulating energy exchange on the Earth's surface. This study investigates the changes in vegetation cover, temperature, and precipitation in northeastern Iran during 2001-2020. MODIS-NDVI time series data and climatic data from 11 synoptic stations were utilized. The nonparametric Mann-Kendall method was employed to detect trends in vegetation cover and climatic variables. Additionally, the correlation between climatic parameters and vegetation was examined. Trend analysis revealed significant increases/decreases in vegetation cover in 32%/26% of the region, respectively. The increasing trend in vegetation cover was predominantly observed in highlands, suggesting that a warmer climate has enhanced the living conditions for plants in these regions. The vegetation trend map indicates an expansion of vegetation cover in the northern and central parts of Iran during the past 20years, whereas the southern and eastern portions experienced declines. The relationship between vegetation and elevation revealed that vegetation increased above 1,850m and decreased below 850m. Trend analysis showed no significant trend in precipitation data since the beginning of the twenty-first century, but an increasing trend in temperature was observed in 82% of the region's area, excluding the western strip. Correlation coefficients between temperature, precipitation, and vegetation indicated that declining temperatures are the limiting factors for vegetation in the highlands, while in lowland areas, the decrease in precipitation significantly diminishes vegetation growth.

Assessing changes in vegetation cover using time-series MODIS NDVI data : A case study of Bayantal soum in Govisumber Province

Determining the pattern of changes in vegetation cover is vital for understanding the environmental change and its influencing factors, as well as for planning and implementing optimal management for mitigation and adaptation. This study aimed to determine the changes in the vegetation cover of the Bayantal soum in Govisumber province, located in transition zones between the steppe and the Gobi. We obtained MODIS NDVI data with a spatial resolution of 250 m and spanning June, July, and August of 2000 to 2020 from the NASA EOS. We applied Sen’s slope and Mann-Kendal test to detect spatial-temporal changes in vegetation cover and its trend. According to the results, average NDVI values ranged between 0.2 to 0.4 in Bayantal soum. During the past 21 years, the number of NDVI pixels with values of 0.2-0.3 significantly decreased, whereas, the number of NDVI pixels with values of 0.3-0.4 increased. Long-term NDVI series data indicated that vegetation cover in the soum had a positive change or increasing tendency. A significant increase in the NDVI was observed and accounted for 46.3% of the total area of the soum. The decrease in NDVI was estimated at 0.1 percent of the total area of the soum but it was not significant. The remaining areas of the soum experienced variations in NDVI. The analysis revealed that vegetation cover increased by 0.0056 units per year. Overall, the vegetation cover in the Bayan soum increased over the past 21 years, which conflicts with other studies indicating the deterioration. Therefore, remote sensing data and field monitoring surveys are recommended to detect robust changes in vegetation cover. MODIS хиймэл дагуулын NDVI мэдээг ашиглан ургамал нөмрөгийн өөрчлөлтийг үнэлэх нь: Говьсүмбэр аймгийн Баянтал сумын жишээн дээр ХУРААНГУЙ: Ургамлан нөмрөгийн өөрчлөлтийг судлах нь байгаль, экологид гарч буй өөрчлөлт, түүнд нөлөөлөх хүчин зүйлсийг танин мэдэх, цаашилбал тэрхүү байгаль экологийн өөрчлөлтийг сааруулах, дасан зохицоход чиглэгдсэн менежментийн оновчтой арга хэмжээг төлөвлөх, хэрэгжүүлэхэд чухал ач холбогдолтой юм. Энэхүү судалгааны ажлын хүрээнд, хээрийн бүсээс говийн бүсэд шилжих экотон бүс нутагт орших Говьсүмбэр аймгийн Баянтал сумын ургамал нөмрөгт гарч буй өөрчлөлтийг тодорхойлохыг зорьсон болно. Судалгаанд MODIS хиймэл дагуулын 2000-2020 оны 6, 7, 8-р саруудын 250 м-ийн орон зайн шийдтэй ургамлын нормчлогдсон ялгаврын индекс (NDVI)-ийн бүтээгдэхүүнийг АНУ-ын Сансар судлалын агентлагийн Эх дэлхийг ажиглах системийн (NASA EOS) мэдээллийн серверээс татан авч ашигласан. NDVI-ийн урт хугацааны цуваа мэдээнд тулгуурлан ургамал нөмрөгт гарч буй өөрчлөлтийг Тейл-Сений налуугийн аргаар, өөрчлөлтийн хандлагыг Манн-Кендаллын аргаар тооцсон. Хуурай хээрийн бүсэд орших Баянтал сумын хэмжээнд ургамлын индексийн зонхилох утга 0.2-0.4 хэлбэлзэж байна. Сүүлийн 21 жилийн хугацаанд NDVI-ийн 0.2-0.3 утгатай пикселийн тоо алгуур багасаж, 0.3-0.4 утгатай пикселийн тоо нэмэгдэх хандлага ажиглагдсан. NDVI-ийн урт хугацааны цуваа мэдээнд хийсэн дүн шинжилгээнээс үзвэл, судалгааны талбайн хэмжээнд NDVI-ийн утга өсөх хандлагатай байгаа хэдий ч, p<0.05 статистик үнэмшлийн түвшинд авч үзвэл, нийт нутгийн 46.3%-д статистикийн ач холбогдолтой өсөх, 0.1%-д статистикийн ач холбогдолгүй NDVI-ийн буурах хандлага ажиглагдсан бол сумын бусад нутагт NDVI-ийн өөрчлөлт статистикийн ач холбогдолгүй байна. Судалгааны талбайн хэмжээнд ургамал нөмрөг 0.0056 нэгж/жилээр өссөн дүнтэй байна. Хиймэл дагуулын 21 жилийн цуваа мэдээнд тулгуурлан тооцоход тус сумын ургамал нөмрөгт сайжрах хандлага илэрч байгаа хэдий ч, бусад судлаачдын уламжлалт хээрийн судалгааны аргаар хийж гүйцэтгэсэн судалгааны үр дүн ургамал нөмрөг доройтсон болохыг харуулж байна. Иймээс тандан судалгааны аргаар тооцоолсон ургамал нөмрөгийн төлөв байдал, өөрчлөлт, хандлагыг тодорхойлох судалгааны үр дүнг хээрийн мониторинг судалгаагаар нарийвчлан судлах нь зүйтэй юм. Түлхүүр үгс: Баянтал сум, ургамлын нормчилсон ялгаврын индекс (NDVI), ургамал нөмрөгийн өөрчлөлт

Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets

CONTEXTThe assimilation of Remote Sensing (RS) data into crop models improves the accuracy of yield predictions by considering crop growth dynamics and their spatial heterogeneity due to the different management practices and environmental conditions. OBJECTIVEThis study proposes a new method for performing sub-regional yield predictions (Nomenclature of territorial units for statistics, NUTS-3 level) using RS time series data and crop models. The primary objective was to release a procedure whereby the heterogeneity of the agricultural landscape (i.e. the agrophenotypes) observed from RS is used to drive crop model calibration. METHODSFine-resolution barley and maize distribution maps (100 m) and related crop calendars have been used to derive the “where” and “when” of the 8-day MODIS NDVI time series data, located in Apulia, Tuscany, and Veneto in 2018–2019. Principal Component Analysis and Hierarchical Clustering have been applied to NDVI seasonal profiles to identify the agrophenotypes, which have been used to derive crop growth and leaf area index dynamics. These data served as the reference to optimize the most relevant parameters of the WOFOST_GT model, using gridded weather data as input (0.25° resolution, Copernicus ERA5). Parameter distributions from multiple automatic calibrations have been sampled to characterize the agricultural heterogeneity within 22 NUTS-3 administrative units. Yield statistics from the Italian National Institute of Statistics have been used as reference data to test the accuracy of the yield simulation by the crop model WOFOST_GT. RESULTS AND CONCLUSIONSThe agrophenotypes reflected the wide north-south latitudinal gradient experienced in Italy by the two crops, leading to a gap of 15–30 days in barley and maize flowering and harvest dates across the study area. Average Nast-Sutcliffe modelling efficiency in reproducing LAI dynamics from RS (0.6) and Relative Root Mean Square Error (RRMSE) in predicting yield data (12.1% for barley, 3.7% for maize) in hindcast simulations demonstrated the effectiveness of our approach. The average RRMSE was reduced by 32.7% (barley) and 8.5% (maize) compared to baseline, decreasing by 0.7–1 Mg ha−1 absolute yield errors on both crops. SIGNIFICANCEThe inclusion of local agrophenotypes in the yield prediction workflow reduced errors in yield prediction compared to unsupervised simulations at NUTS-3 level. The script for agrophenotypes extraction and the model parameter sets are released to the scientific community, to foster improvements and further applications to other crops, ecoclimatic regions, satellite sensors and spatial scales.

Spatiotemporal Patterns of Land Surface Phenology from 2001 to 2021 in the Agricultural Pastoral Ecotone of Northern China

Vegetation phenology is one of the most sensitive indicators to understanding terrestrial ecosystem status and change. However, few studies have been conducted to reveal vegetation phenology variation characteristics over the past two decades, especially under the background of the global warming hiatus since 1998. The agricultural pastoral ecotone of northern China (APENC) is an ideal place to analyze land surface phenology (LSP) variation. Therefore, the spatiotemporal patterns of LSP were quantitatively analyzed at regional, basin and pixel scales based on time-series MODIS NDVI data (2001–2021). Results showed that: (i) The start of the growing season (SOS) occurred in 105–141 Julian days, the end of the growing season (EOS) was between 257 and 281 Julian days and the length of the growing season (LOS) varied from 130 to 172 days. The later SOS was mainly distributed in croplands and typical grassland areas, while the early SOS was observed in forests and sandy vegetation coverage areas. The early EOS occurred in typical grasslands, and the later EOS was concentrated in the southeast boundary. The magnitude of the SOS and LOS fluctuation was less than EOS. (ii) The SOS and EOS exhibited overall insignificant advanced and delayed trends at a rate of −0.09 days·yr−1 and 0.12 days·yr−1, respectively, and the LOS displayed an insignificant extended trend at a rate of 0.26 days·yr−1 at a regional scale. The trends of phenological metrics were consistent with the APENC in the Yellow River and Haihe River Basins. The shortened trend of LOS occurred due to the delayed SOS and advanced EOS in the Songliao River and Continental Basins. (iii) The SOS variation gradually changed from an advanced trend to a delayed trend from a southwest to northeast direction in cropland and grassland ecosystems, whereas an opposite trend was found for EOS. The LOS exhibited a significant extended trend due to the significant advanced and delayed trend of SOS and EOS at p < 0.01 in forest ecosystems. This work provides a critical reference for the vegetation phenology dynamic research of semi-arid and semi-humid regions.

Characterizing the Turning Points in Ecosystem Functioning and Their Linkages to Drought and Human Activities over the Arid and Semi-Arid Regions of Northern China

Identifying the changes in dryland functioning and the drivers of those changes are critical for global ecosystem conservation and sustainability. The arid and semi-arid regions of northern China (ASARNC) are located in a key area of the generally temperate desert of the Eurasian continent, where the ecological conditions have experienced noticeable changes in recent decades. However, it is unclear whether the ecosystem functioning (EF) in this region changed abruptly and how that change was affected by natural and anthropogenic factors. Here, we estimated monthly rain use efficiency (RUE) from MODIS NDVI time series data and investigated the timing and types of turning points (TPs) in EF by the Breaks For Additive Season and Trend (BFAST) family algorithms during 2000–2019. The linkages between the TPs, drought, the frequency of land cover change, and socioeconomic development were examined. The results show that 63.2% of the pixels in the ASARNC region underwent sudden EF changes, of which 26.64% were induced by drought events, while 55.67% were firmly associated with the wetting climate. Wet and dry events were not detected in 17.69% of the TPs, which might have been caused by human activities. TP types and occurrences correlate differently with land cover change frequency, population density, and GDP. The improved EF TP type was correlated with the continuous humid climate and a reduced population density, while the deteriorated EF type coincided with persistent drought and increasing population density. Our research furthers the understanding of how and why TPs of EF occur and provides fundamental data for the conservation, management, and better decision-making concerning dryland ecosystems in China.

Estimating Large-Scale Interannual Dynamic Impervious Surface Percentages Based on Regional Divisions

Impervious surface information is an important indicator to describe urban development and environmental changes. The substantial increase in impervious surface area will have a significant impact on the regional landscape and environment. Therefore, the timely and accurate acquisition of large-scale impervious surface percentage (LISP) is of great significance for urban management and ecological assessment. However, previous LISP estimation methods often ignored the impact of regional geographic environment and climate differences on remote sensing information, resulting in low overall accuracy and obvious regional differences in the estimated results. Thus, in this study, based on the time-series characteristics of multi-temporal remote sensing images combined with the information on geographical environment and climate heterogeneity, a method of time-series remote sensing image fusion and LISP estimation based on regional divisions was proposed. Firstly, the entire region was divided into several regions according to the spatial differences of Köppen–Geiger climate data and MODIS NDVI time-series data. Subsequently, adaptive time-series image fusion methods and remote sensing feature construction methods were proposed for different regions. Finally, the proposed method was used to estimate the percentage of impervious surfaces in other years in Asia. The results indicate that the overall R2 of each region is better than 0.82, and the estimation models have a good ability to transfer across time and can directly estimate the impervious surface percentage in other years without using additional samples. In addition, compared with other existing impervious surface products, the proposed method has higher overall estimation accuracy and regional consistency.

Analysis of vegetation change and its driving factors in Hunan province based on MODIS NDVI time series data

Analysis of vegetation change and its driving factors in Hunan province based on MODIS NDVI time series data

Assessment of Poplar Looper (Apocheima cinerarius Erschoff) Infestation on Euphrates (Populus euphratica) Using Time-Series MODIS NDVI Data Based on the Wavelet Transform and Discriminant Analysis

Poplar looper (Apocheima cinerarius Erschoff) is a destructive insect infesting Euphrates or desert poplars (Populus euphratica) in Xinjiang, China. Since the late 1950s, it has been plaguing desert poplars in the Tarim Basin in Xinjiang and caused widespread damages. This paper presents an approach to the detection of poplar looper infestations on desert poplars and the assessment of the severity of the infestations using time-series MODIS NDVI data via the wavelet transform and discriminant analysis, using the middle and lower reaches of the Yerqiang River as a case study. We first applied the wavelet transform to the NDVI time series data in the period of 2009–2014 for the study area, which decomposed the data into a representation that shows detailed NDVI changes and trends as a function of time. This representation captures both intra- and inter-annual changes in the data, some of which characterise transient events. The decomposed components were then used to filter out details of the changes to create a smoothed NDVI time series that represent the phenology of healthy desert poplars. Next the subset of the original NDVI time series spanning the time period when the pest was active was extracted and added to the smoothed time series to generate a blended time series. The wavelet transform was applied again to decompose the blended time series to enhance and identify the changes in the data that may represent the signals of the pest infestations. Based on the amplitude of the enhanced pest infestation signals, a predictive model was developed via discriminant analysis to detect the pest infestation and assess its severity. The predictive model achieved a severity classification accuracy of 91.7% and 94.37% accuracy in detecting the time of the outbreak. The methodology presented in this paper provides a fast, precise, and practical method for monitoring pest outbreak in dense desert poplar forests, which can be used to support the surveillance and control of poplar looper infestations on desert poplars. It is of great significance to the conservation of the desert ecological environment.

Driving forces of grassland vegetation changes in Chen Barag Banner, Inner Mongolia

ABSTRACT Inner Mongolia is an important ecological zone of northern China and 67% of its land area is grassland. This ecologically fragile region has experienced significant vegetation degradation during the last decades. Although the spatial extents and rates of vegetation change have previously been characterized through various remote sensing and GIS studies, the underlying driving factors of vegetation changes are still not well understood. In this study, we first used time-series MODIS NDVI data from 2000 to 2016 to characterize the temporal trend of vegetation changes. These vegetation change trends were compared with climate and socioeconomic variables to determine the potential drivers. We used a set of statistical methods, including multiple linear regression (MLR), spatial correlation analysis, and partial least squares (PLS) regression analyzes, to quantify the spatial distribution of the driving forces and their relative importance to vegetation changes. Results show that the main driving factors and their impact magnitude (weight) are in the order of human activities (r = -0.785, p < 0.01, VIP = 1.37), precipitation (r = 0.541, p < 0.05, VIP = 0.89), temperature (r = -0.319, p > 0.05 VIP = 0.59). The area affected by human activities was 10.57%. Specific human activities, such as coal mining and grazing were negatively associated with vegetation cover, while eco-engineering projects had positive impacts. This study provided thorough quantification of driving forces of vegetation change and enhanced our understanding of their interactions. Our integrated geospatial-statistical approach is particularly important for sustainable development of ecosystem balance in Chen Barag Banner and other areas facing similar challenges.

Spatial Variability in Length of Growing Period Using Meteorological and Space-based Data in Nagpur District of Maharashtra

The LGP was assessed using meteorological data (1988-2018) and MODIS-based NDVI data between 2000 to 2018. The meteorological data indicated that highest rainfall (1569 mm) was observed in Nagpur-Rural tehsil and the lowest rainfall was recorded in Narkhed tehsil (982 mm). Highest PET was observed in Hingna tehsil (1758 mm) and the lowest PET was recorded in Nagpur-Rural tehsil (1656 mm). The LGP map of Nagpur district generated using meteorological data showed that LGP ranged from 139 to 155 days. The pixel-wise LGP data computed using MODIS NDVI time series data has been reclassified in to 5 classes viz., 90-120, 120-150, 150-180, 180-210 and 210-240 days in which LGP class150-180 days occupied maximum area (40.7%) followed by 210-240 days (17.7%), 180-210 days (15.4%), 120-150 days (14.8%), whereas, the lowest area (11.8%) was under LGP class 90-120 days. LGP found in different landforms indicated that maximum LGP was in lower pediplains (46.4%) followed by pediments (21.7%) and plateau (11.1%). Lower LGP was observed in upper pediplains (7.1%), hills (5.4%), pediplain (hillside) (2.5%), scarp slope (1.9%), valley (1.6%) and gully land (0.9%). LGP with different land use/land cover indicate that maximum LGP was under agriculture (69.5%) followed by forest (23.3%) and scrubland (7.1%). Out of the 5 classes of LGP, 93.7 per cent of total agricultural area has LGP ranging from 120-240 days.

Understanding Spatio-temporal Pattern of Grassland Phenology in the western Indian Himalayan State

The present study has analysed grassland phenology: start of greening (SOG), end of greening (EOG) and length of greening (LOG), and their rate of change in the western Himalaya in India (Himachal Pradesh) using MODIS NDVI time series data (2001–2015). These metrics were inspected at different stratification levels: state, elevation, climatic zones and bio-geographic provinces. Delayed SOG was observed over 44.87% (P < 0.1), and delayed EOG over 63.3% (P < 0.1) of grassland grids. LOG was shortened in 24.37% (P < 0.1) and extended in 58.04% (P < 0.1) of the grids. At the state level, when statistically significant pixels (SSP) and all the pixels (AP) are used (given as SSP:AP), SOG is delayed by 20.27:6.28 days year−15, while EOG is delayed by 38.02:14.97 days year−15 and LOG is extended by 35.07:8.70 year−15 days. Extended LOG is observed over the temperate and cold arid regions, and shortened LOG is observed over sub-alpine and alpine regions. Variations in SOG and EOG are not uniform across different climatic and bio-geographic regions. However, in the sub-alpine and alpine zones, SOG and EOG followed elevation gradients, i.e. late SOG with early EOG over higher elevations, and early SOG with late EOG over lower elevations. Our study has revealed an interesting pattern of translational phenology (i.e. late SOG and late EOG) of grasslands which hints towards shifting winter period. Overall, it is observed that variations in timing of snowfall and snow cover extent are the reasons for inter-annual variations in the grassland phenology.

Object-based rice mapping using time-series and phenological data

Remote sensing techniques are often used in mapping rice, but high quality time-series remote sensing data are difficult to obtain due to the cloudy weather of rice growing areas and long satellite revisit interval. As such, rice mapping is usually based on mono-temporal Landsat TM/ETM+ data, which have large uncertainties due to the spectral similarity of different vegetation types. Moreover, conventional pixel-based classification method is unable to meet the required accuracy for rice mapping. Therefore, this study proposes a new strategy for mapping rice in cloud-prone areas using fused data of Landsat-8 OLI time-series and phenological parameters, based on the object-based method. We determine the critical growth stages of paddy rice from observed phenological data and MODIS-NDVI time-series data. The spatial and temporal adaptive reflectance fusion model (STARFM) is used to blend the MODIS and Landsat data to obtain a multi-temporal Landsat-like dataset for classification. Finally, an object-oriented algorithm is used to extract rice paddies from the Landsat-like, time-series dataset. The validation experiments show that the proposed method can provide high accuracy rice mapping, with an overall accuracy of 92.38% and a kappa coefficient of 0.85.

Differences in ecosystem water-use efficiency among the typical croplands

Water use efficiency (WUE) is an important parameter to assess agricultural production and the reasonable utilization of water resources. Especially in the context of changing hydrological environment, more attention need to be paid on how to use limited water resource to improve crop yield for ensuring food security. Based on 33 site-years of flux measurements over 10 cropland sites using the eddy covariance (EC) technique, the study systematically evaluated the large differences in seasonal and interannual variations of gross primary productivity (GPP), evapotranspiration (ET) and ecosystem WUE across the four crops worldwide including soybean, maize, winter wheat and paddy rice. The lengths of the growing seasons across the main crops extracted from time-series MODIS NDVI data, implied that the longest growth period in winter wheat and the shortest growing season in paddy rice field. Further analyses suggest that maize cropland has the strongest ecosystem WUE with 2.48 ± 0.69 g C kg−1 H2O, followed by winter wheat (2.00 ± 0.39 g C kg−1 H2O), soybean (1.92 ± 0.52 g C kg−1 H2O), and paddy rice (1.88 ± 0.63 g C kg−1 H2O). Meanwhile, the variability in ecosystem WUE exhibited apparent seasonality, and peaked together with GPP in the most active summertime. A series of biotic and abiotic factors affected the GPP as well as WUE variability. Given the complicated interactions among these environmental factors, this study revealed the great potential to remotely retrieve the WUE variability using time-series MODIS NDVI data over large areas. Ecosystem WUE of the C4 crop –maize was obviously higher than the other three C3 crops. Engineering C4 feature into C3 crops may be a feasible way to increase photosynthesis and yield.

Quantifying the relative impacts of climate and human activities on vegetation changes at the regional scale

Understanding human and climate-induced vegetation changes could benefit regional ecological management. In this paper, a framework was established to quantify the relative contribution rates of human and climate factors to vegetation changes in Chongqing, China, from 2000 to 2015. MODIS NDVI time series data were collected, and land use data were produced using Landsat TM/OLI images. A combined analysis of land use and vegetation changes was conducted, and the residual trends method (RESTREND) was explored in the framework. The results showed that Chongqing experienced a significant vegetation increase from 2000 to 2015. The relative contribution rates of human activities and climate to the vegetation changes were 90.96% and 9.04%, respectively. These results indicate that human activities had a dominant role in vegetation restoration in Chongqing in 2000–2015. Throughout the study period, extensive land use changes occurred in Chongqing. During the study period, persistent forested land, farmland-forested land (a land use conversion type), persistent farmland, and grassland-forested land were the four most important land use (changed and unchanged) types attributed to the growing season NDVI (GSN) increases in Chongqing from 2000 to 2015. The contributions of these four land use types were 45.03%, 21.19%, 16.79% and 12.60%, respectively. The farmland-forested land was characterized as the most effective land use conversion type for vegetation restoration. The proposed framework allows for human and climate-induced vegetation changes to be quantitatively distinguished at the regional scale and provides the contribution rates of each changed and unchanged land use type. The framework is expected to be useful for regional ecological management and research.

Prediction of vegetation dynamics using NDVI time series data and LSTM

Understanding and analyzing the changes in vegetation cover is very important in several aspects including climatic changes, water budget, ecological balance and specially to undertake necessary conservation measures. The concept of neural network has gained much significance in the analysis of vegetation dynamics using remote sensing satellite data. In the current study an attempt has been made to predict the vegetation dynamics using MODIS NDVI time series data sets and long short term memory network, an advanced technique adapted from the artificial neural network. The dataset of 861 NDVI images from January 2000 to June 2016 is used for making the time series. The data is segregated into three sets which comprises of training set (70%), validation set (20%), and testing set (10%). To check the reliability of the experiment we have finalised two different regions after extensive research for investigation. These include different terrains in the Great Nicobar Islands, one region along the coast where vegetation has severe ecological damage due to 2004 Indian Ocean tsunami and the other, an interior region which remained imperturbable during the tsunami. Long short term memory network, an advanced neural network is trained with these NDVI values for both the regions separately to predict the future vegetation dynamics. To measure the accuracy of the LSTM network, root mean square error is calculated. The resulting plots from both the experiments indicate that the long short-term memory neural network follows the series in addition to coinciding with the required time series. Also, an unanticipated change in the trend of the NDVI series were well adapted by the network and was able to predict the future NDVI values with good accuracy maintaining RMSE less than 0.03 without providing any supplementary data. By adopting the prescribed method in the paper, anticipation of vegetation changes can be done accurately much ahead of time and take proactive measures accordingly to safeguard and improve the vegetation in any area.

Spatio-temporal dynamics of NDVI and its response to climate factors in the Heihe River Basin, China

Ecosystem in the Heihe River Basin (HRB) is very vulnerable and sensitive to variations in the climatic conditions and human activities. In this study, The SPOT and MODIS NDVI time series data from 1998 to 2012 were used to analyze NDVI variation characteristics and its response to climatic factors in the HRB based on Mann–Kendall test, relative Sen’s slope, linear regression and partial correlation analysis. The results indicate that: (1) There is an obvious decreasing trend during 1998-2001 and a significantly increasing trend from 2001 to 2012. The vegetation in the HRB was improved by 1.5% of annual average level from 1998 to 2012, and rate of change is greatest in the lower reaches. (2) During 1998-2001, the rainfall is proved to be the most significant factor influencing NDVI for each reaches, and temperature and radiation are also related significantly with NDVI in the middle and lower reaches respectively. However, during 2001-2012, all climatic factors are not significantly related with NDVI for each reaches, excepting rainfall in the middle reaches. This demonstrates that the climatic variation cannot totally explain the significant improvement of vegetation during 2001-2012 in the HRB, whereas ecological restoration projects implemented in this period might be a reasonable factor for the improvement of ecological environment in the HRB.

念青唐古拉山南坡高寒草甸生产力对温度和降水变化的敏感性及其海拔分异

PDF HTML阅读 XML下载 导出引用 引用提醒 念青唐古拉山南坡高寒草甸生产力对温度和降水变化的敏感性及其海拔分异 DOI: 10.5846/stxb201606141147 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 中国科学院先导专项B"青藏高原多圈层相互作用及资源环境效应"子课题（XDB03030402）；国家自然科学基金面上项目（31170451）；国家973计划课题"全球变化影响下自然生态系统的脆弱性及评价指标"（2010CB951301） Altitudinal variations in the sensitivity of alpine meadow productivity to temperature and precipitation changes along the southern slope of Nyainqentanglha Mountains Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:目前人们仍不清楚不同海拔高寒草地植被生长对气候变化的敏感性差异及其与最适宜海拔分布中心的关系。利用西藏当雄县念青唐古拉山南坡7个海拔梯度固定样地的高山嵩草草甸地上净初级生产力（ANPP）观测数据（2009-2013），建立了ANPP与同期遥感植被指数（MODIS NDVI）的线性回归方程。基于长时间序列的NDVI数据，利用建立的回归方程估算了研究区2000-2013年的ANPP。结合沿海拔梯度的HOBO气象站数据（2006-2013）及当雄县气象站数据（2000-2013），分析了2000-2013年该地区高寒草甸ANPP对降水和温度变化的敏感性及其随海拔的变化规律。结果表明：（1）多年平均ANPP随海拔的变化均表现为先增加后降低的单峰分布格局，最大值出现在海拔4893-4942 m，说明在海拔梯度上存在一个最适宜高寒草甸植被生长的分布中心；（2）ANPP与生长季降水量（GSP）呈正相关关系，与生长季平均气温（GST）呈负相关关系，其相关斜率的绝对值（指示ANPP的降水敏感性和温度敏感性大小）与ANPP的海拔格局具有相反的变化趋势，即在最适宜高寒草甸植被生长的海拔分布中心附近，ANPP对降水和温度变化的敏感性最低，而在远离该分布中心的较高和较低海拔，ANPP对降水和温度变化的敏感性则相对较大。研究明确了高寒草甸ANPP对降水和温度变化的敏感性随海拔的分异性及其与高寒草甸最适宜海拔分布中心的关系，这有助于理解沿海拔梯度不同水热组合环境下高寒生态系统对未来气候变化的响应模式。 Abstract:It remains unclear whether and how the sensitivity of alpine plant growth to climate change may vary with distance away from the altitudinal distribution center of a vegetation type. On the basis of measurements of aboveground net primary productivity (ANPP) in an alpine Kobresia meadow across 7 altitudes along the southern slope of Nyainqentanglha Mountains in Tibet during 2009-2013, we established a linear regression model between ground ANPP and satellite vegetation index (MODIS NDVI). Using the long time-series MODIS NDVI data, we then applied the established model to estimate ANPP in the study area during 2000-2013. In combination with climatic data obtained from altitudinal HOBO weather stations (2006-2013) and Damxung weather station (2000-2013), we aimed to clarify the altitudinal pattern in the sensitivity of ANPP to changes in temperature and precipitation in alpine meadows. A unimodal pattern in multi-year mean ANPP was observed along the altitudes; ANPP increased with increasing altitude up to 4893-4942 m and then decreased, demonstrating the altitudinal distribution center of the alpine meadow at mid-altitude. ANPP was positively correlated with growing season precipitation but negatively correlated with growing season mean air-temperature during 2000-2013. The absolute values of the linear regression slopes (referred to as the sensitivity of ANPP to changes in precipitation and temperature) showed a pattern opposite to the change in ANPP along the altitudes. The precipitation and temperature sensitivities of ANPP were the lowest around the distribution center but higher at lower or higher altitudes with distance away from the distribution center. In conclusion, our data identified the altitudinal pattern in sensitivity of alpine meadow ANPP to climate change with a link to the altitudinal distribution center of the alpine meadow. Such knowledge is important for understanding the response of alpine meadow ecosystems to future climate change in different environments along an altitudinal gradient. 参考文献 相似文献 引证文献

Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data.

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification.

Spatial-temporal process and characteristics of vegetation recovery after Wenchuan earthquake: A case study in Longxi River basin of Dujiangyan, China.

Quantitative evaluation on the vegetation recovery after the earthquake is of great scienti-fic significance for local ecological system rebuilding and regional social-economic sustainable deve-lopment. By taking the Longxi River Basin in Dujiangyan of Sichuan Province as an example, and employing the MODIS-NDVI time-series data, this paper illustrated the dynamic changes of the ve-getation cover rates in Longxi River basin before and after the 5.12 Wenchuan Earthquake by using Landsat data, and then time-space characters of the recovery rates of the damaged vegetation was quantified structurally by referring the factors of river-system and terrain. The results showed that the recovery of impaired vegetation coverage worked well from a holistic point of view, but the response of vegetation coverage to earthquake disaster showed a lagging-phenomenon. The recovery rate of damaged vegetation was significantly correlated to its distance to surrounding river systems, elevation, slope degree, and slope aspect. This finding could provide technical supports for decision-makers to intervene the recovering process after earthquake.

Extracting the Spatiotemporal Pattern of Cropping Systems From NDVI Time Series Using a Combination of the Spline and HANTS Algorithms: A Case Study for Shandong Province

Multiple cropping systems are beneficial for grain yields. Monitoring the spatial and temporal variations in cropping systems is important for evaluating food production and making scientific decisions for agricultural development. Satellite-derived NDVI time series can describe crop metrics (e.g., seeding, jointing, and harvesting) and can be used to obtain cropping information. In this study, rather than statistical data, MODIS NDVI time-series data at a resolution of 250 m for 2000, 2005, and 2010 were used to extract the cropping index in Shandong province. A method combining the spline interpolation and HANTS algorithms was proposed to reconstruct high-quality NDVI time series. The results showed that the proposed method can compensate for the weaknesses of the individual algorithms and effectively reduce the noise in NDVI time series. The NDVI-derived cropping index using the proposed method showed a high degree of consistency with statistical data and field sample data. The errors in the cropping index were due to land use changes and mixed crop types within a pixel. Croplands with 2 crops per year were mainly located mainly in the western plain, whereas croplands with 1 crop were found throughout Shandong province, most predominantly in the north, the middle mountainous regions, and the eastern hilly regions. The cropping index was largest in 2000 and smallest in 2010. The interannual variations are caused mainly by social and economic factors such as economic profit and crop rotation.