Normalized Difference Vegetation Index Trends Research Articles

Identifying trend shifts in vegetation greenness in China from 1982 to 2015

AbstractUnderstanding vegetation evolution is critical for exploring changes in terrestrial ecosystems and identifying future challenges. However, analyses that simultaneously examine trend shifts in vegetation greenness at the national, regional, biome, and pixel scales in China are still rare. Using long‐term (1982–2015) satellite data and the breaks for additive season and trend (BFAST) technique, we identified breakpoints in normalized difference vegetation index (NDVI) in China. The results showed that the annual mean NDVI gradually increased. A total of 68.8% of the vegetated area exhibited upward trends in NDVI, most of which was distributed in Southeast China and the Loess Plateau. Changes in NDVI trends occurred in 78.7% of the total vegetated areas, while hotspots were concentrated in Northwest and North China. A rapid increase in breakpoints was detected after 1999, mainly concentrated in North and Northwest China, and corresponding to the times and areas with the highest ecological engineering efforts. Positive shifts in NDVI trends were more common and generally distributed on the eastern side of the Hu Huanyong line, while browning (negative) shifts were mainly distributed on the western side and were gradually expanding, indicating a possible tendency toward environmental degradation. Although unstable vegetation areas that underwent land cover type changes had higher frequencies of breakpoints, the proportion of stable vegetation experiencing NDVI trend shifts was higher after 2000, probably because human intervention buffered external disturbances in unstable areas. Identifying hotspot areas of shifts in vegetation greenness can help setting priorities when taking measures for ecosystem conservation, thus provide scientific reference for sustainable land development.

Coupling coordination relationship between geology-geomorphology and ecology in Northeast China.

Northeast China is an important ecological barrier and commodity grain base in China. The coupling coordination relationship between geology–geomorphology and ecology has become a critical background condition for ecosystem protection and sustainable development. Taking Northeast China as a case (accounting for about 13% of China’s land area), 9 divisions are divided according to the characteristics of regional ecology and geology–geomorphology, and 17 indicators are selected to build an evaluation index system. Methods of analytic hierarchy process, entropy weight and game theory are used to determine the index weights. Based on the coupling coordination degree (CCD) model, the spatial coupling coordination characteristics of geology–geomorphology and ecology are studied. The variation characteristics of the Normalized Difference Vegetation Index (NDVI) are evaluated by Sen+Mann–Kendall (Sen+MK) method. Our results are as follows. (1) The coupling between geology–geomorphology and ecology is strong, but the spatial differentiation of CCD is obvious. Nine divisions are evaluated as two high–level, three medium–level and three low–level coordination types and one mild imbalance type. (2) The plain divisions Ⅰ and Ⅳ where the typical black soil belt is located are high coordination types. Restricted by geology–geomorphological conditions or ecological conditions, mountain divisions Ⅲ and Ⅶ and plain division Ⅴ are moderate coordination types, mountain divisions Ⅱ and Ⅷ and plateau division Ⅸ are low coordination types, and mountain division Ⅵ is mild imbalance type. (3) The variation trend of NDVI shows a significant increase in divisions Ⅲ, Ⅴ, Ⅰ, Ⅱ and Ⅶ. it shows a significant decrease in part of divisions Ⅳ, Ⅵ, Ⅷ and Ⅸ, and ecological management and construction should be strengthened in these divisions. The research shows that the CCD model method is feasible for evaluating the relationship between geology–geomorphology and ecology and can provide eco–geological background information for Northeast China.

Integrating Landscape Connectivity and Natural-Anthropogenic Interaction to Understand Karst Vegetation Restoration: A Case Study of Guizhou Province, China

Because of implementation of ecological projects, the restoration of vegetation not only changes the typological composition and spatial structure of the landscape, but also improves the regional ecosystem function. The present study considered the effects of natural-anthropogenic factors and landscape connectivity on vegetation restoration. It also explored the impact and underlying mechanisms by which structural changes in landscape connectivity affect vegetation coverage in the karst region of Guizhou Province and provided a novel perspective for the maintenance of regional ecological security. We used morphological spatial pattern analysis (MSPA) and integrated valuation of ecosystem services and tradeoffs (InVEST) and circuit theory to identify ecological networks and explore the changes in landscape structure. We performed a Theil-Sen Median trend analysis and a Mann-Kendall (MK) trend test to determine spatiotemporal variations in vegetation coverage. We conducted a coupling analysis to discover correlations between the average cumulative current density (CCD) and the normalized difference vegetation index (NDVI) in various karst landform counties. We also implemented a geographical detector to detect the factors affecting the NDVI trend and disclose interactions among factors. The results showed that (1) Though the total area of forests and core areas was reduced, the ecological networks and landscape connectivity steadily improved. (2) Areas with improved vegetation coverage accounted for 77.77% of the total. By contrast, the degraded areas covered 14.28% while the remaining 7.95% was stable. (3) The relationships between the average CCD and the NDVI were inconsistent among various geomorphological counties. The counties with the highest proportions of karst landforms presented with negative correlations between the average CCD and the NDVI in 2005 and positive correlations between these parameters in 2018. (4) The NDVI trend was influenced by several factors. Of these, anthropogenic activity played a dominant role. Nevertheless, changes in landscape connectivity was also implicated. Attention should be paid to the impact of landscape connectivity on ecological restoration. The foregoing results indicated that the rocky desertification projects effectively improved landscape connectivity and vegetation coverage and provided a reference for developing policies establishing and maintaining ecological security of the karst ecosystem and coordinating sustainable development in this region.

Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

This study aims to investigate how alternations of the land surface temperature (LST) affects the normalized difference vegetation index (NDVI) in Paphos forest, Cyprus, using Landsat-5 and Landsat-8 imagery for the time periods 1993–2000 and 2013–2018, respectively. A total of 262 Landsat images were processed to compute the mean monthly NDVI and LST values and create a time series. Using the Cook’s distance, the effect of missing values in the analysis of the time series were examined. Results from the cross-correlation and cross-variograms, decomposition model, and the BFAST algorithm were compared to produce reliable conclusions on forest changes and satellite, meteorological, and environmental data were combined to interpret the changes that occurred inside the forest. The decomposition analysis showed a decrease of 2.7% in the LST for the period 1993–2000 and an increase of 4.6% in the LST during the period 2013–2018. The NDVI trend is negatively correlated to the LST trend for both time periods. An increase in the LST trend was identified in November 1998 as well as in the NDVI trend in October 1994 and May 2014 that was caused by favorable climatic conditions. An increase in the NDVI trend from May 2014 to December 2015 may be related to reduced pityocampa attacks. An abrupt decrease was detected in December 2015 that was probably caused by the locust invasion that occurred in the island earlier that year. A positive correlation appears for LST and NDVI variables for time lags 4, 5, 6, 7, and 8 months. Overall, it was shown that LST and NDVI analysis is very promising for identifying potential forest decline.

Effects of multi-temporal scale drought on vegetation dynamics in Inner Mongolia from 1982 to 2015, China

Climate change is causing severe changes in the ecosystem, thus understanding how plants respond to drought is essential to protecting ecosystems in Inner Mongolia. Multi-temporal variations of drought in Inner Mongolia were studied using the Standardized Precipitation Evapotranspiration Index (SPEI), and the vegetation growth was analyzed using the Normalized Difference Vegetation Index (NDVI). Moreover, the correlation between droughts of different lengths and NDVI was studied. Subsequently, we developed an optimal binary probability model to calculate the probability of vegetation productivity loss at different drought levels using the copula method. The results showed: (1) a weak aridification in the western parts and significant wetness in the central and eastern parts of Inner Mongolia; (2) a significant decreasing trend of NDVI from 1982 to 2009 and a significant increasing trend from 2010 to 2015; (3) a strong positive correlation between NDVI and SPEI and a difference between their related time scales based on soil textures; and (4) a higher probability of vegetation productivity loss with increasing drought at different drought levels.

NDVI-based ecological dynamics of forest vegetation and its relationship to climate change in Romania during 1987–2018

Forests have become increasingly stressed by climate change, including in Romania over recent decades, but their response to climate dynamics has not yet been analysed in this country. This study aims to investigate, for the first time, recent ecological changes in forests across Romania, in relation to climate dynamics that affected the country from 1987 to 2018. To this end, countrywide remote sensing (Landsat) data were processed for forest boundaries over the 32 years, in order to compute annual (summer season data) Normalized Difference Vegetation Index (NDVI) datasets, which were subsequently investigated as trends using the Mann-Kendall test and Sen's slope estimator. Simultaneously, various climatic data (temperature, precipitation and reference evapotranspiration) were processed through interpolation techniques and via the same two statistical tools, and were subsequently used for exploring the impact of climate change on Romanian forestlands after 1987. The results highlighted general greening (increasing NDVI) trends of forests nationally (65% of all NDVI changes, which total over 30.000 km2 across Romania), which were dominated by widespread positive NDVI trends detected in the Carpathians region of Romania. This general ecological dynamic suggests a possible enhancement in vegetation productivity in the country’s high-altitude areas. Contrasting browning (decreasing NDVI) trends were found for 35% of Romanian forestlands marked by NDVI changes, especially apparent in the Extra-Carpathians (lowland) region, which indicates that in these cases forests were degraded or devitalized. However, the statistical significance of both greening and browning trends is limited across the country. The analysis of climatic trends and of correlations between annual NDVI and climate data indicated that recent warming throughout Carpathians may be an important driving force of forest greening in temperature-limited mountain regions. This finding is supported by an at least moderate intensity of air temperature – NDVI relationships (r correlation coefficient values of ∼50%, the highest of all eco-climatic relationships analysed), generally detected throughout mountain environments. At the same time, it seems that evapotranspiration increase accounted at least in part for forest browning in lowland areas, while the impact of precipitation in forest ecological dynamics remains unclear. All these findings can be useful for a better forest management under the future climate change conditions in Romania.

Contrasting 20-year trends in NDVI at two Siberian larch forests with and without multiyear waterlogging-induced disturbances

The fate of a boreal forest may depend on the trend in its normalized difference vegetation index (NDVI), such as whether the NDVI has been increasing significantly over the past few decades. In this study, we analyzed the responses of two Siberian larch forests at Spasskaya Pad and Elgeeii in eastern Siberia to various waterlogging-induced disturbances, using satellite-based NDVI and meteorological data for the 2000–2019 period. The forest at Spasskaya Pad experienced waterlogging (i.e. flooding events caused by abnormal precipitation) during 2005–2008 that damaged canopy-forming larch trees and increased the abundance of water-resistant understory vegetation. By contrast, the forest at Elgeeii did not experience any remarkable disturbance, such as tree dieback or changes in the vegetation community. Significant increasing NDVI trends were found in May and June–August at Elgeeii (p < 0.05), whereas no significant trends were found at Spasskaya Pad (p > 0.05). NDVI anomalies in May and June–August at Elgeeii were significantly associated with precipitation or temperature depending on the season (p < 0.05), whereas no significant relationships were found at Spasskaya Pad (p > 0.05). Thus, the 20 year NDVI trend and NDVI–temperature–precipitation relationship differed between the two larch forests, although no significant trends in temperature or precipitation were observed. These findings indicate that nonsignificant NDVI trends for Siberian larch forests may reflect waterlogging-induced dieback of larch trees, with a concomitant increase in water-resistant understory vegetation.

Vulnerability assessment and its driving forces in terms of NDVI and GPP over the Loess Plateau, China

With the impact of climate change and the implementation of the “Grain-for-Green” ecological restoration project, the dynamics of water and heat transport and storage in the Loess Plateau (LP) have changed substantially. However, studies on the vulnerability of the LP ecosystem in response to ecological restoration are less concerned. Based on the normalized difference vegetation index (NDVI) and the gross primary production (GPP), this study assessed the spatial change of ecological vulnerability (EV) in the LP during 2001–2019. Results showed that (1) the annual mean NDVI and GPP was high in the southeast of LP and low in the northwest. The trend of NDVI and GPP presented a significant upward trend over approximately 85% of the regions; (2) the ecological vulnerability of LP displayed a low-to-high gradient from southeast to the northwest. Medium (0.3–0.45) and heavy (≥0.45) vulnerability levels occurred in the northwest of LP, which was attributed to less vegetation and serious land degradation; (3) forestland has the lowest vulnerability whereas grassland, sparse vegetation and urban land exhibited higher vulnerability, with the respective percentages of vulnerable areas of 63.8%, 90.4%, 70% in terms of NDVI and 87.1%, 49.8%, 38.4% in terms of GPP; and (4) the EV of LP was impacted by climate and terrain. The magnitude of vulnerability was negatively correlated with precipitation, temperature and slope, and positively correlated with drought. It is suggested that precipitation, slope and drought stress were the dominant factors of EV. The findings provide guidance for appropriate planning of ecological restoration in the LP.

Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin

Vegetation regulates the exchange of terrestrial carbon and water fluxes and connects the biosphere, hydrosphere, and atmosphere. Over the last four decades, vegetation greening has been observed worldwide using satellite technology. China has also experienced a notably widespread greening trend. However, the responsiveness of vegetation dynamics to elevated CO2 concentration, climate change, and human activities remains unclear. In this study, we attempted to explore the impact of natural (precipitation, air temperature), biogeochemical (CO2), and anthropogenic drivers (nighttime light, afforestation area) on changes in vegetation greenness in the Haihe River Basin (HRB) during 2002–2018 at the county-level. We further determined the major factors affecting the variation in satellite-derived normalized difference vegetation index (NDVI) from moderate resolution imaging spectroradiometer (MODIS) for each county. The results indicated that over 85% of the counties had a significantly increased NDVI trend, and the average linear trend of annual NDVI across the study region was 0.0037 per year. The largest contributor to the NDVI trend was CO2 (mean contribution 45%), followed by human activities (mean contribution of 27%). Additionally, afforestation was a pronounced driving force for NDVI changes in mountainous areas, resulting from ecosystem restoration efforts. Our findings emphasize the crucial role of CO2 fertilization in vegetation cover change, while considering CO2 concentration, climate change, and human activities, and shed light on the significant influences of afforestation programs on water resources, especially in mountainous areas.

Landsat-based multi-decadal spatio-temporal assessment of the vegetation greening and browning trend in the Eastern Indian Himalayan Region

The Eastern Indian Himalayan region (EIHR) is biodiversity-rich and ecologically sensitive. The region is under tremendous pressure due to a variety of challenges such as population pressure, shifting cultivation, and climate change leading to impact the vegetation. Therefore, the present study focuses on the evaluation of spatio-temporal dynamics of vegetation utilizing the trend and rate of change of annual median NDVI (Normalized Difference Vegetation Index) of EIHR. The annual median NDVI composite of EIHR was prepared for each year from 1990 to 2019 using Landsat satellite data collection available for the entire year in Google Earth Engine (GEE). The time series annual median NDVI data were analysed for the non-parametric (Mann-Kendall and Theil-Sen) trend analysis. Based on these trends, regions of greening (increasing NDVI) and browning (decreasing NDVI) was assessed. It revealed that the majority of the EIHR has a more greening trend than browning with an annual rate of increase of NDVI between 0.002 and 0.007 with a very high (p<0.01) to high (p<0.05) level of confidence. Further, the NDVI trend was also analysed for its spatial distribution in forest type and forest cover of EIHR. It was found that while about 58% of the total forest area has shown a continuously increasing NDVI trend, only less than 1% area has a decreasing trend. The increase in the forest area and greening trends in NDVI may be attributed to the conservation efforts. The outcome of the study may help in the better management of forest ecosystems besides the future planning for landscape and regional development.

内蒙古大青山国家级自然保护区植被归一化指数时空变化及其与环境因子的关系

PDF HTML阅读 XML下载 导出引用 引用提醒 内蒙古大青山国家级自然保护区植被归一化指数时空变化及其与环境因子的关系 DOI: 10.5846/stxb202108072161 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 内蒙古自治区科技重大专项项目（zdzx2018060，2019ZD007）；内蒙古自治区退耕还林生态效益监测项目 Spatio-temporal variation of NDVI and influencing factors in the Daqing Mountain National Nature Reserve, Inner Mongolia 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:选取2000-2020年MODIS影像的归一化指数（NDVI）数据及同期的气象数据，采用趋势分析、变异系数、相关分析等方法，从海拔、坡度和坡向的角度分析了内蒙古大青山国家级自然保护区生长季NDVI的时空变化特征，并进一步探讨了NDVI时空变化与气候因子的相关性。结果表明：（1）2000-2020年，内蒙古大青山国家级自然保护区植被总体覆盖状况良好且呈增长趋势，增长速率为0.058/10a；（2）保护区NDVI空间分布呈现出西部低、东部高的从西到东逐渐增加的特征。保护区生长季NDVI均值随着海拔的升高而明显增加，随着坡度的增加呈缓慢增加趋势，在不同坡向上，生长季NDVI均值大小依次为阴坡 > 半阴坡 > 半阳坡 > 阳坡；（3）从NDVI变化趋势分析来看，2000-2020年大青山自然保护区植被覆盖改善的面积占总面积的61.75%，其中，显著改善、极显著改善面积分别占总面积的41.13%、20.62%，基本保持不变的面积占总面积的38.17%，退化面积占总面积的0.07%。在空间分布上，NDVI显著、极显著增加的区域主要分布在保护区西部和东部，中部以变化不显著为主。保护区各功能区NDVI变化趋势看，NDVI极显著增加、显著增加面积百分比大小顺序为实验区 > 核心区 > 缓冲区；（4）2000-2020年，稳定性整体表现为"不同波动变化并存，较低、中等和较高波动变化居多，低波动和高波动变化面积占比相对较小"的特点，高波动变化区域主要分布在保护区西部、中部和实验区，人为影响和降水量是影响稳定性的主要驱动因素；（5）大青山自然保护区生长季NDVI与年均降水量以正相关为主，正相关面积比例达到99.79%，与温度相关性不显著为主，不显著面积占总面积的97.91%，降水量是影响大青山自然保护区植被覆盖的最主要气象因子。 Abstract:Based on the Normalized Difference Vegetation Index (NDVI) data of Moderate Resolution Imaging Spectroradiometer (MODIS) image and the relative meteorological data from 2000 to 2020, the temporal and spatial variation characteristics of NDVI in the growing season of Daqing Mountain National Nature Reserve in Inner Mongolia are analyzed from the perspective of elevation, slope and slope aspect by using the methods of trend analysis, coefficient of variation and correlation analysis. The correlation between temporal and spatial variation of NDVI and climate factors was further explored. The results show that:(1) The overall vegetation coverage of Daqing Mountain National Nature Reserve was good and growing from 2000 to 2020, with a growth rate of 0.058/10a. (2) The spatial distribution of the NDVI shows the characteristics of low in the west and high in the east, and gradually increasing from west to east of the reserve. With the increase of elevation, the mean value of NDVI in the growing season of the reserve increased significantly, but increased slowly with the increase of the slope. In different slope aspects, the mean value of NDVI in growing season was shady slope > semi-shady slope > semi-sunny slope > sunny slope. (3) From the analysis of NDVI change trend in 2000-2020, the area of vegetation coverage improvement in Daqing Mountain National Nature Reserve accounted for 61.75% of the total area, of which the areas of significant improvement and extremely significant improvement accounted for 41.13% and 20.62% of the total area, respectively, the area basically unchanged accounted for 38.17% of the total area, and the degraded area accounted for 0.07% of the total area. In terms of spatial distribution, the areas with the significant and extremely significant increase in NDVI were mainly distributed in the west and east of the reserve, and the change was not significant in the middle. According to the changing trend of NDVI in each functional area of the reserve, the NDVI increased significantly, and the area percentage increased significantly in the order of experimental zone > core zone > buffer zone. (4) From 2000 to 2020, the overall performance of stability is characterized by the coexistence of different fluctuations, the majority of low, medium and high fluctuations, and the proportion of low and high fluctuations being relatively small. The high fluctuations are mainly distributed in the western and central areas of the reserve and the experimental zone, and human disturbance and precipitation are the main driving factors. (5) The average annual maximum NDVI in the growing season of Daqing Mountain National Nature Reserve is mainly positively correlated with the average annual precipitation, and the proportion of the positive correlation area reaches 99.79%. It is mainly insignificant with temperature, and the insignificant area accounts for 97.91% of the total area. Precipitation is the main meteorological factor affecting the vegetation coverage of Daqing Mountain National Nature Reserve. 参考文献 相似文献 引证文献

Emerging hot spot analysis and the spatial–temporal trends of NDVI in the Jing River Basin of China

As an important indicator of vegetation coverage, the normalized difference vegetation index (NDVI) reflects the changing pattern and evolving trend of the environment. In the Loess Plateau, vegetation plays a critical role in soil and water conservation, which strongly affects the achievement of sustainable development goals. The study of the spatial distribution and temporal trends of NDVI is of great practical importance for the planning of soil and water conservation measures and the evaluation of the environmental situation. In this study, the NDVI, precipitation, land use and land cover data of the Jing River Basin were collected, the emerging hot spot patterns of the NDVI analyzed, the characteristics of spatial distribution and temporal variation of the NDVI in the basin obtained, and the impacts on NDVI from the climate changes and the land cover changes discussed. The results show that the NDVI in Jing River Basin represents a spatial trend of decreasing from northwest to southeast. The emerging hot spot analysis results show that diminishing cold spot, oscillating hot spot and intensifying hot spot are predominant patterns in the basin. The whole basin shows a statistically significant upward trend of high value aggregation of NDVI. The temporal trend of NDVI in the basin varies from − 0.0171 to 0.0185 per year. The increasing trend of vegetation coverage in the basin is statistically significant. The positive correlation between the NDVI and the precipitation mainly observed upstream of the basin reveals that the growth of vegetation in the Loess Plateau is more dependent on the water supply from the precipitation. Land cover transition patterns and the land use patterns also impact the spatial–temporal trends of the vegetation coverage in the basin. The study results may be helpful for the vegetation restoration, soil and water conservation and sustainable development of the Jing River Basin.

Quantitative Assessment of the Contributions of Climate Change and Human Activities to Vegetation Variation in the Qinling Mountains

Quantitative assessment of the contributions of climate change and human activities to vegetation change is important for ecosystem planning and management. To reveal spatial differences in the driving mechanisms of vegetation change in the Qinling Mountains, the changing patterns of the normalized difference vegetation index (NDVI) in the Qinling Mountains during 2000–2019 were investigated through trend analysis and multiple regression residuals analysis. The relative contributions of climate change and human activities on vegetation NDVI change were also quantified. The NDVI shows a significant increasing trend (0.23/10a) from 2000 to 2019 in the Qinling Mountains. The percentage of areas with increasing and decreasing trends in NDVI is 87.96% and 12.04% of the study area, respectively. The vegetation change in the Qinling Mountains is caused by a combination of climate change and human activities. The Tongguan Shiquan line is a clear dividing line in the spatial distribution of drivers of vegetation change. Regarding the vegetation improvement, the contribution of climate change and human activities to NDVI increase is 51.75% and 48.25%, respectively. In the degraded vegetation area, the contributions of climate change and human activities to the decrease in NDVI were 22.11% and 77.89%, respectively. Thus, vegetation degradation is mainly caused by human activities. The implementation of policies, such as returning farmland to forest and grass, has an important role in vegetation protection. It is suggested that further attention should be paid to the role of human activities in vegetation degradation when formulating corresponding vegetation protection measures and policies.

Spatio-temporal dependency of vegetation dynamics on climatic variables during 1982–2015 over India

The abnormally changing climate has a direct or indirect impact on the vegetation dynamics. The main objective here is to understand how the vegetation dynamics respond to climate change during the historical period, which could act as a clarion call for the future assessment as well. To accomplish the same, role of potential climatic variables viz., solar radiation, precipitation, maximum and minimum temperatures on changing normalized difference vegetation index (NDVI) is analysed. Vegetation dynamics largely depend on geographical conditions. This study is applied to whole India, comprising 1139 grid points over the period 1982–2015. This study made use of satellite derived longest available data record from Global Inventory Monitoring and Modelling System (GIMMS) and Climate data records (CRU) from climate research unit to study the spatio-temporal changes and assess the driving forces for change in vegetation dynamics. The trends of NDVI and all the climatic variables are analysed using modified Mann–Kendall approaches. Significant climatic trends along with NDVI trends are evidenced over India during period of analysis. Linear association between NDVI and each climatic variable are analysed based on Pearson correlation coefficient at monthly and seasonal scales. In this study, we have proposed spatio-temporal dependency modelling of vegetation dynamics (i.e., NDVI) on climatic variables. The stepwise linear regression analysis is performed for dependency modelling at grid level covering whole of India. Concurrent and Lag-1 climatic variables are used to consider temporal dependency, and for spatial dependency both average- and max-pooling-based climatic features are extracted. It is evident from the present study that consideration of spatio-temporal dependency could improve the modelling performance (of NDVI-climatic variable) to a great extent in more number of grid points. Spatio-temporal dependency modelling could achieve higher coefficient of determination (R 2 ) values (≥0.5) in 314 grids, which was possible only in 157 grids in case of simple dependency modelling.

Evaluation of consistency among three NDVI products applied to High Mountain Asia in 2000–2015

The current study evaluates consistency among three Normalized Difference Vegetation Index (NDVI) datasets, namely GIMMS, MODIS and SPOT, to characterize alpine vegetation dynamics (greening and browning) across High Mountain Asia (HMA) in 2001–2015. The utility of these datasets is explored to evaluate the vegetation's variability at different spatial-temporal scales and, elevation, and to compare their spatial trends and distribution patterns. In addition to the Pearson correlation coefficients performed to quantitatively analyze the consistency and inconsistency of each dataset, an NDVI quality control (QC) layer and Landsat NDVI are also used to evaluate the findings. The results indicate that the GIMMS has the highest NDVI mean, while SPOT has the lowest. However, GIMMS also showed a browning trend for both Tianshan (TS) and the Qinghai Tibet Plateau (TP) at a rate of −0.3 × 10−3 per year, whereas MODIS and SPOT exhibit a greening trend (TSMODIS = 0.5 × 10−3 per year, TSSPOT = 0.6 × 10−3 per year, TPMODIS = 0.9 × 10−3 per year, TPSPOT = 1.6 × 10−3 per year). Furthermore, MODIS-SPOT shows the highest correlation (RGREEN = 0.73; RBROWN = 0.47), followed by MODIS-GIMMS, and GIMMS-SPOT. The overall, NDVI trend consistency appears to be higher in TS. Finally, the consistent greening pixels mainly distributed in central TP stretching to the northeastern part, and in western stretching to eastern TS, account for 32.14%, while 8.32% of consistent browning pixels are concentrated in southwestern TP and central TS. The inconsistent pixels account for 59.54%, with 39.21% of inconsistent greening pixels being widely distributed across HMA, and 20.58% of inconsistent browning pixels being relatively pronounced in central TS and southern TP. This study provides baseline inferences for the selection and reconstruction of data in follow-up studies on vegetation dynamics.

MONITORING POST-DISASTER MANGROVE FOREST RECOVERIES IN LAWAAN-BALANGIGA, EASTERN SAMAR USING TIME SERIES ANALYSIS OF MOISTURE AND VEGETATION INDICES

Abstract. The mangrove forests of Lawaan-Balangiga in Eastern Samar lost significant cover due to the Typhoon Haiyan that struck the region in 2013. The mangroves in the area have since shown signs of recovery in terms of growth and spatial coverage, but these widely varied with locations. This study aims to further examine the status of recovery of mangroves across different locations by analysing the time series trends of selected vegetation and moisture indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), and Normalized Difference Moisture Index (NDMI). These indices were extracted from Landsat 8 surface reflectance images, spanning 2014 to 2020, using Google Earth Engine (GEE). The time series analyses showed similar NDVI, MSAVI and NDMI values and trends after the 2013 typhoon event. The trend slopes also indicated high correlation (0.91 – 1.00) between and among the indices, with NDVI having the highest correlation with MSAVI (∼1.00). The study was able to corroborate the previous study on mangroves in Lawaan-Balangiga, by presenting positive trend results in the identified recovered areas. These trends, however, would still have to be validated by collecting and comparing biophysical parameters in the field. The next step of the research would be to identify the factors that contribute to the varying rates of recovery in the areas and to evaluate how this can affect the carbon sequestration rates of recovering mangroves.

Land use changes and its impact on biophysical environment: Study on a river bank

The rapidly changing land use from agriculture to aquaculture and its emerging adverse impact on various biophysical environments along the Rasulpur River bank, West Bengal, India have been quantified by using Landsat data from 2003 to 2017. The biophysical indices like Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Bareness Index (NDBaI) and Soil Adjusted Vegetation Index (SAVI) as well as Land Surface Temperature (LST) were estimated to extract the changing relationship between each other’s using Pearson’s correlation coefficient, generalized multiple linear regression (GMLR) model and Multiple Linear Regression (MLR) model. Results revealed the decreasing trend of NDVI and SAVI along with increasing trends of NDWI, MNDWI and NDBaI controlled land surface temperature (LST). Hence, both the regression models were well fitted at the significance level of p < 0.01. Moreover, spatial autocorrelation and Getis-Ord-Gi* were used to evaluate the environmental sensitive zone through hot spot analysis. Consequently, some environmental stewardship planning and people’s participation programs were proposed to protect environmental biophysical health and ecosystem services.

Temporal and Spatial Variation of NDVI and Its Driving Factors in Qinling Mountain

Qinling Mountains is the north–south boundary of China’s geography; the vegetation changes are of great significance to the survival of wildlife and the protection of species habitats. Based on Landsat products in the Google Earth Engine (GEE) platform, Pearson’s correlation coefficient method, and classification and regression models, this study analyzed the changes in NDVI (Normalized Difference Vegetation Index) in the Qinling Mountains in the past 38 years and the sensitivity of its driving factors. Finally, residual analysis method and accumulate slope change rate are used to identify the impact of human activities and climate change on NDVI. The research results show the following: (1) The NDVI value in most areas of Qinling Mountains is at a medium-to-high level, and 99.76% of the areas correspond to an increasing trend of NDVI, and the significantly increased area accounts for more than 20%. (2) From 1981 to 2019, the NDVI of the Qinling Mountains increased from 0.63 to 0.78, showing an overall upward trend, and it increased significantly after 2006. (3) Sensitivity analysis results show that the western high-altitude area of Qinling Mountain area dominated by grassland is mainly affected by precipitation. The central and southeastern parts of the Qinling Mountains are significantly affected by temperature, and they are mainly distributed in areas dominated by forest. (4) The contribution rates of climate change and human activities to NDVI are 36.04% and 63.96%, respectively. Among them, the positive impact of human activities on the NDVI of the Qinling Mountains accounted for 99.85% of the area. The area with significant positive effect accounted for 36.49%. The significant negative effect area accounts for only 0.006%, mainly distributed in urban areas and coal mining areas.

Expanding infrastructure and growing anthropogenic impacts along Arctic coasts

The accelerating climatic changes and new infrastructure development across the Arctic require more robust risk and environmental assessment, but thus far there is no consistent record of human impact. We provide a first panarctic satellite-based record of expanding infrastructure and anthropogenic impacts along all permafrost affected coasts (100 km buffer, ≈6.2 Mio km2), named the Sentinel-1/2 derived Arctic Coastal Human Impact (SACHI) dataset. The completeness and thematic content goes beyond traditional satellite based approaches as well as other publicly accessible data sources. Three classes are considered: linear transport infrastructure (roads and railways), buildings, and other impacted area. C-band synthetic aperture radar and multi-spectral information (2016–2020) is exploited within a machine learning framework (gradient boosting machines and deep learning) and combined for retrieval with 10 m nominal resolution. In total, an area of 1243 km2 constitutes human-built infrastructure as of 2016–2020. Depending on region, SACHI contains 8%–48% more information (human presence) than in OpenStreetMap. 221 (78%) more settlements are identified than in a recently published dataset for this region. 47% is not covered in a global night-time light dataset from 2016. At least 15% (180 km2) correspond to new or increased detectable human impact since 2000 according to a Landsat-based normalized difference vegetation index trend comparison within the analysis extent. Most of the expanded presence occurred in Russia, but also some in Canada and US. 31% and 5% of impacted area associated predominantly with oil/gas and mining industry respectively has appeared after 2000. 55% of the identified human impacted area will be shifting to above 0 ∘C ground temperature at two meter depth by 2050 if current permafrost warming trends continue at the pace of the last two decades, highlighting the critical importance to better understand how much and where Arctic infrastructure may become threatened by permafrost thaw.

Temporal Pattern Analysis of Cropland Phenology in Shandong Province of China Based on Two Long-Sequence Remote Sensing Data

Vegetation phenology dynamics have attracted worldwide attention due to its direct response to global climate change and the great influence on terrestrial carbon budgets and ecosystem productivity in the past several decades. However, most studies have focused on phenology investigation on natural vegetation, and only a few have explored phenology variation of cropland. In this study, taking the typical cropland in the Shandong province of China as the target, we analyzed the temporal pattern of the Normalized Difference Vegetation Index (NDVI) and phenology metrics (growing season start (SOS) and end (EOS)) derived from the Global Inventory Monitoring and Modeling System (GIMMS) 3-generation version 1 (1982–2015) and the Vegetation Index and Phenology (VIP) version 4 (1981–2016), and then investigated the influence of climate factors and Net Primary Production (NPP, only for EOS) on SOS/EOS. Results show a consistent seasonal profile and interannual variation trend of NDVI for the two products. Annual average NDVI has significantly increased since 1980s, and hugely augmentations of NDVI were detected from March to June for both NDVI products (p &lt; 0.01), which indicates a consistent greening tendency of the study region. SOSs from both products are correlated well with the ground-observed wheat elongation and spike date and have significantly advanced since the 1980s, with almost the same changing rate (0.65/0.64 days yr-1, p &lt; 0.01). EOS also exhibits an earlier but weak advancing trend. Due to the significant advance of SOS, the growing season duration has significantly lengthened. Spring precipitation has a relatively stronger influence on SOS than temperature and shortwave radiation, while a greater correlation coefficient was diagnosed between EOS and autumn temperature/shortwave radiation than precipitation/NDVI. Autumn NPP exhibits a nonlinear effect on EOS, which is first earlier and then later with the increase of autumn NPP. Overall, we highlight the similar capacity of the two NDVI products in characterizing the temporal patterns of cropland phenology.