Decrease In Normalized Difference Vegetation Index Research Articles

Multi-factor weighted image fusion method for high spatiotemporal tracking of reservoir drawdown area and its vegetation dynamics

Reservoir drawdown areas (RDAs) with distinct dry-wet cycles and vegetation dynamics have emerged as significant hotspots for carbon-related activities. However, high-resolution spatiotemporal tracking of the variations and vegetation dynamics of RDAs remains challenging because they often change dramatically and are controlled by both human activities and natural factors. Herein, a modified image fusion method was proposed to capture rapid variations in RDAs by integrating impact factor information into the analysis. The capability of the proposed method was tested in the Danjiangkou (DJK) Reservoir as it is the largest artificial freshwater lake in Asia with a highly variable RDA, since it is surrounded by gently sloping plains or hills. The results showed that the modified workflow produced reliable predictions (r=0.83,RMSE=0.097) compared with the original Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) workflow (r=0.60,RMSE=0.195), demonstrating improved capability for mapping water surface changes and vegetation dynamics. Using the proposed method, the 15-d variations in the RDA were derived from 2013 to 2022 with a 30-m resolution. The interannual maximum RDA was estimated to be 278 km2 after the dam was elevated in 2013. The Normalized Difference Vegetation Index (NDVI) decreased as inundation frequency (IF) increased. Mean NDVI in the growing season (May–October) decreased by 0.109 (17.6 %) and 0.156 (26.0 %) under 30 %–40 % IF and 60 %–70 % IF, respectively, compared with the vegetation under 0 %–10 % IF, which was referred as “natural” vegetation considering its rare inundation. Moreover, the mean growing season length decreased to only 63 and 19 d for 30 %–40 % IF and 60 %–70 % IF, respectively. Furthermore, 77.3 % of the RDA exhibited a decrease in NDVI, whereas 22.7 % showed an unusual increase, possibly due to the selection of dominant species well-adapted to inundation during vegetation succession. Overall, this study not only proposed a new method for the high spatiotemporal monitoring of RDAs, but also highlighted the importance of variations and vegetation dynamics within RDAs for accurate estimation of their carbon budgets.

Short-Term Tree-Ring Series of Pinus hartwegii Lindl. Taken at Ground Level Correlate to Normalized Difference Vegetation Index Series

In this work, we propose that small wood core samples (≤10 cm length), taken from 0.3 m above the soil, represent an accurate means of correlating forest growth with remote sensing data. Short-term correlations between the Normalized Difference Vegetation Index (NDVI) and tree-ring components of the last three to four decades were tested, using 210 Pinus hartwegii wood cores collected at two locations, Tláloc (TLA) and Jocotitlán (JOCO) in central Mexico. The NDVI time series were generated with the Google Earth Engine (GEE) using Landsat 8 images. Also, seasonal trends in NDVI (e.g., spring, summer, autumn, winter) were analyzed through longitudinal analysis. The results showed more statistically significant dendrochronological indices in TLA than in JOCO, but both locations consistently showed an NDVI decrease in 2018 and 2020, indicating a reduction in vegetation vigor. At the two locations, the minimum and maximum NDVI occurred in April and October, respectively. Seasonal NDVI changes for spring were mainly seen at TLA with a decreasing trend, which may be related to a less defined dry season. The significant correlations (p < 0.05) between tree-ring components and the NDVI occurred in the dry season, indicating that the productivity of a given year is defined by the tree vigor shown in April and May, in the case of TLA, and between January and March, for JOCO. Although the NDVI values of JOCO were higher than those of TLA, tree growth, expressed by tree-ring indices, was lower. Our proposed field method to correlate tree-ring information and the NDVI is reliable and can be used in other coniferous forests.

Bi-Temporal Analysis of Vegetation Index on Land Surface Temperature in Kottayam, Kerala

The impact of NDVI (Normalized Difference Vegetation Index) on the LST (Land Surface Temperature) as well as on the genesis of surface heat islands in urban areas during two different time periods was assessed in Kottayam district, Kerala, India. Landsat TM, Landsat OLI and TIRS imagery from the years 1988 and 2020 were employed to scrutinize the relationship between NDVI and LST. The area covered under different NDVI classes were quantified. The finding indicated that NDVI of the research region decreased from 0.77 in 1988 to 0.59 in 2020, resulting in an increase in LSTmax from 34.46 °C in 1988 to 40.63 °C in 2020. Decrease in NDVI resulted in an increase in the high UHI class from 20.83 km2 in 1988 to 660.59 km2 and from 7.26 km2 to 181.35km2 in the very high UHI class. An inverse relationship was observed between NDVI and LST, with Pearson coefficients of 0.5737 and 0.5199 for 1988 and 2020, respectively, which indicates that NDVI could serve as a crucial metric for evaluating LST and UHI effects. Future research will explore the effect of seasonal variability in LULCC on LST, day and night time UHI and their impacts on human health and energy consumption.

COMPARATIVE ANALYSES OF SECONDARY ECOLOGICAL SUCCESSION FOLLOWING WILDFIRES IN THREE DISTINCT FOREST TYPES. A STUDY CASE FROM MOGUER, SPAIN

Wildfires have become increasingly prevalent and destructive in forest ecosystems worldwide, necessitating a comprehensive understanding of post-fire recovery dynamics for effective conservation and management. Remote sensing technology, coupled with vegetation indices such as Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Green Red Vegetation Index (GRVI), and Red Vegetation Index (RVI), offers a powerful means to investigate these processes. In this study, we utilize remote sensing techniques to conduct a comparative analysis of secondary ecological succession following wildfires in three distinct forest types (Coniferous, Sclerophyll and Mixed) of a forest affected by fire near Moguer, Spain. Through the acquisition and analysis of multispectral satellite imagery, we monitored changes in vegetation health and recovery across the region of interest. The NBR index allowed us to assess the severity and extent of wildfire damage, while NDVI quantified vegetation greenness and regrowth. GRVI and RVI provided insights into subtle variations in vegetation composition and health. We identified distinct temporal and spatial patterns in post-fire recovery among the different forest types by applying these indices for the period between 2017 and 2021. Our findings underscore the significance of understanding the diverse responses of these ecosystems to wildfires. While common recovery patterns emerged, such as an initial decrease in NDVI followed by regeneration, variations were observed in the timing and magnitude of recovery. These distinctions are attributed to differences in species composition, fire adaptations, and ecological processes specific to each forest type. In conclusion, the utilization of NBR, NDVI, GRVI, and RVI indices allows for a more nuanced evaluation of post-fire recovery dynamics.

A Near Four-Decade Time Series Shows the Hawaiian Islands Have Been Browning Since the 1980s

The Hawaiian Islands have been identified as a global biodiversity hotspot. We examine the Normalized Difference Vegetation Index (NDVI) using Climate Data Records products (0.05 × 0.05°) to identify significant differences in NDVI between neutral El Niño-Southern Oscillation years (1984, 2019) and significant long-term changes over the entire time series (1982–2019) for the Hawaiian Islands and six land cover classes. Overall, there has been a significant decline in NDVI (i.e., browning) across the Hawaiian Islands from 1982 to 2019 with the islands of Lāna’i and Hawai’i experiencing the greatest decreases in NDVI (≥44%). All land cover classes significantly decreased in NDVI for most months, especially during the wet season month of March. Native vegetation cover across all islands also experienced significant declines in NDVI, with the leeward, southwestern side of the island of Hawai’i experiencing the greatest declines. The long-term trends in the annual total precipitation and annual mean Palmer Drought Severity Index (PDSI) for 1982–2019 on the Hawaiian Islands show significant concurrent declines. Primarily positive correlations between the native ecosystem NDVI and precipitation imply that significant decreases in precipitation may exacerbate the decrease in NDVI of native ecosystems. NDVI-PDSI correlations were primarily negative on the windward side of the islands and positive on the leeward sides, suggesting a higher sensitivity to drought for leeward native ecosystems. Multi-decadal time series and spatially explicit data for native landscapes provide natural resource managers with long-term trends and monthly changes associated with vegetation health and stability.

Time Series of Remote Sensing Data for Interaction Analysis of the Vegetation Coverage and Dust Activity in the Middle East

Motivated by the lack of research on land cover and dust activity in the Middle East, this study seeks to increase the understanding of the sensitivity of dust centers to climatic and surface conditions in this specific region. In this regard, we explore vegetation cover and dust emission interactions using 16-day long-term Normalized Difference Vegetation Index (NDVI) data and daily Aerosol Optical Depth (AOD) data from Moderate Resolution Imaging Spectroradiometer (MODIS) and conduct spatiotemporal and statistical analyses. Eight major dust hotspots were identified based on long-term AOD data (2000–2019). Despite the relatively uniform climate conditions prevailing throughout the region during the study period, there is considerable spatial variability in interannual relationships between AOD and NDVI. Three subsets of periods (2000–2006, 2007–2013, 2014–2019) were examined to assess periodic spatiotemporal changes. In the second period (2007–2013), AOD increased significantly (6% to 32%) across the studied hotspots, simultaneously with a decrease in NDVI (−0.9% to −14.3%) except in Yemen−Oman. Interannual changes over 20 years showed a strong relationship between reduced vegetation cover and increased dust intensity. The correlation between NDVI and AOD (−0.63) for the cumulative region confirms the significant effect of vegetation canopy on annual dust fluctuations. According to the results, changes in vegetation cover have an essential role in dust storm fluctuations. Therefore, this factor must be regarded along with wind speed and other climate factors in Middle East dust hotspots related to research and management efforts.

Monitoring Maize Growth and Calculating Plant Heights with Synthetic Aperture Radar (SAR) and Optical Satellite Images

The decrease in water resources due to climate change is expected to have a significant impact on agriculture. On the other hand, as the world population increases so does the demand for food. It is necessary to better manage environmental resources and maintain an adequate level of crop production in a world where the population is constantly increasing. Therefore, agricultural activities must be closely monitored, especially in maize fields since maize is of great importance to both humans and animals. Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical satellite images were used to monitor maize growth in this study. Backscatter and interferometric coherence values derived from Sentinel-1 images, as well as Normalized Difference Vegetation Index (NDVI) and values related to biophysical variables (such as Leaf Area Index (LAI), Fraction of Vegetation Cover (fCover or FVC), and Canopy Water Content (CW)) derived from Sentinel-2 images were investigated. Sentinel-1 images were also used to calculate plant heights. The Interferometric SAR (InSAR) technique was applied to calculate interferometric coherence values and plant heights. For the plant height calculation, two image pairs with the largest possible perpendicular baseline were selected. Backscatter, NDVI, LAI, fCover, and CW values were low before planting, while the interferometric coherence values were generally high. Backscatter, NDVI, LAI, fCover, and CW values increased as the maize grew, while the interferometric coherence values decreased. Among all Sentinel-derived values, fCover had the best correlation with maize height until maize height exceeded 260 cm (R2 = 0.97). After harvest, a decrease in backscatter, NDVI, LAI, fCover, and CW values and an increase in interferometric coherence values were observed. NDVI, LAI, fCover, and CW values remained insensitive to tillage practices, whereas backscatter and interferometric coherence values were found to be sensitive to planting operations. In addition, backscatter values were also sensitive to irrigation operations, even when the average maize height was about 235 cm. Cloud cover and/or fog near the study area were found to affect NDVI, LAI, fCover, and CW values, while precipitation events had a significant impact on backscatter and interferometric coherence values. Furthermore, using Sentinel-1 images, the average plant height was calculated with an error of about 50 cm.

Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang

Time-series normalized difference vegetation index (NDVI) is commonly used to conduct vegetation dynamics, which is an important research topic. However, few studies have focused on the relationship between vegetation type and NDVI changes. We investigated changes in vegetation in Xinjiang using linear regression of time-series MOD13Q1 NDVI data from 2001 to 2020. MCD12Q1 vegetation type data from 2001 to 2019 were used to analyze transformations among different vegetation types, and the relationship between the transformation of vegetation type and NDVI was analyzed. Approximately 63.29% of the vegetation showed no significant changes. In the vegetation-changed area, approximately 93.88% and 6.12% of the vegetation showed a significant increase and decrease in NDVI, respectively. Approximately 43,382.82 km2 of sparse vegetation and 25,915.44 km2 of grassland were transformed into grassland and cropland, respectively. Moreover, 17.4% of the area with transformed vegetation showed a significant increase in NDVI, whereas 14.61% showed a decrease in NDVI. Furthermore, in areas with NDVI increased, the mean NDVI slopes of pixels in which sparse vegetation transferred to cropland, sparse vegetation transferred to grassland, and grassland transferred to cropland were 9.8 and 3.2 times that of sparse vegetation, and 1.97 times that of grassland, respectively. In areas with decreased NDVI, the mean NDVI slopes of pixels in which cropland transferred to sparse vegetation, grassland transferred to sparse vegetation were 1.75 and 1.36 times that of sparse vegetation, respectively. The combination of vegetation type transformation NDVI time-series can assist in comprehensively understanding the vegetation change characteristics.

Positive Associations of Vegetation with Temperature over the Alpine Grasslands in the Western Tibetan Plateau during May

Abstract The Tibetan Plateau (TP) has undergone extreme changes in climatic and land surface conditions that are due to a warming climate and land-cover changes. We examined the change in vegetation dynamics from 1982 to 2015 and explored the associations of vegetation with atmospheric variables over the alpine grasslands in the western TP during May as an early growing season. The linear regression analysis of area-averaged normalized difference vegetation index (NDVI) over the western TP in May demonstrated a 7.5% decrease of NDVI during the period from 1982 to 2015, an increase of NDVI by 11.3% from 1982 to 1998, and a decrease of NDVI by 14.5% from 1999 to 2015. The significantly changed NDVI in the western TP could result in the substantial changes in surface energy balances as shown in the surface climatic variables of albedo, net solar radiation, sensible heat flux, latent heat fluxes, and 2-m temperature. The land and atmosphere associations were not confined to the surface but also extended into the upper-level atmosphere up to the 300-hPa level as indicated by the significant positive associations between NDVI and temperatures in both air temperature and equivalent temperature, resulting in more than a 1-K increase with NDVI. Therefore, we concluded that the increasing or decreasing vegetation cover in the western TP during May can respectively increase or decrease the temperatures near the surface and upper atmosphere through a positive physical linkage among the vegetation cover, surface energy fluxes, and temperatures. The positive energy processes of vegetation with temperature could further amplify the variations of temperature and thus water availability. Significance Statement The Tibetan Plateau (TP) is an important landmass that plays a significant role in both regional and global climates. This study aims to examine the vegetation change in the TP during May as an early growing season to examine the changes in the near-surface and upper-level climatic conditions associated with vegetation change and to identify the plausible physical processes of the vegetation effects on atmosphere. The satellite-derived vegetation index showed a 7.5% decrease from 1982 to 2015 in the western TP during May. This study identified the positive associations of vegetation activity with temperature and proposed a positive energy process for land–atmosphere interactions over the alpine grasslands in the western region of TP during the transition period from winter to spring.

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.

Assessing the Spatiotemporal Evolution of Anthropogenic Impacts on Remotely Sensed Vegetation Dynamics in Xinjiang, China

The dynamics of the ecosystem represented by vegetation under the influence of human activities have become an important issue in the study of the regional ecological environment. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. However, the spatiotemporal patterns and evolutionary trends of anthropogenic impacts on vegetation dynamics in Xinjiang are still unclear. In this study, the anthropogenic impacts on vegetation dynamics were quantitatively assessed by combining the improved normalized difference vegetation index (NDVI) prediction model and the residual analysis method in Xinjiang, China. The human driving factors were analyzed with the support of a stepwise multiple regression model for vegetation changes at the county scale. Based on trend analysis and the Hurst exponent, the spatiotemporal characteristics and evolutionary trends of the impact of human activities on vegetation change were discussed. The results show that (1) the NDVI values in Xinjiang showed a gradually increasing trend at a rate of 0.005/10 years from 1982 to 2018, and the vegetation dynamics mainly showed significant improvements (57.09% of the vegetated areas), especially for crops. (2) The anthropogenic effects of vegetation changes in Xinjiang mainly included positive impact increases (43.22% of the vegetated areas) from 2000 to 2018. Human activities promoted the increase in the NDVI of various vegetation types. Both the positive and negative impacts of human activities increased over the study period, and the growth rate of the positive influence (0.08%/10 years) was higher than that of the negative influence (0.04%/10 years). (3) The cultivated area, GDP of primary industry, and population are the main anthropogenic factors causing the increase in NDVI, which dominate the vegetation greening in 30.34%, 29.22%, and 28.09% of the counties in Xinjiang, respectively. The animal husbandry population, agricultural population, and livestock number are the main anthropogenic factors causing the decrease in NDVI, which dominate the vegetation degradation in 23.60%, 21.35%, and 17.98% of the counties in Xinjiang, respectively. (4) The evolutionary trend of the anthropogenic impact on vegetation dynamics in Xinjiang will be dominated by anti-persistence (53.84% of the vegetated areas), thereby mainly showing that the positive impacts continued to increase (22.56% of the vegetated areas), especially for crops, shrubs, grasslands, and alpine vegetation. Our results are helpful in understanding the characteristics and evolutionary trends of vegetation changes in arid areas caused by human activities and are of significance as a reference for policymakers to appropriately adjust policy guidance in a timely manner to promote the protection and sustainable development of fragile ecosystems.

Implications of landscape configuration on understory forage productivity: a remote sensing assessment of native forests openings

Sound management of native forests used for cattle grazing requires understanding the dynamics of forage productivity in the openings. Despite their importance, forage productivity drivers in highly heterogeneous forested landscapes, or their variability over the year, are still unclear. The aim of this work is to find predictors of Normalized Difference Vegetation Index (NDVI) variation in the openings of native temperate forests and to evaluate how these predictors change within the growing season. We used high spatial resolution remote sensing imagery from NW Patagonia to separate forest openings from tree dense canopy. We obtained data of each opening related with herbaceous and shrub forage productivity and calculated landscape metrics. We estimated a multiple linear regression model for predicting NDVI in each season. Beyond known variables related with forage productivity (altitude, precipitation, etc.), the shape of forest’ openings appeared as relevant in predicting NDVI. Higher values of forest opening perimeters were related with a decrease in NDVI in spring when soil water content is not limiting and conversely with an increase in NDVI in summer when water is limiting growth. These results suggest that environmental drivers such as temperature and soil moisture inside the opening, and competition or facilitation process between trees and grasses are mediated by the shape of the opening. Management of heterogeneous native forests for cattle raising requires considering the shape of the openings to maximize forage productivity.

Effect of heatwaves and greenness on mortality among Chinese older adults

Heatwaves and greenness have been shown to affect health, but the evidence on their joint effects is limited. We aim to assess the associations of the combined exposure to greenness and heatwaves. We utilized five waves (February 2000–October 2014) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a prospective cohort of older adults aged 65. We defined heatwaves as the daily maximum temperature ≥92.5th percentile with duration ≥3 days. We calculated the number of heatwave days in one year before death to and cumulative Normalized Difference Vegetation Index (NDVI) during follow-up to assess individual long-term exposure to heatwaves and greenness. Cox proportional hazards models were used to assess the effects of greenness, heatwaves, and their interaction on mortality, adjusted for covariates. We conducted subgroup analyses by residence, gender, and age. There were 20,758 participants in our study, totaling 67,312 person-years of follow-up. The mean NDVI was 0·41 (SD 0.13), and the mean number of heatwave days was 8.92 (2.04). In the adjusted model, the mortality hazard ratio (HR) for each 3-day increase in heatwave days was 1.04 (95% CI 1.04, 1.05), each 0.1-unit decrease in cumulative NDVI was 1.06 (1.05, 1.07). In the adjusted model with an interaction term, the HR for the interaction term was 1.01 (1.01, 1.02) with a p-value less than 0.001. In our subgroup analyses, the HR for each 3-day increase in heatwave days was higher in urban areas than in rural areas (1.06 vs. 1.03), and the HR for 0.1-unit decrease in NDVI was higher in urban areas than in rural areas (1.08 vs. 1.04). Greenness can protect against the effect of heatwaves on mortality, and heatwaves affect the health effects of greenness. Urban dwellers have a higher response to the detrimental effect of heatwaves and a higher marginal benefit from greenness exposure.

Effect of heatwaves and greenness on mortality among Chinese elderly people

BACKGROUND AND AIM: Both heatwaves and greenness have been shown to affect health, but the evidence on their joint effects is needed to be better elucidated. We aim to assess the associations of the combined exposure to greenness and heatwaves and whether the effect varies between different subgroups. METHODS: We utilized the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a prospective cohort of older adults aged 65. We used the number of heatwave days in one year before death to estimate individual long-term heatwaves exposure and cumulative Normalized Difference Vegetation Index (NDVI) during follow-up to assess individual greenness exposure. Cox proportional hazards models were used to assess the effects of greenness, heatwaves, and their interaction on mortality, adjusted for a range of covariates. We conducted subgroup analyses by residence, gender, and age. RESULTS:There were 20,758 participants in our study, totaling 67,312 person-years of follow-up. The mean NDVI was 0·41 (SD 0.13), and the mean number of heatwave days was 8.92 (2.04). In the adjusted model, the mortality hazard ratio (HR) for each 3-day increase in heatwave days was 1.04 (95% CI 1.04,1.05), each 0.1-unit decrease in cumulative NDVI was 1.06 (1.05,1.07). In the adjusted model with an interaction term, the HR for the interaction term was 1.01 (1.01,1.02) with a p-value less than 0.001. In our subgroup analyses, the HR for each 3-day increase in heatwave days was higher in urban areas than in rural areas (1.06 vs. 1.03), and the HR for 0.1-unit decrease in NDVI was higher in urban areas than in rural areas (1.08 vs. 1.04). CONCLUSIONS:Greenness can protect against the effect of heatwaves on mortality, and the number of heatwave days will affect the health effects of greenness. Urban dwellers have a higher response to the detrimental effect of heatwaves and have a higher marginal benefit from greenness exposure. KEYWORDS: Green space, Temperature, Environmental epidemiology

Vegetation dynamics and its linkage with climatic and anthropogenic factors in the Dawen River Watershed of China from 1999 through 2018.

The Dawen River Watershed (DRW), an important sub-basin of the Yellow River, has been experiencing substantial climatic and anthropogenic stresses. Identifying how stressors relate to shifts in vegetation growth is critical for maintaining the health and stability of its regional ecosystems. To address this, we constructed a 20-year dataset (1999-2018) reflecting changes in satellite-based normalized difference vegetation index (NDVI), climate variables, and land use in the DRW. We then used time series, principal component, and partial correlation analyses to detect spatial and temporal patterns in vegetation dynamics over time, as well as linkages with temperature, precipitation, and anthropogenic activities. Over 20 years, the DRW exhibited a warming-greening trend and experienced four regime shifts in its climate-vegetation system, roughly centered on 2001, 2006, 2013, and 2016. Both the average and maximum NDVI increased in all seasons, likely due to favorable changes in seasonal climatic conditions. Temperature was the dominant factor promoting vegetative growth in spring, autumn, and throughout the growing season. Precipitation had a considerable positive effect on the average NDVI during the summer. Spatial analyses indicated that 67.94% of the study area exhibited significant increase in NDVI values over time, mainly locating in the mountains and in Dongping County; Significant NDVI decrease was generally located in the urban expansion areas around cities and counties. Land cover types and annual growth cycles appeared to govern spatial patterns and the extent of variation in vegetation growth, followed by land use-related drivers and climate anomalies. These findings offer an insight on appropriate ecological management and climatic adaptation within the Dawen River Watershed.

Forest cover assessment using remote-sensing techniques in Crete Island, Greece

Abstract Remote-sensing satellite images provided rapid and continuous spectral and spatial information of the land surface in the Sougia River catchment by identifying the major changes that have taken place over 20 years (1995–2015). Vegetation indices (VIs) of normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index were derived for monitoring and mapping variations in vegetation cover. The quantified decrease in NDVI was found to be 4% between 1995 and 2005, and further decreased by 77.1% between 2005 and 2015; it declined back to almost the initial status of 1995. EVI results were inconsistent suggesting that seasonal crops influence the temporal distribution of vegetation cover. The temporal variations in the VIs were important input parameters for the modelling and management of the catchment’s hydrological behaviour. Image classification found that the 4- and the 6-class classifications between 1995 and 2005 were unstable and produced, respectively, a 13.8% and 16.2% total change between classes. Meanwhile, the 8-, 10- and the 12-class showed an almost horizontal line with a minor fluctuation of less than 0.05%. The results of the post-classification change detection analysis indicated a land degradation in terms of natural vegetation losses with sparser or even with no natural vegetation cover.

Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

Smokey the Beaver: beaver-dammed riparian corridors stay green during wildfire throughout the western United States.

Beaver dams are gaining popularity as a low-tech, low-cost strategy to build climate resiliency at the landscape scale. They slow and store water that can be accessed by riparian vegetation during dry periods, effectively protecting riparian ecosystems from droughts. Whether or not this protection extends to wildfire has been discussed anecdotally but has not been examined in a scientific context. We used remotely sensed Normalized Difference Vegetation Index (NDVI) data to compare riparian vegetation greenness in areas with and without beaver damming during wildfire. We include data from five large wildfires of varying burn severity and dominant landcover settings in the western United States in our analysis. We found that beaver-dammed riparian corridors are relatively unaffected by wildfire when compared to similar riparian corridors without beaver damming. On average, the decrease in NDVI during fire in areas without beaver is 3.05 times as large as it is in areas with beaver. However, plant greenness rebounded in the year after wildfire regardless of beaver activity. Thus, we conclude that, while beaver activity does not necessarily play a role in riparian vegetation post-fire resilience, it does play a significant role in riparian vegetation fire resistance and refugia creation.

Exploring the Use of Sentinel-2 Data to Monitor Heterogeneous Effects of Contextual Drought and Heatwaves on Mediterranean Forests

The use of satellite data to detect forest areas impacted by extreme events, such as droughts, heatwaves, or fires is largely documented, however, the use of these data to identify the heterogeneity of the forests’ response to determine fine scale spatially irregular damage is less explored. This paper evaluates the health status of forests in southern Italy affected by adverse climate conditions during the hot and dry summer of 2017, using Sentinel-2 images (10m) and in situ data. Our analysis shows that the post-event—NDVI (Normalized Difference Vegetation Index) decrease, observed in five experimental sites, well accounts for the heterogeneity of the local response to the climate event evaluated in situ through the Mannerucci and the Raunkiaer methods. As a result, Sentinel-2 data can be effectively integrated with biological information from field surveys to introduce continuity in the estimation of climate change impacts even in very heterogeneous areas whose details could not be captured by lower resolution observations. This integration appears to be a successful strategy in the study of the relationships between the climate and forests from a dynamical perspective.

SMAP underestimates soil moisture in vegetation-disturbed areas primarily as a result of biased surface temperature data

The Soil Moisture Active Mission (SMAP) baseline V-pol single channel algorithm (SCA-V) retrieves soil moisture (SM) based on the 2000–2010 Moderate-resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) climatology. This parameterization scheme might affect SM retrievals in the context of vegetation disturbances, e.g., as a result of drought. By referencing the European Space Agency (ESA) Climate Change Initiative (CCI), the Global Land Data Assimilation System (GLDAS), the Soil Moisture and Ocean Salinity (SMOS) L3 (SMOS-L3) and the SMOS-IC SM datasets, this study investigated the effects of SMAP vegetation parameterization on SMAP–reference SM differences in global land areas that are subject to differing vegetation disturbances. The results show that SMAP might underestimate SM by ~0.007 m3·m−3 for a relative NDVI decrease of 10%. The underestimation might be primarily caused by low biases in the Global Modeling and Assimilation Office (GMAO) surface temperature. Although using NDVI climatology might cause an overestimation of SM (due to an overestimation of vegetation effects and an underestimation of surface roughness effects), the concurrent underestimation of the GMAO surface temperature might cause an even larger underestimation of SM. The vegetation biases, particularly the surface temperature biases, should be considered for SMAP SM retrieval. These results have implications for future updates of SMAP SCA-V and applications of time series SMAP SM data for hydroecological studies, especially in the future, which is projected to have strong and long-lasting droughts.