Early Monitoring of Health Status of Plantation-Grown Eucalyptus pellita at Large Spatial Scale via Visible Spectrum Imaging of Canopy Foliage Using Unmanned Aerial Vehicles

Eucalyptus is a diverse genus from which several species are often deployed for commercial industrial tree plantation due to their desirable wood properties for utilization in both solid wood and fiber products, as well as their growth and productivity in many environments. In this study, a method for monitoring the health status of a 22.78 ha Eucalyptus pellita plantation stand was developed using the red-green-blue channels captured using an unmanned aerial vehicle. The ortho-image was generated, and visual atmospheric resistance index (VARI) indices were developed. Herein, four classification levels of pest and disease were generated using the VARI-green algorithm. The range of normalized VARI-green indices was between −2.0 and 2.0. The results identified seven dead trees (VARI-green index −2 to 0), five trees that were severely infected (VARI-green index 0 to 0.05), 967 trees that were mildly infected (VARI-green index 0.06 to 0.16), and 10,090 trees that were considered healthy (VARI-green index 0.17 to 2.00). The VARI-green indices were verified by manual ground-truthing and by comparison with normalized difference vegetation index which showed a mean correlation of 0.73. This study has shown practical application of aerial survey of a large-scale operational area of industrial tree plantation via low-cost UAV and RGB camera, to analyze VARI-green images in the detection of pest and disease.

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Contributions of Vegetation Greening and Climate Change to Evapotranspiration Trend after Large-Scale Vegetation Restoration on the Loess Plateau, China

Water ◽

10.3390/w13131755 ◽

2021 ◽

Vol 13 (13) ◽

pp. 1755

Author(s):

Shuo Wang ◽

Chenfeng Cui ◽

Qin Dai

Keyword(s):

Climate Change ◽

Loess Plateau ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Potential Evapotranspiration ◽

Development Trend ◽

Future Trend ◽

Natural Ecosystem ◽

The Loess Plateau

Since the early 2000s, the vegetation cover of the Loess Plateau (LP) has increased significantly, which has been fully recorded. However, the effects on relevant eco-hydrological processes are still unclear. Here, we made an investigation on the changes of actual evapotranspiration (ETa) during 2000–2018 and connected them with vegetation greening and climate change in the LP, based on the remote sensing data with correlation and attribution analysis. Results identified that the average annual ETa on the LP exhibited an obvious increasing trend with the value of 9.11 mm yr−1, and the annual ETa trend was dominated by the changes of ETa in the third quarter (July, August, and September). The future trend of ETa was predicted by the Hurst exponent. Partial correlation analysis indicated that annual ETa variations in 87.8% regions of the LP were controlled by vegetation greening. Multiple regression analysis suggested that the relative contributions of potential evapotranspiration (ETp), precipitation, and normalized difference vegetation index (NDVI), to the trend of ETa were 5.7%, −26.3%, and 61.4%, separately. Vegetation greening has a close relationship with the Grain for Green (GFG) project and acts as an essential driver for the long-term development trend of water consumption on the LP. In this research, the potential conflicts of water demanding between the natural ecosystem and social-economic system in the LP were highlighted, which were caused by the fast vegetation expansion.

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Climate Drivers Linked to Changing Seasonality of Alaska Coastal Tundra Vegetation Productivity

Earth Interactions ◽

10.1175/ei-d-15-0013.1 ◽

2015 ◽

Vol 19 (19) ◽

pp. 1-29 ◽

Cited By ~ 25

Author(s):

Peter A. Bieniek ◽

Uma S. Bhatt ◽

Donald A. Walker ◽

Martha K. Raynolds ◽

Josefino C. Comiso ◽

...

Keyword(s):

Land Surface ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Sea Breeze ◽

Tundra Vegetation ◽

Land Surface Temperatures ◽

Alaskan Tundra ◽

Northern Alaska

Abstract The mechanisms driving trends and variability of the normalized difference vegetation index (NDVI) for tundra in Alaska along the Beaufort, east Chukchi, and east Bering Seas for 1982–2013 are evaluated in the context of remote sensing, reanalysis, and meteorological station data as well as regional modeling. Over the entire season the tundra vegetation continues to green; however, biweekly NDVI has declined during the early part of the growing season in all of the Alaskan tundra domains. These springtime declines coincide with increased snow depth in spring documented in northern Alaska. The tundra region generally has warmed over the summer but intraseasonal analysis shows a decline in midsummer land surface temperatures. The midsummer cooling is consistent with recent large-scale circulation changes characterized by lower sea level pressures, which favor increased cloud cover. In northern Alaska, the sea-breeze circulation is strengthened with an increase in atmospheric moisture/cloudiness inland when the land surface is warmed in a regional model, suggesting the potential for increased vegetation to feedback onto the atmospheric circulation that could reduce midsummer temperatures. This study shows that both large- and local-scale climate drivers likely play a role in the observed seasonality of NDVI trends.

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Temporal and Spatial Variations in NDVI and Analysis of the Driving Factors in the Desertified Areas of Northern China From 1998 to 2015

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.633020 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xuyang Wang ◽

Yuqiang Li ◽

XinYuan Wang ◽

Yulin Li ◽

Jie Lian ◽

...

Keyword(s):

Ecological Restoration ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Climatic Factors ◽

Northern China ◽

Gobi Desert ◽

Desert Grassland ◽

Dune Stability ◽

Mobile Dunes

China faces some of the most serious desertification in the world, leading to many problems. To solve them, large-scale ecological restoration projects were implemented. To assess their effectiveness, we analyzed normalized-difference vegetation index (NDVI) data derived from SPOT VEGETATION and gridded climate datasets from 1998 to 2015 to detect the degrees of desertification and the effects of human and climate drivers on vegetation dynamics. We found that NDVI of desertified areas generally decreased before 2000, then increased. The annual increase in NDVI was fixed dunes (0.0013) = semi-fixed dunes (0.0013) > semi-mobile dunes (0.0012) > gobi (gravel) desert (0.0011) > mobile dunes (0.0003) > saline–alkali land (0.0000). The proportions of the area of each desert type in which NDVI increased were fixed dunes (43.4%) > semi-mobile dunes (39.7%) > semi-fixed dunes (26.7%) > saline–alkali land (23.1%) > gobi desert (14.4%) > mobile dunes (12.5%). Thus, the vegetation response to the restoration efforts increased as the initial dune stability increased. The proportion of the area where desertification was dominated by temperature (1.8%) was far less than the area dominated by precipitation (14.1%). However, 67.6% of the change was driven by non-climatic factors. The effectiveness of the ecological restoration projects was significant in the Loess Plateau and in the Mu Us, Horqin, and Hulunbuir sandy lands. In contrast, there was little effect in the Badain Jaran, Ulan Buh, and Tengger deserts; in particular, vegetation cover has declined seriously in the Hunshandake Sandy Land and Alkin Desert Grassland. Thus, more or different ecological restoration must be implemented in these areas.

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Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing

Advances in Meteorology ◽

10.1155/2017/8434020 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Erika Andujar ◽

Nir Y. Krakauer ◽

Chuixiang Yi ◽

Felix Kogan

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Thermal Emission ◽

Plant Stress ◽

Meteorological Drought ◽

Condition Index ◽

Vegetation Health ◽

Vegetation Health Index

Remote sensing is used for monitoring the impacts of meteorological drought on ecosystems, but few large-scale comparisons of the response timescale to drought of different vegetation remote sensing products are available. We correlated vegetation health products derived from polar-orbiting radiometer observations with a meteorological drought indicator available at different aggregation timescales, the Standardized Precipitation Evapotranspiration Index (SPEI), to evaluate responses averaged globally and over latitude and biome. The remote sensing products are Vegetation Condition Index (VCI), which uses normalized difference vegetation index (NDVI) to identify plant stress, Temperature Condition Index (TCI), based on thermal emission as a measure of surface temperature, and Vegetation Health Index (VHI), the average of VCI and TCI. Globally, TCI correlated best with 2-month timescale SPEI, VCI correlated best with longer timescale droughts (peak mean correlation at 13 months), and VHI correlated best at an intermediate timescale of 4 months. Our results suggest that thermal emission (TCI) may better detect incipient drought than vegetation color (VCI). VHI had the highest correlations with SPEI at aggregation times greater than 3 months and hence may be the most suitable product for monitoring the effects of long droughts.

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Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

Remote Sensing ◽

10.3390/rs12010190 ◽

2020 ◽

Vol 12 (1) ◽

pp. 190 ◽

Cited By ~ 3

Author(s):

Ruyin Cao ◽

Yan Feng ◽

Xilong Liu ◽

Miaogen Shen ◽

Ji Zhou

Keyword(s):

Western Europe ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spatial Scales ◽

Vegetation Indices ◽

High Latitudes ◽

Large Spatial Scale ◽

Arid Grasslands ◽

Seasonal Snow Cover ◽

Spring Snowmelt

Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.

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Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery

Remote Sensing ◽

10.3390/rs12193153 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3153

Author(s):

André Duarte ◽

Luis Acevedo-Muñoz ◽

Catarina I. Gonçalves ◽

Luís Mota ◽

Alexandre Sarmento ◽

...

Keyword(s):

Random Forest ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Forest Density ◽

Random Forest Classification ◽

Otsu Thresholding ◽

Forest Classification ◽

Density Maps ◽

Kappa Value

Eucalyptus Longhorned Borers (ELB) are some of the most destructive pests in regions with Mediterranean climate. Low rainfall and extended dry summers cause stress in eucalyptus trees and facilitate ELB infestation. Due to the difficulty of monitoring the stands by traditional methods, remote sensing arises as an invaluable tool. The main goal of this study was to demonstrate the accuracy of unmanned aerial vehicle (UAV) multispectral imagery for detection and quantification of ELB damages in eucalyptus stands. To detect spatial damage, Otsu thresholding analysis was conducted with five imagery-derived vegetation indices (VIs) and classification accuracy was assessed. Treetops were calculated using the local maxima filter of a sliding window algorithm. Subsequently, large-scale mean-shift segmentation was performed to extract the crowns, and these were classified with random forest (RF). Forest density maps were produced with data obtained from RF classification. The normalized difference vegetation index (NDVI) presented the highest overall accuracy at 98.2% and 0.96 Kappa value. Random forest classification resulted in 98.5% accuracy and 0.94 Kappa value. The Otsu thresholding and random forest classification can be used by forest managers to assess the infestation. The aggregation of data offered by forest density maps can be a simple tool for supporting pest management.

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Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery

Sensors ◽

10.3390/s19051221 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1221 ◽

Cited By ~ 4

Author(s):

Jun Wang ◽

Lichun Sui ◽

Xiaomei Yang ◽

Zhihua Wang ◽

Yueming Liu ◽

...

Keyword(s):

Satellite Imagery ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Eastern China ◽

Chlorophyll Concentration ◽

False Negative ◽

Distribution Data ◽

Landsat 8 ◽

Negative Results

Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analysis using this expensive imagery requires a large number of images. In contrast, medium resolution satellite imagery, such as Landsat 8 images, are available at no cost, cover large areas with less data volume, and provide acceptable results. Therefore, we used Landsat 8 images to extract the presence of coastal raft aquaculture. Because the high chlorophyll concentration of coastal raft aquaculture areas cause the Normalized Difference Vegetation Index (NDVI) and the edge features to be salient for the water background, we integrated these features into the proposed method. Three sites from north to south in Eastern China were used to validate the method and compare it with our former proposed method using only object-based visually salient NDVI (OBVS-NDVI) features. The new proposed method not only maintains the true positive results of OBVS-NDVI, but also eliminates most false negative results of OBVS-NDVI. Thus, the new proposed method has potential for use in rapid monitoring of coastal raft aquaculture on a large scale.

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Environmental drivers of variability in the movement ecology of turkey vultures ( Cathartes aura ) in North and South America

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2013.0195 ◽

2014 ◽

Vol 369 (1643) ◽

pp. 20130195 ◽

Cited By ~ 82

Author(s):

Somayeh Dodge ◽

Gil Bohrer ◽

Keith Bildstein ◽

Sarah C. Davidson ◽

Rolf Weinzierl ◽

...

Keyword(s):

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spatial Dynamics ◽

Movement Ecology ◽

Environmental Drivers ◽

Long Distance ◽

Cathartes Aura ◽

Earth’S Climate ◽

Turkey Vultures

Variation is key to the adaptability of species and their ability to survive changes to the Earth's climate and habitats. Plasticity in movement strategies allows a species to better track spatial dynamics of habitat quality. We describe the mechanisms that shape the movement of a long-distance migrant bird (turkey vulture, Cathartes aura ) across two continents using satellite tracking coupled with remote-sensing science. Using nearly 10 years of data from 24 satellite-tracked vultures in four distinct populations, we describe an enormous amount of variation in their movement patterns. We related vulture movement to environmental conditions and found important correlations explaining how far they need to move to find food (indexed by the Normalized Difference Vegetation Index) and how fast they can move based on the prevalence of thermals and temperature. We conclude that the extensive variability in the movement ecology of turkey vultures, facilitated by their energetically efficient thermal soaring, suggests that this species is likely to do well across periods of modest climate change. The large scale and sample sizes needed for such analysis in a widespread migrant emphasizes the need for integrated and collaborative efforts to obtain tracking data and for policies, tools and open datasets to encourage such collaborations and data sharing.

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Association between Surrounding Greenness and Mortality: An Ecological Study in Taiwan

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17124525 ◽

2020 ◽

Vol 17 (12) ◽

pp. 4525

Author(s):

Hsiao-Yun Lee ◽

Chih-Da Wu ◽

Yi-Tsai Chang ◽

Yinq-Rong Chern ◽

Shih-Chun Candice Lung ◽

...

Keyword(s):

Vegetation Index ◽

Mixed Model ◽

Normalized Difference Vegetation Index ◽

Underlying Mechanism ◽

Risk Of Death ◽

Asian Populations ◽

Green Vegetation ◽

Moderate Resolution ◽

Green Index ◽

The Relationship

Exposure to surrounding greenness is associated with reduced mortality in Caucasian populations. Little is known however about the relationship between green vegetation and the risk of death in Asian populations. Therefore, we opted to evaluate the association of greenness with mortality in Taiwan. Death information was retrieved from the Taiwan Death Certificate database between 2006 to 2014 (3287 days). Exposure to green vegetation was based on the normalized difference vegetation index (NDVI) collected by the Moderate Resolution Imagine Spectroradiometer (MODIS). A generalized additive mixed model was utilized to assess the association between NDVI exposure and mortality. A total of 1,173,773 deaths were identified from 2006 to 2014. We found one unit increment on NDVI was associated with a reduced mortality due to all-cause (risk ratio [RR] = 0.901; 95% confidence interval = 0.862–0.941), cardiovascular diseases (RR = 0.892; 95% CI = 0.817–0.975), respiratory diseases (RR = 0.721; 95% CI = 0.632–0.824), and lung cancer (RR = 0.871; 95% CI = 0.735–1.032). Using the green land cover as the alternative green index showed the protective relationship on all-cause mortality. Exposure to surrounding greenness was negatively associated with mortality in Taiwan. Further research is needed to uncover the underlying mechanism.

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Scale-Location Dependence of the Relationship between NDVI and Environmental Factors in Wuyi Mountain

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.955-959.3828 ◽

2014 ◽

Vol 955-959 ◽

pp. 3828-3834

Author(s):

Wei Cheng Zou ◽

G. R. Xiao

Keyword(s):

Environmental Factors ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Windward Side ◽

Slope Aspect ◽

Leeward Side ◽

Small Scale ◽

Medium Scale ◽

Wavelet Coherency

The correlation between Normalized Difference Vegetation Index (NDVI) and environmental factors is examined at different scales and locations in world heritage of Wuyi Mountain by wavelet coherency. These factors are elevation, slope, aspect, distance to nearest resident, distance to nearest road , and distance to nearest river along two transects based on data of DEM, residents, roads, rivers and ALOS remote sensing image in 2009.The results show that:(1) The relationships between NDVI and environmental factors change along with scale. The relationships between NDVI and environmental factors in the first transect are all weak at small scale (<480m). At medium scale (480-7680m), NDVI is significantly correlated with elevation, slope, resident , and road. At large scale (>7680m), NDVI is significantly correlated with elevation, resident and river. For the second transect, NDVI is significantly correlated with aspect at small scale; and significantly correlated with elevation, aspect, slope and river at medium scale; and significantly correlated with elevation, aspect, and slope at large scale. Thus elevation is the dominant controlling factors on the vegetation cover.(2)The relationships between NDVI and environmental factors also change when location changes. There is positive correlation between NDVI and elevation below the altitude of 600 m and the windward side of the southeast monsoon above 600m, while it is negative in the leeward side above 600m. Besides, NDVI is directly related with road, resident, slope, and river in the areas where the elevation is below 1200m, but inversely above 1200m.

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