Minimum Normalized Difference Vegetation Index Research Articles

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.

Identifying Risk Factors for Stephanofilaria-Caused Ulcerative Dermal Lesions, in Black and White Rhinoceros’ Meta-Population in Kenya

Ulcerative skin lesions caused by Stephanofilaria dinniki are common in populations of the critically endangered eastern black rhinoceros (Diceros bicornis michaeli). Although considered benign, they have been associated with loss of body condition and mortality in rhinoceros. Stephanofilarial lesions in rhinoceros may also lead to delayed puberty, reduced milk yields, and prolonged intercalving periods as observed with a similar disease in cattle. In this study the prevalence and predilection sites of stephanofilarial wounds were examined and the influence of age, sex, normalized difference vegetation index (NDVI), rainfall, temperature, rhinoceros’ species, and population density on the prevalence of stephanofilarial skin lesions was evaluated in 10 rhinoceros’ populations in Kenya. The results revealed that the prevalence of stephanofilarial skin lesions was 0% in the lowland sanctuaries such as Ngulia Rhino Sanctuary, Tsavo West Intensive Protection Zone, and Tsavo East National Park to ≥90% in the highland rhinoceros’ sanctuaries including Solio Wildlife Sanctuary, Ol Pejeta Wildlife Conservancy, Lewa-Borana Wildlife Conservancies, and Nairobi National Park. Generalized linear models revealed that monthly minimum temperature and NDVI negatively influenced the probability of stephanofilarial skin lesions. However, spatial heterogeneity in NDVI, and rhino density were positively related to the probability of rhinoceros’ infection with Stephanofilaria. Black rhinoceros had a higher average prevalence of stephanofilarial wounds (50.9%, n = 228) than white rhinoceros’ (3.6%, n = 110). Temperature and heterogeneity in NDVI can directly influence the presence of the filaria vector Rhinomusca dutoiti as corroborated by the previous studies on their distribution. Moreover, the inverse relationship between NDVI and the prevalence of stephanofilarial skin lesions suggests that nutrition negatively impacts rhinoceros’ immunity. Comprehensive studies on the vectors of Stephanofilaria and their bionomics may illuminate the epidemiological patterns of stephanofilariosis in rhinoceros.

Temporal and spatial analysis of vegetation cover change in the Yellow River Delta based on Landsat and MODIS time series data.

Based on the Landsat normalized difference vegetation index (NDVI) and the NDVI product of MODIS, this study synthesized two kinds of time-series images. The features were selected according to the characteristics of the time series, and the random forest algorithm was used for classification. Based on the classification results and GIS spatial analysis, the temporal and spatial changes in vegetation cover in the Yellow River Delta from 2000 to 2020 were studied. The results showed that from 2000 to 2020, the vegetation first increased and then decreased, and the dynamic degree of land cover change was generally low. The monthly average minimum NDVI values during the vegetation growth period mostly occurred before 2010, and the maximum values occurred after 2010. From the spatial perspective, the average vegetation area of the Yellow River Delta accounted for 31.54% of the total study area; specifically, the spatial pattern of vegetation distribution was relatively fixed, and the fixed vegetation area accounted for 63.90% of the total vegetation area. The spatial distribution had significant differences, and the vegetation was distributed radially from the center of the Yellow River to the periphery, with significant fragmentation found outside the watershed. The Yellow River had a strong interference with vegetation growth, and the stable vegetation distribution areas were concentrated near the Yellow River. The correlation coefficient between vegetation distribution and the location of the Yellow River was - 0.9964.

Developing a High-Resolution Seamless Surface Water Extent Time-Series over Lake Victoria by Integrating MODIS and Landsat Data

To effectively monitor the spatio–temporal dynamics of the surface water extent (SWE) in Lake Victoria, this study introduced a novel methodology for generating a seamless SWE time series with fine resolution by integrating daily a Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. In the proposed methodology, daily normalized difference vegetation index (NDVI) time series data with 30 m resolution were first generated based on the constructed pixel-by-pixel downscaling models between the simultaneously acquired MODIS-NDVI and Landsat-NDVI data. In the compositing process, a Minimum Value Composite (MinVC) algorithm was used to generate monthly minimum NDVI time series, which were then segmented into a seamless SWE time series of the years 2000–2020 with 30 m resolution from the cloud background. A comparison with the existing Landsat-derived JRC (European Joint Research Centre) monthly surface water products and altimetry-derived water level series revealed that the proposed methodology effectively provides reliable descriptions of spatio–temporal SWE dynamics. Over Lake Victoria, the average percentage of valid observations made using the JRC’s products was only about 70% due to persistent cloud cover or linear strips, and the correlation with the water level series was poor (R2 = 0.13). In contrast, our derived results strongly correlated with the water level series (R2 = 0.54) and efficiently outperformed the JRC’s surface water products in terms of both space and time. Using the derived SWE data, the long-term and seasonal characteristics of lake area dynamics were studied. During the past 20 years, a significant changing pattern of an initial decline followed by an increase was found for the annual mean SWE, with the lowest area of 66,386.57 km2 in 2006. A general seasonal variation in the monthly mean lake area was also observed, with the largest SWE obtained during June–August and the smallest SWE observed during September–November. Particularly in the spring of 2006 and the autumn of 2020, Lake Victoria experienced intense episodes of drought and flooding, respectively. These results demonstrate that our proposed methodology is more robust with respect to capturing spatially and temporally continuous SWE data in cloudy conditions, which could also be further extended to other regions for the optimal management of water resources.

Quantitative assessment of land surface temperature and vegetation indices on a kilometer grid scale.

Due to expanding populations and thriving economies, studies into the built environment's thermal characteristics have increased. This research tracks and predicts how land use and land cover (LULC) changes may affect ground temperatures, urban heat islands, and city thermal fields (UTFVI). The current study examines land surface temperature (LST), urban thermal field variance index (UTFVI), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and land use land cover (LULC) on a kilometer scale. According to the comparative study, the mean LST decreases by 3°C and the NDVI increases considerably. Correlation analysis showed that LST and NDVI are inversely connected, while LST and NDBI are positively correlated. NDVI and NDBI have a strong negative association, while LST and UTFVI have a positive correlation. Urban planners and environmentalists can study the LST's effects on land surface parameters in different environmental contexts during the lockout period. The urban heat island (UHI) phenomenon, in which the land surface qualities of an urban region cause a change in the urban thermal environment, forms and intensifies over an urban area. The minimum and maximum LST in grid number 1 in 2009 was 20.30°C and 29.91°C, respectively, with a mean LST of 25.1°C. There was a decline in the minimum and maximum LST in grid number 1 in 2020 with a minimum and maximum LST of 17.31°C and 25.35°C, respectively, with a mean LST of 21.33°C. There was a 3.8°C drop in the LST of this grid. The minimum and maximum NDVI were also - 0.16 and 0.59, respectively, with an average NDVI value of 0.21. Therefore, it is essential to evaluate and foresee the impact of LULC change on the thermal environment and examines the connection between LULC shifts with subsequent changes in land surface temperature (LST) along with the UHI phenomenon. Maps of the UTFVI reveal positive UHI phenomena, with the highest UTFVI zones occurring over the developed area and none over the adjacent rural territory. During the summer months, the urban area with the strongest UTFVI zone grows noticeably larger than it does during the winter months during the forecasted years. Future policymakers and city planners can mitigate the effects of heat stress and create more sustainable urban environments by evaluating the expected distribution maps of LULC, LST, UHI, and UTFVI.

Developing Turfgrass Water Response Function and Assessing Visual Quality, Soil Moisture and NDVI Dynamics of Tall Fescue Under Varying Irrigation Scenarios in Inland Southern California

Highlights A turfgrass water response function was developed for 'Westcoaster' tall fescue. The Weathermatic smart controller demonstrated reliable estimations of reference evapotranspiration. Restricting irrigation frequency did not yield water savings or lead to improvements in turfgrass quality. Abstract. Efficient landscape irrigation is an important water conservation strategy and reduces strain on limited water resources in inland Southern California. A three-year field irrigation trial was conducted in Riverside, California, to evaluate the response of 'Westcoaster' tall fescue (Festuca arundinacea Schreb.) to a wide range of autonomous irrigation scenarios implemented using a Weathermatic smart controller. The response of the turfgrass was assessed using visual rating (VR) and normalized difference vegetation index (NDVI) data collected with a handheld sensor. These experimental data were then used to develop a regression-based turfgrass water response function (TWRF), which was further employed to estimate the response of tall fescue to different irrigation levels during years with varying atmospheric evaporation demands, including extreme conditions. A strong correlation was observed between NDVI and VR (r = 0.88). A minimum NDVI threshold of 0.7 was identified to maintain an acceptable level of tall fescue quality for residential areas. During the trial periods, irrigation application rates below 100% of reference evapotranspiration (ETo) were insufficient to sustain the desired quality of tall fescue. The TWRF estimations suggested that 60% ETo can be applied for short periods (7 to 43 days, depending on the atmospheric evaporative demands) during the summer before the visual quality drops below the minimum threshold. Comparing different irrigation strategies, it was observed that on-demand irrigation, which adjusts irrigation frequency based on ETo demand, resulted in higher NDVI and VR values compared to a limited watering schedule of only a few days per week. Analysis of soil moisture data revealed that the primary root water uptake occurred mainly in the topsoil layer (<33 cm), and the applied irrigation water primarily replenished the shallow soil layer (<20 cm) due to the coarse sandy loam soil characteristics. The Weathermatic controller, equipped with on-site air temperature measurements, provided reliable ETo estimations and can be used for autonomous landscape irrigation scheduling in semiarid regions. Keywords: Keywords., Evapotranspiration, Remote sensing, Smart irrigation controller, Urban irrigation, Water conservation.

Impact of illumination gradients on the raw, atmospherically and topographically corrected snow and vegetation areas of Jhelum basin, Western Himalayas

Mountainous terrains severely affect the sun-target-sensor geometry due to surface dispersion of solar radiation, resulting to variation in the observed radiance. The effect of topographic shadow on raw, atmospherically and topographically processed Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI) values was investigated for Jhelum basin, Kashmir Himalaya. The results indicate that NDSI and NDVI derived from raw images show less area of snow and vegetation, due to underestimation of corresponding pixels in the shadow regions, in comparison to atmospherically and topographically processed datasets. Among the three datasets, raw images exhibited low values of minimum and maximum NDSI and NDVI for both shadow and sunlit slopes. All the three datasets show significantly higher values of minimum and maximum NDSI, NDVI and surface temperature in the sunlit slopes, relative to shadow slopes. This research demonstrates that topographic shadow has significant impact on the raw, DOS atmospheric corrected and C topographic processed Landsat-8 Operational Land Imager (OLI) datasets as well as on Thermal Infrared Sensor (TIRS) sensor.

Estimation of Canopy Leaf Area Index of Tropical Rainforests of Sri Lanka from the Normalized Difference Vegetation Index along a Wide Altitudinal Gradient

The leaf area index (LAI) of a forest is a key determinant of its primary productivity. Estimation of LAI via remote sensing of forest canopies offers an alternative to direct measurement, which is difficult in tropical rainforests (TRFs). Our objectives were to determine the variation of LAI and Normalized Difference Vegetation Index (NDVI) of a range of TRFs of Sri Lanka across a wide altitudinal range (117-2,132 m above sea level) and develop a predictive model to estimate LAI from NDVI and canopy architectural properties. Ten permanent sampling plots (PSPs) of one hectare each were established in forest reserves of Kanneliya (117-174 m), Sinharaja-Pitadeniya (509-618 m), Sinharaja-Enasalwatte (1,042-1,065 m), Rilagala (1,668 m), Hakgala (1,804 m), Piduruthalagala (2,080 m) and Horton Plains (2,132 m). Canopy LAI and its architectural properties (i.e., Mean Leaf Angle (MLA) and leaf angle distribution) were computed by analysis of ‘fish-eye’ images obtained from September 2019 to July 2020, using hemispherical photography and HemiView software. Satellite images for March-April, 2020 were downloaded from Landsat 8 OLI/TIRS C2L1. NDVI (NDVI=λNIR-λRED/λNIR+λRED) was calculated from ENVI software, where λNIR and λRED are reflectances of near-infrared and red wavebands. ENVI software computed the maximum NDVI (NDVIMax) and minimum NDVI (NDVIMin) values among 30 m×30 m pixels within each PSP. Mean NDVI (NDVIMean) was computed by taking the mean of NDVI values of all pixels within a PSP. Canopy LAI ranged from 1.94 (Pidurutalagala) to 3.38 (Pitadeniya). The corresponding ranges for NDVIMax, NDVIMean and NDVIMin were 0.620-0.767, 0.594-0.764 and 0.429-0.747 respectively. Canopy LAI, NDVIMax and NDVIMean showed significant (p<0.05) linear decreasing trends with increasing altitude. For every 1,000 m increase in altitude, LAI, NDVIMax and NDVIMean decreased by 0.396 (Adjusted-R2=0.407, AIC=-19.92), 0.066 (0.698,-66.72) and 0.058 (0.493,-61.27). In contrast, NDVIMin did not show a significant trend with altitude. Second-order polynomial functions showed greater explanatory power than the linear functions, in terms of adjusted-R2and AIC, in fitting the variation of LAI (Adj.-R2=0.492, AIC=-20.77) and NDVIMax with altitude (0.755,-68.15). The estimated maximum LAI and NDVIMax were at 637 and 329 m above sea level respectively. From among a range of multiple linear regression models using different combinations of NDVI, canopy architectural properties and altitude, the following two models were selected for predicting LAI, based on their adj-R2 and AIC values: LAI=1.269+[3.567.NDVIMax]–[1.598.NDVIMin]–[0.000215.Altitude] (Adj[1]R2=0.544, AIC=-21.41); LAI=-0.673+[5.887.NDVIMax]–[1.490.NDVIMin]–[0.000452.MLA] (0.489, -20.27).
 Keywords: Normalized Difference Vegetation Index, Leaf Area Index, Tropical rainforests, Altitude, Canopy architecture

Weather, fire, and density drive population dynamics of small mammals in the Brazilian Cerrado

AbstractUnderstanding the relative importance of exogenous and endogenous factors in natural population dynamics has been a central question in ecology. However, until recently few studies used long-term data to assess factors driving small mammal abundance in Neotropical savannas. We used a 9-year data set, based on monthly captures, to understand the population dynamics of two scansorial small mammals inhabiting the Brazilian Cerrado: the semelparous gracile mouse opossum (Gracilinanus agilis), and the iteroparous long-tailed climbing mouse (Rhipidomys macrurus), the two most abundant species at Panga Ecological Station (Uberlândia/MG). We tested the impact of two fires that occurred in 2014 and 2017 on the abundance of both populations. Also, we used Royama’s framework to identify the role of the endogenous system (intraspecific competition) and exogenous factors (annual rainfall, days with minimum and maximum temperatures, annual minimum Normalized Difference Vegetation Index [NDVI], and Southern Oscillation Index) in population dynamics. Extensive and severe fires had a lasting, negative impact on the studied populations, probably by reducing the carrying capacity of the environment. Both populations were influenced by negative first-order feedback, indicating density-dependent effects. Moreover, the endogenous system and the annual minimum NDVI operated as vertical effects determining G. agilis dynamics, while the R. macrurus population was governed by the vertical effects of 1-year lagged rainfall. Our results support the contention that small mammal population fluctuations are driven by the interaction between endogenous (density-dependent) and exogenous factors, which in this study were mainly associated with habitat complexity. Despite ecological similarities shared by both species, their response and recovery time to disturbances and environmental variables varied, probably due to their contrasting life histories. Hence, we emphasize the need to consider species life histories to understand the responses of small mammals to extreme events and reinforce the importance of long-term studies that evaluate the combined effects of endogenous and exogenous variables on population dynamics.

Mangrove-based Ecotourism Sustainability Analysis using NDVI and AHP Approach

This article aims to analyze the sustainability of mangrove ecotourism using the Normalized Difference Vegetation Index (NDVI) and Analytical Hierarchy Process (AHP) approaches. Based on Landsat 8 OLI satellite imagery calculation using the NDVI technique, there has been a decrease in vegetation value on Dodola Island in 2017. This condition needs to be analyzed scientifically, considering the Dodola Island mangrove area to be preserved. In addition to the interests of tourism infrastructure development. The research method used is a mixed research method through a case study approach in Dodola Island, Morotai Island Regency, North Maluku Province, Indonesia. This study adopts remote sensing techniques and decision support systems to describe the results of sustainable mangrove ecotourism analysis. This study indicates that the calculation results of Landsat 8 OLI spatial data from 2013-to 2021 show a significant decrease in vegetation value in 2017, where the maximum NDVI value is 0.30, and the minimum NDVI value is 0.11. Specifically, the mangrove area also experienced a decrease in vegetation value with a maximum NDVI value is 0.23 and a minimum NDVI value is 0.02. To anticipate environmental damage in mangrove areas, this study recommends mangrove conservation programs, namely rehabilitation, restoration, reclamation, and conservation of mangrove areas. In addition, the results of the priority analysis using the AHP approach show that the rehabilitation program is a program that needs to be prioritized because it follows the existing conditions and capabilities of the Dodola Island managers.

An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China

Spectral unmixing remains the most popular method for estimating the composition of mixed pixels. However, the spectral-based unmixing method cannot easily distinguish vegetation with similar spectral characteristics (e.g., different forest tree species). Furthermore, in large areas with significant heterogeneity, extracting a large number of pure endmember samples is challenging. Here, we implement a fractional evergreen forest cover-self-adaptive parameter (FEVC-SAP) approach to measure FEVC at the regional scale from continuous intra-year time-series normalized difference vegetation index (NDVI) values derived from moderate resolution imaging spectroradiometer (MODIS) imagery acquired over southern China, an area with a complex mixture of temperate, subtropical, and tropical climates containing evergreen and deciduous forests. Considering the cover of evergreen forest as a fraction of total forest (evergreen forest plus non-evergreen forest), the dimidiate pixel model combined with an index of evergreen forest phenological characteristics (NDVIann-min: intra-annual minimum NDVI value) was used to distinguish between evergreen and non-evergreen forests within a pixel. Due to spatial heterogeneity, the optimal model parameters differ among regions. By dividing the study area into grids, our method converts image spectral information into gray level information and uses the Otsu threshold segmentation method to simulate the appropriate parameters for each grid for adaptive acquisition of FEVC parameters. Mapping accuracy was assessed at the pixel and sub-pixel scales. At the pixel scale, a confusion matrix was constructed with higher overall accuracy (87.5%) of evergreen forest classification than existing land cover products, including GLC 30 and MOD12. At the sub-pixel scale, a strong linear correlation was found between the cover fraction predicted by our method and the reference cover fraction obtained from GF-1 images (R2 = 0.86). Compared to other methods, the FEVC-SAP had a lower estimation deviation (root mean square error = 8.6%). Moreover, the proposed method had greater estimation accuracy in densely than sparsely forested areas. Our results highlight the utility of the adaptive-parameter linear unmixing model for quantitative evaluation of the coverage of evergreen forest and other vegetation types at large scales.

Predicting bird species presence in urban areas with NDVI: An assessment within and between cities

The increasing urbanization within cities correlates with the loss of green cover, affecting the distribution of bird species. Therefore, to foresee possible effects induced by green cover loss, it is essential to have spatial data of species occurrence. Remote sensors produce spatial data of green cover, such as the Normalized Difference Vegetation Index (NDVI), which can be used to model bird species occurrence within and between cities. The objectives of this study were: 1) to construct and evaluate predictive models between NDVI and bird species distribution in Mar del Plata city, Argentina; and 2) to extrapolate these models to two other cities of the region, Balcarce and Miramar. Most of the 21 species analyzed showed positive associations with NDVI. However, the minimum NDVI values at which species started to be present were dissimilar, showing different habitat requirements. The models of nine species had r-squared values ≥ 0.40, and eight of these species showed an adequate prediction of occurrence in additional areas of Mar del Plata. However, only the occurrence of the Tropical Kingbird (Tyrannus melancholicus) was adequately extrapolated to Miramar and Balcarce. Results showed that NDVI is successful to predict bird species occurrence within a city, but the extrapolation to other cities is not totally adequate. Thus, bird-habitat suitabilities for urban species are considered to be specific for each city.

Land use/land cover changes and their impact on land surface temperature using remote sensing technique in district Khanewal, Punjab Pakistan

ABSTRACT The aim of this research was to assess the land use/land cover (LULC) changes and its impact on land surface temperature (LST) using remote-sensing (RS) technique in the district Khanewal, Punjab, Pakistan. Data were pre-processed using ERDAS imagine 15 and Arc GIS 10.4 software for layer stacking, mosaicking, and sub-setting of Landsat images. After pre-processing, the supervised classification scheme was applied for the years 1980, 2000, and 2020, which explains the maximum likelihood algorithm to identify LULC changes observed in the study area. “Built-up area” in 1980 occupied 1.75% but in 2020, the build-up area increased (5.27%) compared to 2020. Vegetation area was decreased by 4.12% from 1980 to 2020 in district Khanewal. It was observed that there has been a rapid change in vegetation area to build-up area. The LST values were increased by 0.50°C from 1980 to 2020 due to the increasing build-up area from East to West direction in district Khanewal. Maximum and minimum normalized difference vegetation index (NDVI) values were observed 0.72 and −0.2 for 1980 and 2020. The regression line produced a definitive explanation, showing a strong negative correlation with NDVI and LST. The outcomes of this study indicated that a dramatic transformation took place in district Khanewal regarding a decrease in greenness due to a rapid increase in population density, urban growth, and other infrastructural developments. Thus, these results will be used in regional and urban planning and will be used for managing agriculture in the coming years of rapid environmental changes.

Application of Normalized Difference Vegetation Index (NDVI) for the Detection of Extreme Precipitation Change

Remote sensing has frequently been employed to monitor extreme climatic events, especially droughts, by identifying the anomalies of vegetation activity from the regional to global scale. However, limited research has addressed the performance of remote sensing on detecting extreme precipitation events. By using the Middle and Lower Reaches of the Yangtze River (MLR-YR) in China as an example, this paper examines the application of the satellite-derived normalized difference vegetation index (NDVI) for detecting the change of extreme precipitation events from 1982 to 2012. The performances of three NDVI-based indices, including minimum, mean, and maximum NDVIs, were examined to capture the sensibility of vegetation activity to changes in extreme precipitation events. The results show not only common enhanced trends, but also obvious spatial discrepancies between the intensity and frequency of extreme precipitation events in the MLR-YR. As to its application on terrestrial vegetation, changes in extreme precipitation intensity coincided with that of the vegetation activity, which was represented as the maximum and the minimum NDVIs, especially the maximum NDVI. In addition, similar patterns were found between the standard deviation of the maximum NDVI and the trend of extreme precipitation intensity. Furthermore, the correlation coefficients were relatively greater between the maximum NDVI and extreme precipitation intensity than that of the minimum NDVI. Our results support the hypothesis that maximum NDVI is more suited to capture the response of vegetation activity to extreme precipitation events in the MLR-YR region, in comparison to the other two NDVI indices.

Improving the Accuracy of Fractional Evergreen Forest Cover Estimation at Subpixel Scale in Cloudy and Rainy Areas by Harmonizing Landsat-8 and Sentinel-2 Time-Series Data

Evergreen forest provides essential ecosystem services such as maintaining the balance in carbon and oxygen cycles and air purification. However, under cloudy and rainy weather conditions, it is difficult to obtain optical remote sensing images with high spatial resolution and complete time series. In addition, surfaces underlying the forest canopy can be complex and fragmented. To solve this problem, we developed a new approach (NDVI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CV</sub> -LS) for mapping urban evergreen forest at the subpixel scale. In order to capture more accurate growth characteristics of evergreen forest, we harmonized Landsat-8 and Sentinel-2 images with cloud cover less than 10% acquired within 1 year to denser the time-series dataset. In view of the time series fluctuation stability of evergreen forest, the coefficient of variation (CV) of the normalized difference vegetation index (NDVI) was used to distinguish evergreen forest from other vegetation. Meanwhile, the annual minimum NDVI (NDVI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ann-min</sub> ) was used as the parameter in a dimidiate pixel model for estimating fractional evergreen forest cover (FVC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ever</sub> ). Hefei, a cloudy and rainy subtropical city in China, was selected as a case study to evaluate the validity of the model. The verification results revealed that harmonizing Landsat-8 and Sentinel-2 time-series images to extract evergreen forest improved the overall accuracy by 8% compared with using Landsat-8 images alone, indicating that the NDVI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CV</sub> -LS model can improve the accuracy of FCV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ever</sub> estimation, especially for areas with complex underlying surfaces under cloudy and rainy conditions.

Spatial and temporal variation of normalized difference vegetation index (NDVI) in the Uben river basin of Gujarat

The Normalized Difference Vegetation Index (NDVI) is important indicators for monitoring of vegetation status. The remote sensing based bio-physical variable maps were used to estimate NDVI in Uben river catchment and command area of Gujarat state, India. Multi date satellite images of Landsat-8 for Rabi season, year 2018-19 were used in the ArcGIS 10.3 software to derive the Normalized Difference Vegetation Index (NDVI). The maximum NDVI in the study area was 0.566 on February 19, 2019.The minimum NDVI in the study area was -0.327 on February 19, 2019. The average NDVI value ranges from -0.132 to 0.368 for wheat crop, 0.092 to 0.294 for coriander crop, 0.098 to 0.204 for cotton crop and 0.083 to 0.103 for waste land.The average NDVI value for wheat and coriander crop was increase from Initial stage to Mid-crop growth stage and then decreased during End season stage. The cotton crop shows higher NDVI value in December i.e. 0.204 then decreased due to harvesting of cotton. The average NDVI values in waste land were less as compared to agricultural land with crop and forest area due to no vegetation and exposed soil.

Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE &lt; 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

Long- Term Estimation of Actual Crop Evapotranspiration Based on Satellite Data Analysis

Abstract Arid regions conditions are overwhelmed with various water scarcities problems, however, one of the reasons of this problems may be due to climate change effect. Moreover, under these conditions, water is a crucial key for any agricultural development processes. Therefore, the aim of this study was to estimate the actual evapotranspiration of different crop pattern under climate change conditions based on satellite data analysis for long time effect (1985 – 2019). The study area is considered active agricultural area , the percentage of bare soil and vegetation cover were (86.9%) and (12.7) in 1985 while in 2019 the percentage of bare soil sharply decrease so it was reached to (34.1%) but the percentage of d vegetation cover was (64.2%). Satellite images have been gathered and analyzed from Landsat TM, Landsat ETM+ and Landsat OLI, through the studied period. However, Normalized Difference Vegetation Index (NDVI) had been obtained for estimating the actual evapotranspiration under the studied area conditions. Planning and management of water use by irrigation agriculture are especially important in an arid and semiarid areas like the study area so the understanding of the Actual Evapotranspiration (AET) prerequisite for better management and conservation of agriculture water, By determine the area of every crop pattern, actual evapotranspiration (AET) was estimated by m3/ha/day and m3/total cultivated area/day. Data analysis indicate that the minimum and maximum NDVI values had been ranged from 0.7 and 0.9 for mango trees from 1985 and 2019, 0.5 and 0.8 for olive trees, 0.4 and 0.8 for forests and 0.7 for annual crops within the studied period from 1985 to 2019. The minimum and maximum actual evapotranspiration under different crop pattern from 1985 to 2019 ranged from 2.6 and 4.9 mm/day, 1.7 and 4 mm/day, 1 to 4.4 mm/day and 2.7 mm/day for the same crop pattern respectively. This study can assist in water management practices for better irrigation management.

Acquisitions and applications of forest canopy hyperspectral imageries at hotspot and multiview angle using unmanned aerial vehicle platform

The accurate retrieval of forest functional and structural parameters is of great significance in the scientific research of ecosystem, global change, and carbon and nitrogen cycles. Recently, an unmanned aerial vehicle (UAV) hyperspectral imaging system provides a cost-effective way to capture the hyperspectral imageries from any points of the hemisphere above a forest canopy. However, compared with single-angle hyperspectral images, the multiangle hyperspectral images provide more information about forest functional and structural characteristics. We developed a semiautomatic multiangle observation method using a UAV hyperspectral imaging system and successfully collected the multiangle hyperspectral imageries with clear hotspots of broadleaf and coniferous forest canopies. Our results indicated that the hotspot of a forest canopy had a great effect on the reflectance, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) of forests. The maximum values of canopy reflectance and EVI were found at the hotspot position, while the minimum NDVI was at the hotspot. Moreover, the hotspot effect was similar in both broadleaf and coniferous forests. Although the hotspot had no obvious effects on the photochemical reflectance index (PRI), different view zenith angles had a great effect on PRI. Our findings provide a solid foundation for retrieving forest structural parameters using fully automatic multiangle hyperspectral imaging system at both aerial and satellite platforms.

Compositing the Minimum NDVI for Daily Water Surface Mapping

Capturing high frequency water surface dynamics via optical remote sensing is important for understanding hydro-ecological processes over seasonally flooded wetlands. However, it is a difficult task due to the presence of clouds on satellite images. This study proposed the MODerate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) Minimum Value Composite (MinVC) algorithm to generate daily water surface data at a 250-m resolution. The algorithm selected pixelwise minimum values from the combined daily Terra and Aqua MODIS NDVI data within a 15-day moving window. Consisting mainly of cloud and water surface information, the MinVC NDVI data were segmented for water surfaces over the Poyang Lake, China (2000–2017) by using an edge detection model. The water surface mapping result was strongly correlated with the Landsat based result (R2 = 0.914, root mean square error, RMSE = 223.7 km2), the cloud free MODIS image based result (R2 = 0.824, RMSE = 356.7 km2), the recent Landsat-MODIS image fusion based result (R2 = 0.765, RMSE = 403 km2), and the hydrodynamic modeling result (R2 = 0.799). Compared to the equivalent eight-day MOD13 NDVI based on the Constraint View-Angle Maximum Value Composite (CV-MVC) algorithm, the daily MinVC NDVI highlighted water bodies by generating spatially homogenous water surface information. Consequently, the algorithm provided spatially and temporally continuous data for calculating water submersion times and trends in water surface area, which contribute to a better understanding of hydro-ecological processes over seasonally flooded wetlands. Within the framework of sensor intercalibration, the algorithm can be extended to incorporate multiple sensor data for improved water surface mapping.