Normalized Difference Vegetation Index Products Research Articles

Ecological Risk Assessment using Satellite Derived NDVI, HFP and Rainfall Erosivity in Nepal

Understanding vegetation dynamics is becoming increasingly crucial to maintain ecosystem in the Himalaya region. An assessment of the normalized difference vegetation index (NDVI) and human footprint pressure indicates the ecological risk scenario of Nepal. The original NOAA NDVI product available from 1981-2015 were used. Similarly, Human footprint pressure data for 1993 and 2009 were used. The study purpose was to develop ecological risk map based on NDVI dynamics and human pressure to natural resources. The result showed that the intensity of vegetation dynamics was 0.065 yr-1 with large positive and negative intensities observed in 2006 and 1984, respectively. Both of the intensity of vegetation changes and variance of NDVI showed increased ecological fragility in the Tran-Himalayan region. However, negative correlation between NDVI and rainfall erosivity showed reduced soil erosion and ecological risk in the region. The human footprint pressure has increased up to 43 which together with increasing annual population growth trend (1.35 yr-1) has exerted pressure on the natural system and created ecological risk. The results have important implications for a better understanding of the vegetation dynamics in response to ongoing climatic variability and associated ecological risks in Nepal.

ОЦЕНКА УРОЖАЙНОСТИ ОЗИМОЙ ПШЕНИЦЫ С ИСПОЛЬЗОВАНИЕМ МЕТОДА ГЛАВНЫХ КОМПОНЕНТ НА ОСНОВЕ КОМПЛЕКСИРОВАНИЯ СПУТНИКОВОЙ И НАЗЕМНОЙ ИНФОРМАЦИИ

The results of the principal component analysis application for estimating the average regional winter wheat yield based on the integration of satellite and ground meteorological information for the southern regions of the Russian Federation are presented. The satellite indices such as NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index), and satellite product LAI (Leaf Area Index) were used. Meteorological information was represented by temperature, humidity deficit, total precipitation, and the Selyaninov hydrothermal coefficient. The parameters that have the greatest impact on the yield were selected. The components with the largest eigenvalues were extracted from this set of parameters using the principal component analysis. Equations for the dependence of the winter wheat yield on the extracted components were calculated. To check the equations, the expected yield for the period from 2012 to 2017 was calculated. The relative error varied within 5-12%. In all cases, the yield calculation error when using the principal component analysis is smaller than when using correlation and regression dependences.

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

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

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

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

Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion

The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise. Although numerous reconstruction methods have been developed, they have not effectively addressed the issues associated with large gaps in the time series over cloudy and rainy regions, due to the insufficient utilization of the spatial, temporal and periodical correlations. In this paper, an adaptive Spatio-Temporal Tensor Completion method (termed ST-Tensor) method is proposed to reconstruct long-term NDVI time series in cloud-prone regions, by making full use of the multi-dimensional spatio-temporal information simultaneously. For this purpose, a highly-correlated tensor is built by considering the correlations among the spatial neighbors, inter-annual variations, and periodic characteristics, in order to reconstruct the missing information via an adaptive-weighted low-rank tensor completion model. An iterative ℓ1 trend filtering method is then implemented to eliminate the residual temporal noise. This new method was tested using MODIS 16-day composite NDVI products from 2001 to 2018 obtained in Mainland Southeast Asia, where the rainy climate commonly induces large gaps and noise in the data. The qualitative and quantitative results indicate that the ST-Tensor method is more effective than the five previous methods in addressing the different missing data problems, especially the temporally continuous gaps and spatio-temporally continuous gaps. It is also shown that the ST-Tensor method performs better than the other methods in tracking NDVI seasonal trajectories, and is therefore a superior option for generating high-quality long-term NDVI time series for cloud-prone regions.

Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China

Nowadays urban climate is a global problem and many studies focused on understanding the relation between urban climate the built-up space using radiometric observations of the land surface temperature to estimate and monitor the surface urban heat island intensity (SUHIs). In this study MODIS land surface temperature (LST) data were used. The Yangtze River Delta Urban Agglomeration (YRDUA), eastern China, was selected as an example to study SUHI and multiple influencing factors in 16 big cities. Anthropogenic factors are considered the most important ones in determining SUHI, while natural factors remain influential. By using stratified random sampling (SRS), 78,085 random points were selected within the 16 cities. Nine influencing factors were selected in this study: distance from building (BD), distance from the main roads (RD), distance from water (WD), digital elevation model product (DEM), gross domestic product (GDP), normalized difference vegetation index product (NDVI), nighttime lighting intensity (NTI), population (POP) and impervious surface area data (%ISA). The SUHI intensity was extracted at each random point as well as the values of the influencing factors, NDVI, DEM, ISA, POP, NTI and GDP. For BD, WD and RD, random points were selected from the water, building and main roads using the near tool in ArcGIS to measure these distances. Boosted regression tree (BRT) model was applied to capture the contributions of the above factors to SUHI. We also applied a different procedure to evaluate the relative influence of Land Use and Land Cover (LULC). The relative influence refers to the contribution of each factor to determine SUHI. The influencing factors were ranked on the basis of the relative influence on SUHI. The results showed that (1) higher SUHI intensity was recorded in Shanghai, Jiaxing and Nanjing cities respectively, while Hangzhou recorded the lowest SUHI. (2) Anthropogenic drivers have slightly higher relative influence on SUHI than natural drivers, i.e. 51.29% and 48.71% respectively. The influence of all drivers on SUHI from high to low is NTI (27.62%), ISA (24.38%), NDVI (12.11%), GDP (7.95%), DEM (7.29%), POP (6.37%), BD (5.33%), WD (4.93%), RD (4.02%). (3) The variation in the socioeconomic level lead to different spatial patterns of different influence factors, further indicating that the overall mean SUHI intensity is affected by the development of the city.

Spatiotemporal Peatland Productivity and Climate Relationships Across the Western South American Altiplano

AbstractThe South American Altiplano is one of the largest semiarid high‐altitude plateaus in the world. Within the Altiplano, peatlands known as “bofedales” are important components of regional hydrology and provide key water resources and ecosystem services to Andean communities. Warming temperatures, changes in hydroclimate, and shifting atmospheric circulation patterns all affect peatland dynamics and hydrology. It is therefore urgent to better understand the relationships between climate variability and the spatiotemporal variations in peatland productivity across the Altiplano. Here, we explore climate influences on peatland vegetation using 31 years of Landsat data. We focus specifically on the bofedal network in the western Altiplano, the driest sector of the plateau, and use the satellite‐derived Normalized Difference Vegetation Index (NDVI) as an indicator of productivity. We develop temporally and spatially continuous NDVI products at multiple scales in order to evaluate relationships with climate variables over the past three decades. We demonstrate that cumulative precipitation and snow persistence over the prior 2 years are strongly associated with growing season productivity. A step change in peatland productivity between 2013–2015 drives an increasing trend in NDVI and is likely a response to consecutive years of anomalously high snow accumulation and rainfall. Early summer minimum temperatures emerge as a secondary influence on productivity. Understanding large‐scale productivity dynamics and characterizing the response of bofedales to climate variability over the last three decades provides a baseline to monitor the responses of Andean peatlands to climate change.

Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

Correction of Directional Effects in VEGETATION NDVI Time-Series

Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V satellites) have led to the acquisition of consistent time-series of Normalized Difference Vegetation Index (NDVI) at a global scale. The wide imaging swath (>2000 km) of the family of VEGETATION space-borne sensors ensures a daily coverage of the Earth at the expense of a varying observation and illumination geometries between successive orbit overpasses for a given target located on the ground. Such angular variations infer saw-like patterns on time-series of reflectance and NDVI. The presence of directional effects is not a real issue provided that they can be properly removed, which supposes an appropriate BRDF (Bidirectional Reflectance Distribution Function) sampling as offered by the VEGETATION program. An anisotropy correction supports a better analysis of the temporal shapes and spatial patterns of land surface reflectance values and vegetation indices such as NDVI. Herein we describe a BRDF correction methodology, for the purpose of the Copernicus Global Land Service framework, which includes notably an adaptive data accumulation window and provides uncertainties associated with the NDVI computed with normalized reflectance. Assessing the general performance of the methodology in comparing time-series between normalized and directional NDVI reveals a significant removal of the high-frequency noise due to directional effects. The proposed methodology is computationally efficient to operate at a global scale to deliver a BRDF-corrected NDVI product based on long-term Time-Series of VEGETATION sensor and its follow-on with the Copernicus Sentinel-3 satellite constellation.

Ecological Monitoring with Spy Satellite Images—The Case of Red Wood Ants in Romania

Dynamics of habitat conditions drive important changes in distribution and abundance of animal species making monitoring an important but also a challenging task when data from the past are scarce. We compared the distribution of ant mounds in the 1960s with recent inventories (2018), looking at changes in canopy cover over time, in a managed forest. Both historical and recent sources of information were used. Habitat suitability at present was determined using a Normalized Difference Vegetation Index (NDVI) image as a proxy for stand canopy cover. The NDVI product was obtained using Google Earth Engine and Sentinel 2 repository. For past conditions (no spectral information available), presence of edges and more open canopies was assessed on a Corona spy-satellite image and based on information from old forest management plans. A threshold distance of 30 m was used to assess location of ant nests compared to favorable habitats. Both old and new information sources showed that ants prefer intermediate canopy cover conditions in their vicinity. Nests remained clustered because of the heterogeneous habitat conditions, but spatial distribution has changed due to canopy alteration along time. The analysis on the NDVI was effective for 82% of cases (i.e., nests occurred within 30 m from favorable habitats). For all the remaining nests (18%), the Google Earth high resolution satellite image revealed in their vicinity the presence of small canopy gaps (undetected by the NDVI). These results show that historical satellite images are very useful for explaining the long-term dynamics of ant colonies. In addition, the use of modern remote sensing techniques provides a reliable and expedite method in determining the presence of favorable small-scale habitat, offering a very useful tool for ecological monitoring across large landscapes and in very different areas, especially in the context of ecosystem dynamics driven and exacerbated by climate change.

Aerosol optical depth (AOD): spatial and temporal variations and association with meteorological covariates in Taklimakan desert, China.

Aerosol optical depth (AOD) is a key parameter that reflects aerosol characteristics. However, research on the AOD of dust aerosols and various environmental variables is scarce. Therefore, we conducted in-depth studies on the distributions and variations of AOD in the Taklimakan Desert and its margins, China. We examined the correlation characteristics between AOD and meteorological factors combined with satellite remote sensing detection methods using MCD19A2-MODIS AOD products (from 2000, 2005, 2010, and 2015), MOD13Q1-MODIS normalized difference vegetation index products, and meteorological data. We analyzed the temporal and spatial distributions of AOD, periodic change trends, and important impacts of meteorological factors on AOD in the Taklimakan Desert and its margins. To explore the relationships between desert aerosols and meteorological factors, a random forest model was used along with environmental variables to predict AOD and rank factor contributions. Results indicated that the monthly average AOD exhibited a clear unimodal curve that reached its maximum in April. The AOD values followed the order spring (0.28) > summer (0.27) > autumn (0.18) > winter (0.17). This seasonality is clear and can be related to the frequent sandstorms occurring in spring and early summer. Interannual AOD showed a gradually increasing trend to 2010 then large changes to 2015. AOD tends to increase from south to north. Based on the general trend, the maximum value of AOD is more dispersed and its low-value area is always stable. The climatic index that has the most significant effect on AOD is relative humidity.

A Gaussian Kernel-Based Spatiotemporal Fusion Model for Agricultural Remote Sensing Monitoring

Time series normalized difference vegetation index (NDVI) is the primary data for agricultural remote sensing monitoring. Due to the tradeoff between a single sensor's spatial and temporal resolutions and the impacts of cloud coverage, the time series NDVI data cannot serve well for precision agriculture. In this study, a Gaussian kernel-based spatiotemporal fusion model (GKSFM) was developed to fuse high-resolution NDVI (Landsat) and low-resolution NDVI (MODIS) to produce a daily NDVI product at a 30-m spatial resolution. Considering that the NDVI curve of crop in each growing season can be characterized by Gaussian function, GKSFM used the Gaussian kernel to fit the nonlinear relationship between the high-resolution NDVI and the low-resolution NDVI, to obtain a more reasonable temporal increment. The experimental results show that GKSFM outperformed the comparative models in different proportions of cropland/noncropland and different crop phenology. In addition, GKSFM was also applied for crop mapping of Mishan County by fusing the NDVI images during the crop growing season. This study demonstrates that the accuracy of the proposed method can be improved in the midseason of crops.

Environmental Drivers of Water Use for Caatinga Woody Plant Species: Combining Remote Sensing Phenology and Sap Flow Measurements

We investigated the water use of Caatinga vegetation, the largest seasonally dry forest in South America. We identified and analysed the environmental phenological drivers in woody species and their relationship with transpiration. To monitor the phenological evolution, we used remote sensing indices at different spatial and temporal scales: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and green chromatic coordinate (GCC). To represent the phenology, we used the GCC extracted from in-situ automated digital camera images; indices calculated based on sensors included NDVI, SAVI and GCC from Sentinel-2A and B satellites images, and NDVI products MYD13Q1 and MOD13Q1 from a moderate-resolution imaging spectroradiometer (MODIS). Environmental drivers included continuously monitored rainfall, air temperature, soil moisture, net radiation, and vapour pressure deficit. To monitor soil water status and vegetation water use, we installed soil moisture sensors along three soil profiles and sap flow sensors for five plant species. Our study demonstrated that the near-surface GCC data played an important role in permitting individual monitoring of species, whereas the species’ sap flow data correlated better with NDVI, SAVI, and GCC than with species’ near-surface GCC. The wood density appeared to affect the transpiration cessation times in the dry season, given that species with the lowest wood density reach negligible values of transpiration earlier in the season than those with high woody density. Our results show that soil water availability was the main limiting factor for transpiration during more than 80% of the year, and that both the phenological response and water use are directly related to water availability when relative saturation of the soil profile fell below 0.25.

Modeling and Assessment of Land Degradation Vulnerability in Semi-arid Ecosystem of Southern India Using Temporal Satellite Data, AHP and GIS

Globally, land degradation becomes a serious environmental issue in the context of anthropogenic pressure and climate change in the twenty-first century. Modeling and assessment of land degradation vulnerability assume a greater importance especially in the semi-arid ecosystems for sustainable land resource management. Consistent and reliable earth observation satellite datasets, Analytic Hierarchy Process (AHP), and Geographic Information System (GIS) are the powerful tools to model and assess the land degradation vulnerability. The present study was aimed to model and assess the land degradation vulnerability through analysis of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), rainfall, terrain characteristics, and pedological parameters by using AHP and GIS in the semi-arid ecosystem of Rayalaseema region of southern India. The NDVI and LST products derived from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, rainfall products from Tropical Precipitation Measuring Mission (TRMM), terrain characteristics from Shuttle Radar Topography Mission (SRTM) (30 m), and pedological parameters derived from legacy soil datasets were used in the study. The AHP- and GIS-based modeling shows that about 25.3 and 9.9% of the study area were under high and very high vulnerability to land degradation, respectively. Inadequate rainfall and vegetative cover, high temperature, problematic soils, and lack of adequate conservation measures were found to be the main causative environmental factors for land degradation in the study area. The study clearly demonstrates the potential of AHP- and GIS-based modeling in the assessment of land degradation vulnerability by using the time-series MODIS NDVI- and TRMM-based rainfall products, terrain characteristics, and pedological parameters.

Ability of Remote Sensing Systems to Detect Bark Beetle Spots in the Southeastern US

Research Highlights: Sentinel-2 Normalized Difference Vegetation Index (NDVI) products show greater potential to detect indications of disturbance by bark beetles in the southeastern US than Moderate Resolution Imaging Spectroradiometer (MODIS), as the high spatiotemporal heterogeneity of the southeastern forest land prevents its deployment at the current resolution. Background and Objectives: Remote sensing technologies have been an essential tool to detect forest disturbances caused by insect pests through spectral trait variation. In the US, coordinated efforts such as ForWarn, led by the US Forest Service and based on MODIS satellite data, are used to monitor biotic and abiotic disturbances. Because of the particular characteristics of the southeastern US landscape, including forest fragmentation and rapid forest turnover due to management, detection and visualization of small bark beetle spots using remote sensing technology developed for more homogeneous landscapes has been challenging. Here, we assess the ability of MODIS and Sentinel-2 time-series vegetation index data products to detect bark beetle spots in the Florida Panhandle. Materials and Methods: We compared ForWarn’s detection ability (lower resolution images) with that of Sentinel-2 (higher resolution images) using bark beetle spots confirmed by aerial surveys and ground checks by the Florida Forest Service. Results: MODIS and Sentinel-2 can detect damage produced by bark beetles in the southeastern US, but MODIS detection via NDVI change exhibits a high degree of false negatives (30%). Sentinel-2 NDVI products show greater potential for identifying indications of disturbance by bark beetles than MODIS change maps, with Sentinel-2 capturing negative changes in NDVI for all spots. Conclusions: Our research shows that for practical bark beetle detection via remote sensing, higher spatial and temporal resolution will be needed.

Assessment of a Spatially and Temporally Consistent MODIS Derived NDVI Product for Application in Index-Based Drought Insurance

In arid and semi-arid regions of Eastern and Southern Africa, drought can be devastating to pastoralists who depend on healthy vegetation for their herds. The Kenya Livestock Insurance Program (KLIP) addresses this challenge through its insurance program that relies on a vegetation index product derived from eMODIS NDVI (enhanced Normalized Difference Vegetation Index). Insurance payouts are triggered when index values fall below a certain threshold for a Unit Area of Insurance (UAI). The objective of this study is to produce an updated, cloud-based NDVI product, potentially allowing for earlier payouts that may help herders to prevent, minimize, or offset drought-induced losses. The new product, named reNDVI (rapid enhanced NDVI), provides an updated cloud filtering algorithm and brings the entire processing chain to the cloud. Access to the scripts used for the processing described and resulting data is openly available. To test the performance of the new product, we provide a robust evaluation of reNDVI and eMODIS NDVI and their derived payout indices against historical drought, payouts provided, and mortality data. The implications of potential payout differences are also discussed. The products show good comparability; the monthly average NDVI per UAI has correlation values over 0.95 and MAPD under 5% for most UAIs. However, there are moderate differences when assessing year-to-year payout amounts triggered. Because the payouts are currently calculated based on the 20th and first percentile of index values from 2003–2016, payouts are very sensitive to even small changes in NDVI. Where livestock mortality was available, payouts for reNDVI and eMODIS had similar correlations (r = 0.453 and r = 0.478, respectively) with mortality rates. Therefore, with the potential reduced latency and updated cloud filtering, the reNDVI product could be a suitable replacement for eMODIS in the Kenya Livestock Insurance Program. The updated reNDVI product shows promise as a vegetation index that could address a pressing drought insurance challenge.

Experimental Evaluation and Consistency Comparison of UAV Multispectral Minisensors

In recent years, the use of unmanned aerial vehicles (UAVs) has received increasing attention in remote sensing, vegetation monitoring, vegetation index (VI) mapping, precision agriculture, etc. It has many advantages, such as high spatial resolution, instant information acquisition, convenient operation, high maneuverability, freedom from cloud interference, and low cost. Nowadays, different types of UAV-based multispectral minisensors are used to obtain either surface reflectance or digital number (DN) values. Both the reflectance and DN values can be used to calculate VIs. The consistency and accuracy of spectral data and VIs obtained from these sensors have important application value. In this research, we analyzed the earth observation capabilities of the Parrot Sequoia (Sequoia) and DJI Phantom 4 Multispectral (P4M) sensors using different combinations of correlation coefficients and accuracy assessments. The research method was mainly focused on three aspects: (1) consistency of spectral values, (2) consistency of VI products, and (3) accuracy of normalized difference vegetation index (NDVI). UAV images in different resolutions were collected using these sensors, and ground points with reflectance values were recorded using an Analytical Spectral Devices handheld spectroradiometer (ASD). The average spectral values and VIs of those sensors were compared using different regions of interest (ROIs). Similarly, the NDVI products of those sensors were compared with ground point NDVI (ASD-NDVI). The results show that Sequoia and P4M are highly correlated in the green, red, red edge, and near-infrared bands (correlation coefficient (R2) > 0.90). The results also show that Sequoia and P4M are highly correlated in different VIs; among them, NDVI has the highest correlation (R2 > 0.98). In comparison with ground point NDVI (ASD-NDVI), the NDVI products obtained by both of these sensors have good accuracy (Sequoia: root-mean-square error (RMSE) < 0.07; P4M: RMSE < 0.09). This shows that the performance of different sensors can be evaluated from the consistency of spectral values, consistency of VI products, and accuracy of VIs. It is also shown that different UAV multispectral minisensors can have similar performances even though they have different spectral response functions. The findings of this study could be a good framework for analyzing the interoperability of different sensors for vegetation change analysis.

Estimation of Crop Water Deficit in Lower Bari Doab, Pakistan Using Reflection-Based Crop Coefficient

There is a global realization in all governmental setups of the need to provoke the efficient appraisal of crop water budgeting in order to manage water resources efficiently. This study aims to use the satellite remote sensing techniques to determine the water deficit in the crop rich Lower Bari Doab Canal (LBDC) command area. Crop classification was performed using multi-temporal NDVI profiles of Landsat-8 imagery by distinguishing the crop cycles based on reflectance curves. The reflectance-based crop coefficients (Kc) were derived by linear regression between normalized difference vegetation index (NDVI) cycles of the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and MYD13Q1 products and Food and Agriculture Organization (FAO) defined crop coefficients. A MODIS 250 m NDVI product of the last 10 years (2004-2013) was used to identify the best performing crop cycle using Fourier filter method. The meteorological parameters including rainfall and temperature substantiated the reference evapotranspiration (ET0) calculated using the Hargreaves method. The difference of potential ET and actual ET, derived from the reflectance-based Kc calculated using reference NDVI and current NDVI, generates the water deficit. Results depict the strong correlation between ET, temperature and rainfall, as the regions having maximum temperature resulted in high ET and low rainfall and vice versa. The derived Kc values were observed to be accurate when compared with the crop calendar. Results revealed maximum water deficit at middle stage of the crops, which were observed to be particularly higher at the tail of the canal command. Moreover, results also depicted that kharif (summer) crops suffer higher deficit in comparison to rabi (winter) crops due to higher ET demand caused by higher temperature. Results of the research can be utilized for rational allocation of canal supplies and guiding farmers towards usage of alternate sources to avoid crop water stress.

Evaluation of NDVI Estimation Considering Atmospheric and BRDF Correction through Himawari-8/AHI

Satellite-based vegetation indices are an essential element in understanding the Earth’s surface. In this study, we estimated the normalized difference vegetation index (NDVI) using Himawari-8/Advanced Himawari Imager (AHI) data and analyzed the sensitivity of products to atmospheric and surface correction. We used the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model for atmospheric correction, and kernel-based semi-empirical bidirectional reflectance distribution function (BRDF) model to remove surface anisotropic effects. From this, top-of-atmosphere, top-of-canopy, and normalized NDVIs were produced. A sensitivity analysis showed that the normalized NDVI had the lowest number of missing values compared with the others and almost no low peaks during the study period. These results were validated by Terra and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) and Project for On-Board Autonomy/Vegetation (PROBA) NDVI product, showing the root mean square error (RMSE) and bias of 0.09 and + 0.04 (MODIS) and 0.09 and − 0.04 (PROBA), respectively. These results also satisfied the FP7 Geoland2/BioPar project-defined user requirements (threshold: 0.15; target: 0.10).

Vegetation expansion in the subnival Hindu Kush Himalaya.

The mountain systems of the Hindu Kush Himalaya (HKH) are changing rapidly due to climatic change, but an overlooked component is the subnival ecosystem (between the treeline and snow line), characterized by short‐stature plants and seasonal snow. Basic information about subnival vegetation distribution and rates of ecosystem change are not known, yet such information is needed to understand relationships between subnival ecology and water/carbon cycles. We show that HKH subnival ecosystems cover five to 15 times the area of permanent glaciers and snow, highlighting their eco‐hydrological importance. Using satellite data from the Landsat 5, 7 and 8 missions, we measured change in the spatial extent of subnival vegetation from 1993 to 2018. The Landsat surface reflectance‐derived Normalized Difference Vegetation Index product was thresholded at 0.1 to indicate the presence/absence of vegetation. Using this product, the strength and direction of time‐series trends in the green pixel fraction were measured within three regions of interest. We controlled for cloud cover, snow cover and evaluated the impact of sensor radiometric differences between Landsat 7 and Landsat 8. Using Google Earth Engine to expedite data processing tasks, we show that there has been a weakly positive increase in the extent of subnival vegetation since 1993. Strongest and most significant trends were found in the height region of 5,000–5,500 m a.s.l. across the HKH extent: R 2 = .302, Kendall's τ = 0.424, p < .05, but this varied regionally, with height, and according to the sensors included in the time series. Positive trends at lower elevations occurred on steeper slopes whilst at higher elevations, flatter areas exhibited stronger trends. We validated our findings using online photographs. Subnival ecological changes have likely impacted HKH carbon and water cycles with impacts on millions of people living downstream, but the strength and direction of impacts of vegetation expansion remain unknown.