Green Normalized Difference Vegetation Index Research Articles

Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm.

Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), Rblue (reflectance at blue band), Rgreen (reflectance at green band), Rred (reflectance at red band) and Rnir (reflectance at near infrared band). The partial least square (PLS) algorithm was used to construct and validate the multivariate remote sensing model of predicting wheat GPC. The research showed a close relationship between wheat GPC and 12 remote sensing variables other than Rblue and Rgreen of the spectral reflectance bands. Among them, except PSRI and Rblue, Rgreen and Rred, other remote sensing vegetation indexes had significant multiple correlations. The optimal principal components of PLS model used to predict wheat GPC were: NDVI, SIPI, PSRI and EVI. All these were sensitive variables to predict wheat GPC. Through modeling set and verification set evaluation, GPC prediction models' coefficients of determination (R2) were 0.84 and 0.8, respectively. The root mean square errors (RMSE) were 0.43% and 0.54%, respectively. It indicated that the PLS algorithm model predicted wheat GPC better than models for linear regression (LR) and principal components analysis (PCA) algorithms. The PLS algorithm model’s prediction accuracies were above 90%. The improvement was by more than 20% than the model for LR algorithm and more than 15% higher than the model for PCA algorithm. The results could provide an effective way to improve the accuracy of remotely predicting winter wheat GPC through satellite images, and was conducive to large-area application and promotion.

Yield estimation in cotton using UAV-based multi-sensor imagery

Monitoring crop development and accurately estimating crop yield are important to improve field management and crop production. This study aimed to evaluate the performance of an unmanned aerial vehicle (UAV)-based remote sensing system in cotton yield estimation. A UAV system, equipped with an RGB camera, a multispectral camera, and an infrared thermal camera, was used to acquire images of a cotton field at two growth stages (flowering growth stage and shortly before harvest). Sequential images from the three cameras were processed to generate orthomosaic images and a digital surface model (DSM), which were registered to the georeferenced yield data acquired by a yield monitor mounted on a harvester. Eight image features were extracted, including normalised difference vegetation index ( NDVI ), green normalised difference vegetation index ( GNDVI ), triangular greenness index ( TGI ), a channel in CIE-LAB colour space ( a∗ ), canopy cover, plant height ( PH ), canopy temperature, and cotton fibre index ( CFI ). Models were developed to evaluate the accuracy of each image feature for yield estimation. Results show that PH and CFI were the best single features for cotton yield estimation, both with R 2 = 0.90. The combination of PH and CFI , PH and a ∗, or PH and temperature were the best two-feature models with R 2 from 0.92 to 0.94. The best three-feature models were among the combinations of PH , CFI , temperature and a ∗. This study found that UAV-based images collected during the flowering growth stage and/or shortly before harvest were able to estimate cotton yield accurately. • Site-specific cotton yield estimation was conducted. • The performance of multiple image sensors were evaluated. • Data were registered based on image features. • Methods were potentially used for precision crop management.

FROM PIXEL TO YIELD: FORECASTING POTATO PRODUCTIVITY IN LEBANON AND IDAHO

Abstract. Idaho and Lebanon rely on potatoes as an economically important crop. NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and MSAVI2 (Modified Soil Adjusted Vegetation Index 2) indices were calculated from PlanetScope satellite imagery for the 2017 growing season cloud free days. Variations in vegetation health were tracked over time and correlated to yield data provided by growers in Idaho. Based on ordinary least squares regression an Idaho yield forecast model was developed. Vegetation response during the growth stage at which potato tubers were filling out was significant in predicting yield for both the Norkotah and Russet potato variety. This corresponded to a week with high recorded temperatures that impacted the health status of the crops. The yield forecasting model was validated with a cross validation approach and then applied to potato fields in Lebanon. The Idaho model successfully displayed yield variation in crops for Lebanon. Spectral indices along with field topography allow the prediction of yield based on the crop type and variety.

Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia

The monitoring of inland water resources in arid environments is an essential element due to their fragility. Reliable prediction of the water quality parameters helps to control and manage the water resources in arid regions. Water quality parameters were estimated using remote sensing data acquired from the beginning of 2017 until the end of 2018. The prediction of the water quality parameters was comprehended by using an adjusted autoregressive integrated moving average (ARIMA) and its extension seasonal ARIMA (S-ARIMA). Maximum Chlorophyll Index (MCI), Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Turbidity Index (NDTI) were the tested water quality parameters using Sentinel-2 sensor on temporal resolution basis of the sensor. Results indicated that the implementation of the ARIMA model failed to sustain a reliable prediction longer than one-month time while S-ARIMA succeeded to maintain a robust prediction for the first 3 months with confidence level of 96%. MCI has its ARIMA at (1,2,2) and S-ARIMA at (1,2,2) (2,1,1)6, GNDVI has its ARIMA at (2,1,2) and S-ARIMA at (2,1,2) (2,2,2)6, and finally, NDTI has its ARIMA at (2,2,2) and S-ARIMA at (2,2,2) (1,1,2)6. The accuracy of S-ARIMA predictions reached 82% at 6-month prediction period. Meanwhile, there was no solid prediction model that lasted till 12 months. Each of the forecasted water quality parameters is unique in its prediction settings. S-ARIMA model is a more reliable model because the seasonality feature is inherited within the forecasted water quality parameters.

Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index

In this study, aquatic plants in a small reservoir were detected using multispectral UAV (Unmanned Aerial Vehicle) imagery and various vegetation indices. A Firefly UAV, which has both fixed-wing and rotary-wing flight modes, was flown over the study site four times. A RedEdge camera was mounted on the UAV to acquire multispectral images. These images were used to analyze the NDVI (Normalized Difference Vegetation Index), ENDVI (Enhance Normalized Difference Vegetation Index), NDREI (Normalized Difference RedEdge Index), NGRDI (Normalized Green-Red Difference Index), and GNDVI (Green Normalized Difference Vegetation Index). As for multispectral characteristics, waterside plants showed the highest reflectance in Rnir, while floating plants had a higher reflectance in Rre. During the hottest season (on 25 June), the vegetation indices were the highest, and the habitat expanded near the edge of the reservoir. Among the vegetation indices, NDVI was the highest and NGRDI was the lowest. In particular, NGRDI had a higher value on the water surface and was not useful for detecting aquatic plants. NDVI and GNDVI, which showed the clearest difference between aquatic plants and water surface, were determined to be the most effective vegetation indices for detecting aquatic plants. Accordingly, the vegetation indices using multispectral UAV imagery turned out to be effective for detecting aquatic plants. A further study will be accompanied by a field survey in order to acquire and analyze more accurate imagery information.

Inversion of leaf area index of winter wheat based on GF-1/2 image

Leaf area index (LAI) is an important parameter for monitoring crop growth, and an important input parameter for crop yield prediction model, hydrological and climatic models. LAI can be used in field crop growth monitoring and verification of remote sensing products. Therefore, accurate, rapid and large-scale estimation of LAI is not only conducive to better monitoring crops, but also conducive to its application in modeling, crop management and precision agriculture. Remote sensing technique has become a promising method to detect and monitor the crop LAI due to its many advantages. In this paper, Banjiequan Village, Wumachang Township, Qitai County, Xinjiang, China was selected as the study area. In order to rapidly and extensively retrieve LAI of winter wheat using domestic remote sensing images, 17 common vegetation indices were extracted from the GF-1/2 images, which was synchronized with field sampling, and observation at intervals of 20 m in the east-west direction and 30 m in the north-south direction in a 130 m × 420 m block. A total of 78 sampling points were taken from a small area of 4 rows × 50 cm. Sampling width was measured by tape ruler and coordinates of sampling points were given by GPS. Based on the vegetation indices extracted from GF-1/2 image and LAI data measured at erecting stage, jointing stage and flowering stage, we established univariate (linear, exponential, power, quadratic polynomials) and multivariate (partial least squares regression, PLSR) empirical models for inversion of winter wheat LAI, and validated them. The correlation coefficients of LAI with MSR (modified simple ratio), GNDVI (green normalized difference vegetation index), EVI (enhanced vegetation index) extracted from the erecting, jointing and flowering stages of GF-1 were the maximum, which were 0.708, 0.671, and 0.743, respectively, indicating that the correlation between these vegetation indices and LAI of winter wheat was significant. The univariate model R 2 based on MSRGF-1, NDVIGF-2 (normalized difference vegetation index), GNDVIGF-1 at jointing stage and EVIGF-1 at flowering stage were all greater than 0.7. Compared with different image data at the same growth stage, the quadratic polynomial model based on NDVIGF-2 and PLSR model based on NDVIGF-2, MSRGF-2, and SAVIGF-2 (soil-adjusted vegetation index) were more precise than those based on GF-1, with R 2 of 0.768 and 0.809 respectively. Compared with the models with the same data (GF-1) at different growth stages, the quadratic polynomial model based on GNDVIGF-1 at jointing stage and the PLSR model based on EVIGF-1, GSRGF-1 (green simple ratio) and NDVIGF-1 at flowering stage had the maximum value of R 2, which was 0.783. The RMSE of the PLSR model was smaller than that of the quadratic polynomial model, indicating the stability of the multivariate model was better than univariate model. Analyzing the LAI distribution maps inverted from different growth stages, it was found that the LAI inversion values basically coincided with the measured LAI values. The above results show that the domestic high-resolution remote sensing image has certain application value in crop physiological parameter inversion, and provides some references for related researches in the future.

Sensor-based evaluation of maize (Zea mays) and weed response to post-emergence herbicide applications of Isoxaflutole and Cyprosulfamide applied as crop seed treatment or herbicide mixing partner.

Some maize post-emergence herbicides obtain their crop/weed selectivity only through the use of chemical crop safeners. Safeners improve the tolerance of maize to herbicidal active ingredients. In order to investigate the crop response to safener (cyprosulfamide) spray application and seed treatment, greenhouse and field trials were conducted on three maize development stages (2-, 4-, and 6-leaf stage). Visual estimations on crop vitality were compared to ground-based and airborne hyperspectral and multispectral sensors. The reduction of cyprosulfamide by 88% when applied as seed treatment did not significantly reduce maize biomass yields at the field. The crop deterioration in both trials was stronger in the cyprosulfamide seed treatments compared to the spray applications but was found to be transient in the field trial. The hyperspectral sensor and multispectral camera data correlated with R2 = 0.84 (CropSpec Vegetation Index) and R2 = 0.64 (Green Normalized Difference Vegetation Index). The sensor-based collection of crop responses to treatments enables early, quantifiable and auditor-independent assessments. In particular, the airborne multispectral imagery assessment of field experiments provides more detailed and comprehensive information than visually collected data. © 2019 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Effect of water surface area on the remotely sensed water quality parameters of Baysh Dam Lake, Saudi Arabia

Abstract. Water quality parameters help to decide the further use of water based on its quality. Changes in water surface area in the lake shall affect the water quality. Chlorophyll a, Nitrate concentration and water turbidity were extracted from satellite images to record each variation on these parameters caused by the water amount in the lake changes. Each water quality measures have been recorded with its surface area reading to analyses the effects. Water quality parameters were estimated from Sentinel-2 sensor based on the satellite temporal resolution for the years 2017–2018. Data were pre-processed then processed to estimate the Maximum Chlorophyll Index (MCI), Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Turbidity Index (NDTI). The Normalized Difference Water Index (NDWI), was used to calculate and record the changes in the water surface area in Baysh dam lake. Results showed different correlation coefficients between the lake surface area and the water quality parameters estimated Remote Sensing data. The response of the water quality parameters to surface water changes was expressed in four different surface water categories. MCI is more sensitive to surface water changes rather than GNDVI and NDTI. Neural network Analysis showed a resemblance between GNDVI and NDTI expressed in sigmoidal function while MCI showed a different behavior expressed in exponential behavior. Therefore, monitoring of the surface water area of the lack is essential in water quality monitoring.

Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (Triticum aestivum L.) Nitrogen Status Based on Canopy Spectrum

Compared to conventional laboratory testing methods, crop nitrogen estimation methods based on canopy spectral characteristics have advantages in terms of timeliness, cost, and practicality. A variety of rapid and non-destructive estimation methods based on the canopy spectrum have been developed on the scale of space, sky, and ground. In order to understand the differences in estimation accuracy and applicability of these methods, as well as for the convenience of users to select the suitable technology, models for estimation of nitrogen status of winter wheat were developed and compared for three methods: drone equipped with a multispectral camera, soil plant analysis development (SPAD) chlorophyll meter, and smartphone photography. Based on the correlations between observed nitrogen status in winter wheat and related vegetation indices, green normalized difference vegetation index (GNDVI) and visible atmospherically resistant index (VARI) were selected as the sensitive vegetation indices for the drone equipped with a multispectral camera and smartphone photography methods, respectively. The correlation coefficients between GNDVI, SPAD, and VARI were 0.92 ** and 0.89 **, and that between SPAD and VARI was 0.90 **, which indicated that three vegetation indices for these three estimation methods were significantly related to each other. The determination coefficients of the 0–90 cm soil nitrate nitrogen content estimation models for the drone equipped with a multispectral camera, SPAD, and smartphone photography methods were 0.63, 0.54, and 0.81, respectively. In the estimation accuracy evaluation, the method of smartphone photography had the smallest root mean square error (RMSE = 9.80 mg/kg). The accuracy of the smartphone photography method was slightly higher than the other two methods. Due to the limitations of these models, it was found that the crop nitrogen estimation methods based on canopy spectrum were not suitable for the crops under severe phosphate deficiency. In addition, in estimation of soil nitrate nitrogen content, there were saturation responses in the estimation indicators of the three methods. In order to introduce these three methods in the precise management of nitrogen fertilizer, it is necessary to further improve their estimation models.

Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques

Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4–R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4–R6).

Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery

Spatial information on crop nutrient status is central for monitoring vegetation health, plant productivity and managing nutrient optimization programs in agricultural systems. This study maps the spatial variability of leaf chlorophyll content within fields with differing quantities of nitrogen fertilizer application, using multispectral Landsat-8 OLI data (30 m). Leaf chlorophyll content and leaf area index measurements were collected at 15 wheat (Triticum aestivum) sites and 13 corn (Zea mays) sites approximately every 10 days during the growing season between May and September 2013 near Stratford, Ontario. Of the 28 sites, 9 sites were within controlled areas of zero nitrogen fertilizer application. Hyperspectral leaf reflectance measurements were also sampled using an Analytical Spectral Devices FieldSpecPro spectroradiometer (400–2500 nm). A two-step inversion process was developed to estimate leaf chlorophyll content from Landsat-8 satellite data at the sub-field scale, using linked canopy and leaf radiative transfer models. Firstly, at the leaf-level, leaf chlorophyll content was modelled using the PROSPECT model, using both hyperspectral and simulated mulitspectral Landsat-8 bands from the same leaf sample. Hyperspectral and multispectral validation results were both strong (R2 = 0.79, RMSE = 13.62 μg/cm2 and R2 = 0.81, RMSE = 9.45 μg/cm2, respectively). Secondly, leaf chlorophyll content was estimated from Landsat-8 satellite imagery for 7 dates within the growing season, using PROSPECT linked to the 4-Scale canopy model. The Landsat-8 derived estimates of leaf chlorophyll content demonstrated a strong relationship with measured leaf chlorophyll values (R2 = 0.64, RMSE = 16.18 μg/cm2), and compared favourably to correlations between leaf chlorophyll and the best performing tested spectral vegetation index (Green Normalised Difference Vegetation Index, GNDVI; R2 = 0.59). This research provides an operational basis for modelling within-field variations in leaf chlorophyll content as an indicator of plant nitrogen stress, using a physically-based modelling approach, and opens up the possibility of exploiting a wealth of multispectral satellite data and UAV-mounted multispectral imaging systems.

Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery

Periodical outbreaks of Thaumetopoea pityocampa feeding on pine needles may pose a threat to Mediterranean coniferous forests by causing severe tree defoliation, growth reduction, and eventually mortality. To cost–effectively monitor the temporal and spatial damages in pine–oak mixed stands using unmanned aerial systems (UASs) for multispectral imagery, we aimed at developing a simple thresholding classification tool for forest practitioners as an alternative method to complex classifiers such as Random Forest. The UAS flights were performed during winter 2017–2018 over four study areas in Catalonia, northeastern Spain. To detect defoliation and further distinguish pine species, we conducted nested histogram thresholding analyses with four UAS-derived vegetation indices (VIs) and evaluated classification accuracy. The normalized difference vegetation index (NDVI) and NDVI red edge performed the best for detecting defoliation with an overall accuracy of 95% in the total study area. For discriminating pine species, accuracy results of 93–96% were only achievable with green NDVI in the partial study area, where the Random Forest classification combined for defoliation and tree species resulted in 91–93%. Finally, we achieved to estimate the average thresholds of VIs for detecting defoliation over the total area, which may be applicable across similar Mediterranean pine stands for monitoring regional forest health on a large scale.

Leaf Scanner: A portable and low-cost multispectral corn leaf scanning device for precise phenotyping

During recent years, portable plant phenotyping instruments have become increasingly important to monitor conditions of plant health non-destructively in the greenhouse and field environments. These devices, such as the Soil Plant Analysis Development (SPAD) meter, leverage indices that represent leaf chlorophyll content due to its strong correlation with leaf nitrogen (N) content. However, instruments such as SPAD meters are expensive and measure only individual, restricted leaf regions per data capture. In this publication, we developed a Leaf Scanner device to analyze the chlorophyll content distributions of whole leaves with greater efficiency and precision. The validating samples for this device were top-collared corn leaves grown under high and low N treatments in a greenhouse. For each region of a corn leaf, this device rapidly flashed visible and near infrared (NIR) LEDs to obtain the visible and NIR transmittance images of leaves. These images were summarized pixel-wisely into the Green NDVI index. A sufficient high framerate permitted continuous collection of regional index images. Using image registration techniques, these regional images were stitched together into a ‘leaf panorama’. The total pixel amount of each leaf panorama, thus, served as a substitute for leaf area. A Minolta SPAD-502Plus meter collected ground-truth measurements along the length of each leaf sample. The results showed that there was a strong correlation between the average Leaf Scanner’s measurements and averaged SPAD values (R2 = 0.92), and the Leaf Scanner was able to clearly detect differences between high and low fertilized samples in terms of chlorophyll content and leaf area. Furthermore, this Leaf Scanner device enabled us to study the chlorophyll content distributions on the plants under different treatments.

Assessing Multiple Years’ Spatial Variability of Crop Yields Using Satellite Vegetation Indices

Assessing crop yield trends over years is a key step in site specific management, in view of improving the economic and environmental profile of agriculture. This study was conducted in a 11.07 ha area under Mediterranean climate in Northern Italy to evaluate the spatial variability and the relationships between six remotely sensed vegetation indices (VIs) and grain yield (GY) in five consecutive years. A total of 25 satellite (Landsat 5, 7, and 8) images were downloaded during crop growth to obtain the following VIs: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI), Green Chlorophyll Index (GCI), and Simple Ratio (SR). The surveyed crops were durum wheat in 2010, sunflower in 2011, bread wheat in 2012 and 2014, and coriander in 2013. Geo-referenced GY and VI data were used to generate spatial trend maps across the experimental field through geostatistical analysis. Crop stages featuring the best correlations between VIs and GY at the same spatial resolution (30 m) were acknowledged as the best periods for GY prediction. Based on this, 2–4 VIs were selected each year, totalling 15 VIs in the five years with r values with GY between 0.729** and 0.935**. SR and NDVI were most frequently chosen (six and four times, respectively) across stages from mid vegetative to mid reproductive growth. Conversely, SAVI never had correlations high enough to be selected. Correspondence analysis between remote VIs and GY based on quantile ranking in the 126 (30 m size) pixels exhibited a final agreement between 64% and 86%. Therefore, Landsat imagery with its spatial and temporal resolution proved a good potential for estimating final GY over different crops in a rotation, at a relatively small field scale.

Spectral vegetation indices of wetland greenness: Responses to vegetation structure, composition, and spatial distribution

Land conversion and fragmentation threaten the resilience and biodiversity of wetland ecosystems which makes the future of their services to humans uncertain. Remote sensing can provide frequent and consistent data to facilitate wetland monitoring from regional to national scales and support their conservation and adaptive management. However, unique characteristics of wetlands, particularly land cover heterogeneity and background reflectance from soil, water and dead biomass, limit the efficacy of remote-sensing based metrics developed for terrestrial ecosystems. To identify the factors impacting satellite-based measurements of wetland greenness, we tested how six spectral vegetation indices responded to the land surface characteristics and regional climatic and edaphic context of 1,138 wetlands sites surveyed by the U.S EPA's National Wetland Condition Assessment. Spectral vegetation indices (SVIs) were estimated using all cloud-free surface reflectance data captured in 2011 by Landsat 5 TM and 7 ETM+. We tested two annually aggregated metrics —maximum and median greenness— for each SVI to facilitate the analysis of such a large dataset of satellite images. Using univariate and multivariate ordinary least square regression, we assessed how the annual maximum and median of each SVI responded to indicators of vegetation structure and composition, presence of dead biomass, open water, bare soil, and climatic/edaphic variables. Results show that, in the full national-scale dataset, the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI) were most responsive to field-based metrics of vegetation structure and composition. However, the responses of SVIs differed significantly among wetland types, suggesting that their use should be tailored to the specific characteristics of the monitored wetlands. Annually aggregated metrics showed different sensitivity in multivariate models, with median greenness being more sensitive to structure and composition, but also to confounding site variables including litter, open water, and bare soil. This study represents a first-time effort to study relationships between the on-site properties of wetlands and their spectral characteristics at a national scale.

Algorithm for detecting deforestation and forest degradation using vegetation indices

In forestry sector, the remote sensing technology hold a key role on forest inventory and monitoring their changes. This paper describes the algorithm for detecting deforestation and forest degradation using high resolution satellite imageries with knowledge-based approach. The main objective of the study is to develop a practical technique for monitoring deforestation and forest degradation occurred within the mangrove and swamp forest ecosystem. The SPOT 4, 5, and 6 images acquired in 2007, 2012 and 2014 were transformed into three vegetation indices, i.e., Normalized Difference Vegetation Index (NDVI), Green-Normalized Difference Vegetation index (GNDVI) and Normalized Green-Red Vegetation index (NRGI). The study found that deforestation was well detected and identified using the NDVI and GNDVI, however the forest degradation could be well detected using NRGI, better than NDVI and GNDVI. The study concludes that the strategy for monitoring deforestation, biomass-based forest degradation as well as forest growth could be done by combining the use of NDVI, GNDVI and NRGI respectively.

Spectral Reflectance Indices and Physiological Parameters in Quinoa under Contrasting Irrigation Regimes

High temperatures, coupled with arid environments, are a major constraint to quinoa (Chenopodium quinoaWilld.) production. Here, we tested the effectiveness of multiple phenotypic and physiological parameters useful in the selection of heat‐tolerant quinoa genotypes. Initially, we screened 112 quinoa genotypes for leaf greenness index under high‐temperature greenhouse conditions. Of these, eight genotypes were selected for field evaluations under irrigated and rainfed treatments in two locations over two growing seasons (2016–2017) in Washington State. Of these eight, four genotypes were selected as potentially heat tolerant (QQ74, Baer, BGQ 352, and Pison), and four were selected as heat‐susceptible (17 GR, La Molina, 3 UISE, and Japanese Strain). Spectral reflectance indices including normalized difference vegetation index (NDVI) and green NDVI, as well as multiple physiological parameters, such as stomatal conductance, leaf temperature, and area under soil plant analysis development (SPAD) value decline curve (AUSDC), were evaluated. Results show variability in stomatal conductance among genotypes with 17 GR and BGQ 352 having the highest stomatal conductance and QQ74 the lowest. Baer, Pison, and QQ74 had the highest NDVI value across environments, and Japanese Strain had the lowest. Best linear unbiased prediction values in AUSDC showed that QQ74 had the highest leaf greenness and Japanese Strain had the lowest; these were classified as heat tolerant and heat susceptible, respectively. The NDVI is a potentially useful measure to predict grain yield in quinoa.

Effect of fusing Sentinel-2 and WorldView-4 imagery on the various vegetation indices

Satellite image fusion techniques have been used for more than two decades. Development of satellite sensors for very high-resolution satellite imagery monitoring contributed to the development of new image fusion techniques. We examine the quality of Ehlers, Brovey transform, modified intensity–hue–saturation (M-IHS), and high-filter resolution merge fusion methods on vegetation indices values combining high-resolution WorldView-4 and low-resolution Sentinel-2 imagery. For image fusion, four bands—blue, green, red, and near-infrared—were used. The effect of fusing Sentinel-2 and WorldView-4 imagery is tested on the various vegetation indices [normalized difference vegetation index (NDVI), blue normalized difference vegetation index (BNDVI), and green normalized difference vegetation index (GNDVI)]. M-IHS fusion showed the best result, with the least difference between indices calculated based on the original Sentinel-2 and fused bands. Difference between vegetation indices calculated using original Sentinel-2 and M-IHS fused bands for 30.08.2015, 30.09.2016, and 30.09.2017 is between −0.041 to 0.037. The second part of this research is an evaluation of three different vegetation classes (grassland, mixed vegetation, and forest) on three different years and three different months within a year. Difference between NDVI, GNDVI, and BNDVI calculated based on the original Sentinel-2, and M-IHS fused bands are ranging from −0.043 to 0.103, −0.041 to 0.050, and −0.052 to 0.030 for grassland, mixed vegetation, and forest, respectively. On the other hand, the difference between vegetation indices for grassland, mixed vegetation, and forest are from −0.110 to 0.050, −0.041 to 0.038, and −0.052 to 0.038, when observing August, September, and October of 2017, respectively.

High-throughput field phenotyping of Ascochyta blight disease severity in chickpea

Pulse crops, such as chickpea (Cicer arietinum L.), serve as excellent food sources that provide proteins and minerals to humans and livestock. Additionally, pulses are important sources of soil nitrogen in rotational cropping systems. However, pulse production is limited by several biotic and abiotic stress factors. One such disease is Ascochyta blight (Ascochyta rabiei) in chickpea. To minimize the impact of Ascochyta blight, timely information on disease outbreak and epidemics is essential for implementing disease control methods. Thus, in this study, the feasibility of monitoring Ascochyta blight disease severity in chickpea using remote sensing techniques was evaluated. Disease severity was monitored using an unmanned aircraft system integrated with different types of sensors (3-band multispectral, 5-band multispectral, and thermal cameras). Results indicated that different flight altitudes (60 m and 90 m above ground level) that lead to different image resolutions did not influence disease detection efficiency, especially with the 3-band camera. Selected image features, including canopy area, percentage of canopy area, and vegetation indices (e.g., green normalized difference vegetation index) from multispectral cameras, and mean canopy temperature from the thermal camera, were significantly correlated with yield and visual ratings of disease severity. Moreover, hyperspectral sensing was found to be useful in predicting disease severity. In summary, this study demonstrated that disease severity of Ascochyta blight in chickpea can be monitored using remote sensing methods under active field conditions. With timely and accurate disease severity information from high-throughput phenotyping technologies, the effects of Ascochyta blight on chickpea yield and quality can be minimized with timely application of proper management techniques.

Field phenotyping using multispectral imaging in pea (Pisum sativum L) and chickpea (Cicer arietinum L)

Pea (Pisum sativum L) and chickpea (Cicer arietinum L) are important grain legumes grown in the Palouse region of the Pacific Northwest United States. The USDA-ARS grain legume breeding program in this region focuses on developing pea and chickpea varieties with high yield potential, resistance to biotic and abiotic stresses, and superior agronomic characteristics. In this study, aerial high resolution multispectral imaging was evaluated to phenotype yield potential differences among genotypes in green pea, yellow pea and chickpea. Five experiments (three field pea and two chickpea) with 10–25 varieties grown at two locations (Pullman, Washington; Genesee, Idaho) were assessed. Images were acquired approximately 60, 70 and 90 days after planting (DAP) at 110 m above ground level. Normalized difference vegetation index (NDVI), green normalized difference vegetation index, soil adjusted vegetation index (SAVI) and simple ratio (SR) image based features (SUM, MIN, MAX, MEAN) were extracted. In most cases, the MEAN NDVI data was found to be consistently correlated with dry seed yield (p < 0.05), with green pea genotypes showing strongest relationship (r = 0.64–0.93 at about 70 DAP, both during “plot-by-plot” and “by genotype” comparisons). The MEAN SAVI and SR values were also strongly correlated with yield at 61–72 DAP in most of the pea experiments. The data collected during flowering and early pod development phenological growth stages was found to be useful in yield estimation. The developed methods can be used for early generation evaluation in breeding programs, where yield cannot be estimated due to limited seed availability.