Use Of Normalized Difference Vegetation Index Research Articles

The estimation of above-ground oil palm carbon stocks of different ages through vegetation index

Above-ground carbon stocks play an important role to support the sustainability of oil palm plantation, and vary with soil characteristics, climate conditions, and agronomic practices. The common direct approach to assess aboveground carbon stocks may be time-consuming, and laborious, so may not be suitable especially for large scale observation. Therefore, the use of remote sensing may overcome some limitations coming up for conventional methods, yet requires a considerable data validation to obtain a reliable model. In this study, the normalized difference vegetation index (NDVI) based on Sentinel-2 satellite imagery, acquired in September 2023, was used in combination with field measurements, i.e. biomass estimation, investigated in three separated of oil palm plantations differing in ages, i.e. young (< 10 years), middle (10 – 20 years) and old (> 20 years), subjected with similar soil characteristics, and climate condition, in Bojong Datar, Cibungur, and Cikasungka, West Java. Results show that above-ground carbon stocks increase with oil palm ages, i.e. from young to middle ages, with value ranging from 3.40 – 65.70 ton/ha, yet decreases down to 17 ton/ha with ages > 20 years. Similarly, it also occurred for NDVI index, i.e. increase from 0.50 – 0.59 for young to middle of oil palms, and down to 0.51 for oil palm > 20 years old. This study suggests a strong correlation between carbon stocks and NDVI values (R2 = 0.878), pointing out the potential use of vegetation index to estimate above-ground oil palm carbon stocks. In the future, with further validation, the use of NDVI to estimate plant biomass may provide a potential substitute for the field measurement methods with a considerable higher efficiency and accuracy.

Estimation of a suitable NDVI oriented for ecological water savings and phytoremediation in Baiyangdian Lake, North China

To maintain the ecosystem services of wetlands, this study developed an estimation method for suitable normalized difference vegetation index (NDVI) values by theintegration of ecological water savings and phytoremediation requirements based on experiments and literature surveys in Baiyangdian Lake, North China. In terms of ecological water savings (namely, reducing wetland water consumption by reducing vegetation evapotranspiration), the upper limit of the reed community area was determined by the water replenishment plan of the lake to achieve water balance. In terms of phytoremediation, Grade III of the Chinese water quality grade scale, the target water quality of the lake, was considered to calculate the lower limit of the reed community area for the purpose of good water quality. Considering the two aspects of the requirements of ecological water savings and phytoremediation comprehensively, the suitable reed planning area and NDVI threshold were determined. The results indicated that the suitable reed planting area was 138.05 ∼ 146.43 km2, and the NDVI threshold was 0.155 ∼ 0.316 in the high-water-lever situation (6.8 ∼ 7.0 m). Concurrently, the appropriate reed planting area was 122.39 ∼ 128.24 km2, and the NDVI threshold was 0.137 ∼ 0.277 in the low-water-lever situation (6.5 ∼ 6.8 m). These findings expanded the use of NDVI in the relationship research between wetland vegetation patterns and water resource utilisations, providing support for making vegetation pattern adjustment decisions for wetland protection and restoration in water-scarce regions.

Normalized Difference Vegetation Index (NDVI) for soybean biomass and nutrient uptake estimation in response to production systems and fertilization strategies

The system fertilization approach emerged to improve nutrient use efficiency in croplands. This new fertilization concept aims at taking advantage of nutrient cycling within an agroecosystem to obtain maximum production from each nutrient unit. To monitor this effect, methodologies such as the Normalized Difference Vegetation Index (NDVI) are promising to evaluate plant biomass and nutrient content. We evaluated the use of NDVI as a predictor of shoot biomass, P and K uptake, and yield in soybean. Treatments consisted of two production systems [integrated crop-livestock system (ICLS) and cropping system (CS)] and two periods of phosphorus (P) and potassium (K) fertilization (crop fertilization—P and K applied at soybean sowing—and system fertilization—P and K applied in the pasture establishment). NDVI was evaluated weekly from the growth stage V2 up to growth stage R8, using the Greenseeker® canopy sensor. At the growth stages V4, V6, R2, and R4, plants were sampled after NDVI evaluation for chemical analysis. Soybean yield and K uptake were similar between production systems and fertilization strategies (P &amp;gt; 0.05). Soybean shoot biomass and P uptake were, respectively, 25.3% and 29.7% higher in ICLS compared to CS (P &amp;lt; 0.05). For NDVI, an interaction between the production system and days after sowing (P &amp;lt; 0.05) was observed. NDVI increased to 0.95 at 96 days after sowing in CS and to 0.92 at 92 days after sowing in ICLS. A significant relationship between NDVI and shoot biomass, and P and K uptake was observed (P &amp;lt; 0.05). Our results show that the vegetation index NDVI can be used for estimating shoot biomass and P and K uptake in the early growth stages of soybean crops, providing farmers with a new tool for evaluating the spatial variability of soybean growth and nutrition.

USING RVI AND NDVI TIME SERIES FOR CROPLAND MAPPING WITH TIME-WEIGHTED DYNAMIC TIME WARPING

Abstract. Monitoring and management of agricultural lands are essential due to reasons affecting agriculture, such as increasing population and global climate. With the increase in the temporal resolution of satellite systems, time-series classifications have become popular in cropland mapping. Because annual plants can give similar spectral reflectance values on the same date. In this context, agricultural land (∼500 km2) was selected in the south of South Dakota in the United States. The area includes alfalfa, corn, soybeans, winter wheat plants, developed, grassland/pasture, herbaceous wetlands, and open water areas. The study aims to map croplands with vegetation indices produced by annual Sentinel-1 and Sentinel-2 satellites. In this context, Radar Vegetation Index (RVI) produced from 25 Sentinel-1, and the Normalized Difference Vegetation Index (NDVI) produced from 26 Sentinel-2 satellites were used for 2020. We used the Time-Weighted Dynamic Time Warping (TWDTW) algorithm, which separates and classifies the similarities between two time series with variable speeds with time constraints. For mapping, the indices were classified both individually and combined. The highest overall accuracy (77.2%) was obtained with the combined use of NDVI and RVI. Among the plant classes, the lowest accuracy (83.71%) was found, and it was determined that the plant classes did not mix much. Sentinel-2 satellite is not available before April due to weather conditions in the region. For this reason, since the Sentinel-1 satellite is not affected by weather conditions, it is thought that the use of two satellites together will be beneficial in time series analysis.

Correlation between Ground Measurements and UAV Sensed Vegetation Indices for Yield Prediction of Common Bean Grown under Different Irrigation Treatments and Sowing Periods

The objective of this study is to assess the possibility of using unmanned aerial vehicle (UAV) multispectral imagery for rapid monitoring, water stress detection and yield prediction under different sowing periods and irrigation treatments of common bean (Phaseolus vulgaris, L). The study used a two-factorial split-plot design, divided into subplots. There were three sowing periods (plots; I—mid April, II—end of May/beginning of June, III—third decade of June/beginning of July) and three levels of irrigation (subplots; full irrigation (F)—providing 100% of crop evapotranspiration (ETc), deficit irrigation (R)—providing 80% of ETc, and deficit irrigation (S) providing—60% of ETc). Canopy cover (CC), leaf area index (LAI), transpiration (T) and soil moisture (Sm) were monitored in all treatments during the growth period. A multispectral camera was mounted on a drone on seven occasions during two years of research which provided raw multispectral images. The NDVI (Normalized Difference Vegetation Index), MCARI1 (Modified Chlorophyll Absorption in Reflectance Index), NDRE (Normalized Difference Red Edge), GNDVI (Green Normalized Difference Vegetation Index) and Optimized Soil Adjusted Vegetation Index (OSAVI) were computed from the images. The results indicated that NDVI, MCARI1 and GNDVI derived from the UAV are sensitive to water stress in S treatments, while mild water stress among the R treatments could not be detected. The NDVI and MCARI1 of the II-S treatment predicted yields better (r2 = 0.65, y = 4.01 tha−1; r2 = 0.70, y = 4.28 tha−1) than of III-S (r2 = 0.012, y = 3.54 tha−1; r2 = 0.020, y = 3.7 tha−1). The use of NDVI and MCARI will be able to predict common bean yields under deficit irrigation conditions. However, remote sensing methods did not reveal pest invasion, so good yield predictions require observations in the field. Generally, a low-flying UAV proved to be useful for monitoring crop status and predicting yield and water stress in different irrigation regimes and sowing period.

The influence of oil exploitation on the degradation of vegetation: A case study in the Yellow River Delta Nature Reserve, China

Crude oil is harmful to plants when it is leaked into the soil. The influence of oil exploitation on the degradation of vegetation is still unclear. Oil wells in the Yellow River Delta Natural Reserve (YRDNR) is an ideal region for the research of oil exploitation on vegetation given that this region is less influenced by other human activities. Field investigation and high-resolution remote sensing images were employed to explore the effect of oil exploitation on vegetation. Eighteen sample quadrats and three vegetation sampling strips were investigated near the oil wells. The total petroleum hydrocarbon (TPH) concentrations of the soil affected soil water content negatively. The water content of the sampled soil exhibited a significant positive effect on the species number, Shannon–Wiener index, and aboveground biomass (AGB) of the vegetation. Furthermore, crude oil contamination lowered plant height and quantity, and plant tolerance to crude oil pollutants differed across various species. Oil production has an impact range of 40 m on the surrounding vegetation. Two indices (Normalized Difference Vegetation Index (NDVI) and New Normalized Difference Water Indices 1 (NNDWI1) were employed in a high-resolution remote sensing image interpretation experiment to identify ground objects, the overall accuracy improved 11% compared with the NDVI utilization approach. Moreover, there existed an excellent linear regression relationship between NDVI and field investigated AGB (R 2 = 0 . 813 ). The degradation rate of AGB caused by oil exploitation, directly and indirectly, was about 16.49% in the YRDNR. • TPH indirectly affect species diversity and biomass of vegetation through soil water content. • The combined use of NDVI and NNDWI1 improved 11% of the image interpretation accuracy. • There existed an excellent linear regression relationship between NDVI and AGB. • The impact range of oil production on the surrounding vegetation is about 40 m. • The degradation rate of AGB caused by oil exploitation is about 16.49% in the YRDNR.

On the Use of NDVI to Estimate LAI in Field Crops: Implementing a Conversion Equation Library

The leaf area index (LAI) is a direct indicator of vegetation activity, and its relationship with the normalized difference vegetation index (NDVI) has been investigated in many research studies. Remote sensing makes available NDVI data over large areas, and researchers developed specific equations to derive the LAI from the NDVI, using empirical relationships grounded in field data collection. We conducted a literature search using “NDVI” AND “LAI” AND “crop” as the search string, focusing on the period 2017–2021. We reviewed the available equations to convert the NDVI into the LAI, aiming at (i) exploring the fields of application of an NDVI-based LAI, (ii) characterizing the mathematical relationships between the NDVI and LAI in the available equations, (iii) creating a software library with the retrieved methods, and (iv) releasing a publicly available software as a service, implementing these equations to foster their reuse by third parties. The literature search yielded 92 articles since 2017, where 139 equations were proposed. We analyzed the mathematical form of both the single equations and ensembles of the NDVI to LAI conversion methods, specific for crop, sensor, and biome. The characterization of the functions highlighted two main constraints when developing an NDVI-LAI conversion function: environmental conditions (i.e., water and light resource, land cover, and climate) and the availability of recurring data during the growing season. We found that the trend of an NDVI-LAI function is usually driven by the ecosystem water availability for the crop rather than by the crop type itself, as well as by the data availability; the data should be adequate in terms of the sample size and temporal resolution for reliably representing the phenomenon under investigation. Our study demonstrated that the choice of the NDVI-LAI equation (or ensemble of equations) should be driven by the trade-off between the scale of the investigation and data availability. The implementation of an extensible and reusable software library publicly queryable via API represents a valid mean to assist researchers in choosing the most suitable equations to perform an NDVI-LAI conversion.

English

Normalized difference vegetation index (NDVI) is considered as a potential screening tool detecting stay green traits for drought tolerance. The present study aimed to evaluate durum wheat genotypes using NDVI under drought condition and investigate its association to grain yield. NDVI scores taken at different growth stages for 64 durum wheat genotypes were replicated twice in both sandy clay and clay textured soils situated at the same geographic location of Debre-Zeit Experimental Station, Ethiopia during 2016 rainy season; also, Green Seeker (Hand held) was used to measure the quantity of photosynthetically active pigments in leaves. Measurements of NDVI were made four to five times on sunny days between booting to physiological maturity. Analysis of variance showed significant variation among genotypes at 0.01% on NDVI values across all growth stages except at physiological maturity in both sandy clay and clay soils. The NDVI scores were highly significantly associated with yield at grain filling and anthesis stages in both soil environments. Overall, it is possible to suggest that use of NDVI would help complement identification of drought tolerant genotypes on durum wheat. &nbsp; Key words: Grain yield, Normalized difference vegetation index (NDVI), Triticum durum.

Prediction of Crop Yield for New Mexico Based on Climate and Remote Sensing Data for the 1920–2019 Period

Agricultural production systems in New Mexico (NM) are under increased pressure due to climate change, drought, increased temperature, and variable precipitation, which can affect crop yields, feeds, and livestock grazing. Developing more sustainable production systems requires long-term measurements and assessment of climate change impacts on yields, especially over such a vulnerable region. Providing accurate yield predictions plays a key role in addressing a critical sustainability gap. The goal of this study is the development of effective crop yield predictions to allow for a better-informed cropland management and future production potential, and to develop climate-smart adaptation strategies for increased food security. The objectives were to (1) identify the most important climate variables that significantly influence and can be used to effectively predict yield, (2) evaluate the advantage of using remotely sensed data alone and in combination with climate variables for yield prediction, and (3) determine the significance of using short compared to long historical data records for yield prediction. This study focused on yield prediction for corn, sorghum, alfalfa, and wheat using climate and remotely sensed data for the 1920–2019 period. The results indicated that the use of normalized difference vegetation index (NDVI) alone is less accurate in predicting crop yields. The combination of climate and NDVI variables provided better predictions compared to the use of NDVI only to predict wheat, sorghum, and corn yields. However, the use of a climate only model performed better in predicting alfalfa yield. Yield predictions can be more accurate with the use of shorter data periods that are based on region-specific trends. The identification of the most important climate variables and accurate yield prediction pertaining to New Mexico’s agricultural systems can aid the state in developing climate change mitigation and adaptation strategies to enhance the sustainability of these systems.

Evaluation of geomorphometric characteristics and soil properties after a wildfire using Sentinel-2 MSI imagery for future fire-safe forest

Understanding spatiotemporal geomorphological and pedological changes as a consequence of wildfires can allow stakeholders, land planners, and policymakers to design efficient fire safety-based afforestation and restoration programs of forest lands. The use of remote sensing techniques is a key tool to achieve this goal. The suitable combination of Sentinel-2 MSI data for mapping of different spectral indices related to burn severity and their relationship with other morphometric and soil properties can contribute to a better understanding of the impact of fire, and this is relevant in regions where is still scarce fire-related research such as Turkey. In this investigation, the use of NDVI (Normalized Difference Vegetation Index), dNDVI (Difference Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NBR (Normalized Burn Ratio), dNBR (Difference Normalized Burn Ratio), RBR (Relativized Burn Ratio), SBI (Soil Bare Index), As (Upslope area), CTI (Compound Topographic Index), TCI (Terrain Characterization Index), SPI (Stream Power Index) and Curvature (Standard Curvature) were combined. As a study case, 47.43 ha in a burned area of Çınarpınar forest unit, Andırın, Kahramanmaraş in Turkey was selected. The results showed that dNDVI, dNBR, RBR, SBI contribute to relevant information about the effect of the wildfire. According to the dNBR fire severity classification, 75% of the total area has been exposed to high-severity fire. The relationship of Sentinel MSI satellite images with some soil and morphometric features have been found meaningful to understand the impact of forest fire in Mediterranean ecosystems. The information collected in the Turkish forest areas affected by wildfires should be relevant for planning and represent a key contribution to the selection of restoration programs and afforestation techniques for a future fire-safe forest.

Does Leaf Waxiness Confound the Use of NDVI in the Assessment of Chlorophyll When Evaluating Genetic Diversity Panels of Wheat?

Ground and aerial-based high throughput phenotyping platforms (HTPPs) to evaluate chlorophyll-related traits have been utilized to predict grain yield in crops including wheat (Triticum aestivum L.). This study evaluated chlorophyll-related and other physiological and yield traits in a panel of 318 Nepali spring wheat genotypes, termed the Nepali Wheat Diversity Panel (NWDP). Field experiments were conducted using an alpha-lattice design in Nepal and Canada. Chlorophyll-related traits were evaluated with a Soil Plant Analysis Development (SPAD) meter and the normalized difference vegetation index (NDVI) using a handheld GreenSeeker and an Unmanned Aerial Vehicle (UAV). Relative leaf epicuticular waxiness was recorded using visual assessments. There was a significant positive association (p &lt; 0.001) between waxiness and SPAD-based chlorophyll estimates, and both of these traits displayed a significant positive relationship with grain yield. However, unexpectedly, NDVI derived from both GreenSeeker and UAV was negatively associated with waxiness and grain yield. The results obtained after segregating the trait means into groups based on waxiness scores and breeding history of genotypes indicated that waxiness along with precipitation could be affecting the multispectral reflectance. These results suggest that caution should be taken when evaluating a large and diverse wheat population for leaf chlorophyll using high-throughput NDVI methods.

Geospatial Approach for the Analysis of Forest Cover Change Detection using Machine Learning

Spatial data classification is famous over recent years in order to extract knowledge and insights into the data. It occurs because vast experimentation was used with various classifiers, and significant improvement was examined in accuracy and performance. This study aimed to analyze forest cover change detection using machine learning. Supervised and unsupervised learning methods were used to analyze spatial data. A Vector machine was used to support the supervised learning, and a neural network method was used to support unsupervised learning. The Normalized Difference Vegetation Index (NDVI) was used to identify the bands and extract pixel information relevant to the vegetation. The supervised method shows better results because of its robust performance and better analysis of spatial data classification using vegetation index. The proposed system experimentation was implemented by analyzing the results obtained from Support Vector Machine (SVM) and NN (Neural Network) methods. It is demonstrated in the results that the use of NDVI mainly enhances the performance and increases the classifier's accuracy to a greater extent.&#x0D; Keywords: Spatial data; Normalized Difference Vegetation Index; NDVI;Vegetation index, Support Vector Machine; Neural Network; Forest Cover Change&#x0D; Copyright (c) 2020 Geosfera Indonesia Journal and Department of Geography Education, University of Jember&#x0D; This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Pemetaan Lahan Sub-Optimal Berbasis Nilai NDVI Sentinel 2a: Studi Pendahuluan

Normalised Difference Vegetation Index (NDVI) is one of the vegetation indices used to analyse vegetation density. This study presents the potential use of NDVI to map dry-marginal-agricultural land (Dry-MAL). The study conducted in the eastern part of Situbondo, which includes three districts, namely, Arjasa, Asembagus and Jangkar. Sentinel-2A (recorded in 2018) and 450 Control points (GCPs) are used as the primary input. The region is an area with distinctive climate characteristics, where the dry season is longer than the rainy season. Analysis using "SNAP plug-ins" and "QGIS". Research procedures include (1) data inventory, (2) data pre-processing, (3) data processing and (4) accuracy testing. The NDVI classification can distinguish six (6) classes of land-use, i.e., water bodies, residential areas, dry MAL, non-irrigated rural area, irrigated paddy fields, forest-plantations. The NDVI classification produces Overall and Kappa accuracy values = 66,9% and 61,6%. Although the overall and kappa accuracy is below the standard, however, the result will benefit for further research of index vegetation or soil more applied for the identification of Dry-MAL

The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L)

This study was conducted to evaluate the feasibility of a mobile phone-based thermal and UAV-based multispectral imaging to assess the irrigation performance of African eggplant. The study used a randomized block design (RBD) with sub-plots being irrigated at 100% (I100), 80% (I80) and 60% (I60) of the calculated crop water requirements using drip. The leaf moisture content was monitored at different soil moisture conditions at early, vegetative and full vegetative stages. The results showed that, the crop water stress index (CWSI) derived from the mobile phone-based thermal images is sensitive to leaf moisture content (LMC) in I80 and I60 at all vegetative stages. The UAV-derived Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI) correlated with LMC at the vegetative and full vegetative stages for all three irrigation treatments. In cases where eggplant is irrigated under normal conditions, the use of NDVI or OSAVI at full vegetative stages will be able to predict eggplant yields. In cases where, eggplant is grown under deficit irrigation, CWSI can be used at vegetative or full vegetative stages next to NDVI or OSAVI depending on available resources.

Landslide Mapping Using Multiscale LiDAR Digital Elevation Models

ABSTRACT This study presents a new methodology to identify landslide and landslide-susceptible locations in Interior Alaska using only geomorphic properties from light detection and ranging (LiDAR) derivatives (i.e., slope, profile curvature, and roughness) and the normalized difference vegetation index (NDVI), focusing on the effect of different resolutions of LiDAR images. We developed a semi-automated object-oriented image classification approach in ArcGIS 10.5 and prepared a landslide inventory from visual observation of hillshade images. The multistage work flow included combining derivatives from 1-, 2.5-, and 5-m-resolution LiDAR, image segmentation, image classification using a support vector machine classifier, and image generalization to clean false positives. We assessed classification accuracy by generating confusion matrix tables. Analysis of the results indicated that LiDAR image scale played an important role in the classification, and the use of NDVI generated better results. Overall, the LiDAR 5-m-resolution image with NDVI generated the best results with a kappa value of 0.55 and an overall accuracy of 83 percent. The LiDAR 1-m-resolution image with NDVI generated the highest producer accuracy of 73 percent in identifying landslide locations. We produced a combined overlay map by summing the individual classified maps that was able to delineate landslide objects better than the individual maps. The combined classified map from 1-, 2.5-, and 5-m-resolution LiDAR with NDVI generated producer accuracies of 60, 80, and 86 percent and user accuracies of 39, 51, and 98 percent for landslide, landslide-susceptible, and stable locations, respectively, with an overall accuracy of 84 percent and a kappa value of 0.58. This semi-automated object-oriented image classification approach demonstrated potential as a viable tool with further refinement and/or in combination with additional data sources.

Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices

Accurate and up-to-date spatial agricultural information is essential for applications including agro-environmental assessment, crop management, and appropriate targeting of agricultural technologies. There is growing research interest in spatial analysis of agricultural ecosystems applying satellite remote sensing technologies. However, usability of information generated from many of remotely sensed data is often constrained by accuracy problems. This is of particular concern in mapping complex agro-ecosystems in countries where small farm holdings are dominated by diverse crop types. This study is a contribution to the ongoing efforts towards overcoming accuracy challenges faced in remote sensing of agricultural ecosystems. We applied time-series analysis of vegetation indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) derived from the Moderate Resolution Imaging Spectrometer (MODIS) sensor to detect seasonal patterns of irrigated and rainfed cropping patterns in five townships in the Central Dry Zone of Myanmar, which is an important agricultural region of the country has been poorly mapped with respect to cropping practices. To improve mapping accuracy and map legend completeness, we implemented a combination of (i) an iterative participatory approach to field data collection and classification, (ii) the identification of appropriate size and types of predictor variables (VIs), and (iii) evaluation of the suitability of three Machine Learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and C5.0 algorithms under varying training sample sizes. Through these procedures, we were able to progressively improve accuracy and achieve maximum overall accuracy of 95% When a small sized training dataset was used, accuracy achieved by RF was significantly higher compared to SVM and C5.0 (P < 0.01), but as sample size increased, accuracy differences among the three machine learning algorithms diminished. Accuracy achieved by use of NDVI was consistently better than that of EVI (P < 0.01). The maximum overall accuracy was achieved using RF and 8-days NDVI composites for three years of remote sensing data. In conclusion, our findings highlight the important role of participatory classification, especially in areas where cropping systems are highly diverse and differ over space and time. We also show that the choice of classifiers and size of predictor variables are essential and complementary to the participatory mapping approach in achieving desired accuracy of cropping pattern mapping in areas where other sources of spatial information are scarce.

Use of NDVI for indirect selection of high yield in tropical rice breeding

Plant breeders evaluate large numbers of breeding lines/accessions and measure various traits in the field in order to identify and select elite genotypes with high yield. Therefore, information on highly correlated yield-related traits and simple high-throughput methods to predict grain yield would be useful for rice breeders. The objective of this study was to examine the usefulness of various traits as indirect selection criteria for high yield in tropical rice breeding, with an emphasis on rapid and non-destructive measurements. We evaluated a wide range of germplasm, including popular varieties, elite genotypes, and donor parents, in the irrigated rice breeding program at the International Rice Research Institute, in the Philippines, during the 2015 wet season and 2016 dry season. We measured the normalized-difference vegetation index (NDVI), leaf chlorophyll content (SPAD value), agronomic traits, and grain yield. Harvest index, NDVI at the heading and late grain-filling stages, and SPAD value at the late grain-filling stage were significantly correlated with yield during the wet season, whereas plant height, panicle length, and NDVI from panicle initiation to the booting stage during the dry season. Our results indicate that pre-harvest analysis of primary and secondary traits such as NDVI has potential for selecting high-yielding accessions in tropical rice breeding programs. By using low-cost portable equipment, NDVI measurements could be incorporated into breeding programs. Trait associations with yield differed between the dry and wet seasons, which has important implications for breeding new varieties and germplasm evaluation in different seasons.

Динамика роста и оценка состояния культур сосны обыкновенной на вейниково-луговиковых вырубках методами дистанционного зондирования

Well-timed and high-quality artificial reforestation is one of the main factors of sustainable forest management. Efficient forest stand formation is influenced by the right choice of the reforestation method and efficient planting material species. Longstanding research of pine crops’ growth in the taiga zone of the Republic of Karelia was held according to the conventional methods. In order to confirm the objectivity of the obtained research results multitime materials of remote sensing taken from the satellites of Landsat and Sentinel series were used for the first time. The presence of red and near infrared channels in the received data allows to calculate NDVI (Normalized Difference Vegetation Index), which gives the opportunity to estimate the photosynthetic active biomass. The index is the difference between the reflected intensities in infrared and red channels divided by the sum of its intensities. The use of NDVI for the study of reforestation dynamics is of reserach interest. Forest live cover analysis has shown that the quota of cereals and fireweed has essentially decreased, while the quota of forest species has increased. It was found that the coniferous stand of the I and Ia quality classes with the stock not fewer than 29.2 m3/ha has developed after Scots pine crops’ formation on the cultivated land of reedgrass meadow cuts by 2019. For citation: Gavrilova O.I., Morozova I.V., Olkhin Yu.V., Yureva A.L., Ioffe A.O. Growth Growth Dynamics and Status Assessment of Scots Pine Crops on Reedgrass Meadow Cutsby Remote Sensing Techniques. Lesnoy Zhurnal [Russian Forestry Journal], 2020, no. 1, pp. 63–74. DOI: 10.37482/0536-1036-2020-1-63-74

Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece

The main objective of this study was to explore the impact of various spectral indices on the performance of change vector analysis (CVA) for detecting the land cover changes on the island of Crete, Greece, between the last two decades (1999–2009 and 2009–2019). A set of such indices, namely, normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), albedo, bare soil index (BSI), tasseled cap greenness (TCG), and tasseled cap brightness (TCB), representing both the vegetation and soil conditions of the study area, were estimated on Landsat satellite images captured in 1999, 2009, and 2019. Change vector analysis was then applied for five different index combinations resulting to the relative change outputs. The evaluation of these outputs was performed towards detailed land cover maps produced by supervised classification of the aforementioned images. The results from the two examined periods revealed that the five index combinations provided promising performance results in terms of kappa index (with a range of 0.60–0.69) and overall accuracy (with a range of 0.86–0.96). Moreover, among the different combinations, the use of NDVI and albedo were found to provide superior results against the other combinations.

Integrating Standard Precipitation Index and Normalised Difference Vegetation Index for near-real-time drought monitoring in Eswatini.

Eswatini, as the rest of southern Africa, is being frequented by drought over the last decade, and modelling experts are predicting that drought years will become more and severe. The expected increase in extreme climatic events makes the use of drought indices essential for drought monitoring and early warning. To enable Eswatini to better prepare, analyse and respond to drought, this study analysed the use of Normalised Difference Vegetation Index (NDVI) and Standard Precipitation Index (SPI) for near-real-time drought monitoring through the development of a model for drought severity. Meteorological stations across all agro-ecological zones with data for the period 1986–2017 were selected for analysis. The SPI computation was achieved through DrinC software. Primary NDVI data sources were CHIRPS gridded rainfall dataset and the MODIS NDVI CMG data. Results of the 3-month SPI indicated that moderate droughts were experienced in 1990/1991, 2005/2006, 2011/2012, 2012/2013 and 2015/2016. The Highveld and Middleveld had the lowest drought occurrence percentage of 3.3%, whereas the likelihood of having a moderate, severe and extreme drought was higher in the Lowveld. The study determined a positive correlation between the SPI and the NDVI at 3-month time scale, and a value of Y (drought severity) greater than 0.54 indicated a significant dry spell and could be used as a drought trigger threshold for early warning. The combined use of NDVI and SPI was deemed capable of providing a near-real-time indicator for drought conditions allowing planners to provide timely information for drought preparedness, mitigation and response planning, thereby helping to lower the eventual drought relief costs, protect food security and reduce the humanitarian impact on the population.