Transformed Vegetation Index Research Articles

Mapping the Dynamics of Mangrove Density in Alas Purwo National Park using the ARVI Index

This study aims to compare the sensor/Top-of-Atmospheric (TOA) reflectance correction method and surface reflectance correction in the ARVI (Atmospherically Resistant Vegetation Index) vegetation index transformation application. The resulting comparison of the two has a correlation that is not much different, sensor reflectance correction (R = 0.9723) and surface reflectance correction (R = 0.9781) are both able to represent mangrove forest canopy density well. The model accuracy resulting from the surface reflectance correction RMSE 1.408 and MAPE 0.48%, is higher than that of the sensor correction (TOA) which has an RMSE value of 2.184 and MAPE 1.34%. For advanced remote sensing analysis, it is better to perform radiometric correction of surface reflectance because the reflectance value used for analysis is the true reflectance value. The condition of mangroves in Grajagan Bay in Alas Purwo National Park is very well preserved, this is shown by the mangrove canopy density >50% increased by 49.11 Ha in a period of 7 years from 2016 to 2023.

Monitoring Urban Green Space Using Remote Sensing Derived-vegetation Indices in Colombo District, Sri Lanka

This study investigated the use of Remote Sensing (RS)-derived vegetation indices for monitoring urban green spaces (UGSs) from Sentinel-2 RS multispectral imagery with a spatial resolution of up to 10 meters. Six vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Green Ratio (GR), and Transformed Vegetation Index (TVI) by using Google Earth Engine (GEE) platform. Derived the original and enhanced images were used to compare the aforementioned vegetation indices in order to assess four image quality parameters: Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), Standard Deviation (SD), and Correlation Coefficient (CC). The findings demonstrated the range of values for each vegetation index: NDVI (-0.398163 to 0.888742), EVI (-0.287905 to 0.615649), SAVI (-0.597015 to 1.00000), MSAVI (-0.495483 to 0.795898), GR (-0.199505 to 0.444334), and TVI (0.058906 to 2.32316). Among the indices, MSAVI exhibited the best fit with image quality parameters of PSNR, RMSE, SD, and CC measuring at 45.31, 0.0197, 0.06, and 0.9641, respectively. This outcome suggests MSAVI's better performance in estimating green vegetation areas compared to other indices in the study area. The study also discussed the limitations of using vegetation indexes to monitor UGS, including the influence of atmospheric conditions, sensor calibration, and data preprocessing techniques. Overall, this study is insightful which is valuable in terms of the effectiveness of different vegetation indices for UGS. The findings of this study can be used to inform future research on UGS monitoring and management, and to identify appropriate vegetation indices for RS applications in other areas.

Pendugaan Cadangan Karbon Dengan Citra Sentinel-2B dan Terrestrial Laser Scanner Di Kawasan Hutan Dengan Tujuan Khusus (KHDTK) Pendidikan dan Pelatihan Kehutanan Fakultas Kehutanan Universitas Mulawarman

Measurement of forest carbon on the ground in a large scale is time-consuming and expensive. This research utilized remote sensing with Sentinel-2B image through its Vegetation Index and related those values with those obtained from field measurements and Terrestrial Laser Scanner (TLS). This study specifically compares the forest stand carbon value using TLS and field measurements and link it to the vegetation index. This study transforms the vegetation indices, i.e.: Transformed Vegetation Index (TVI), Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) from Sentinel-2B imagery and relates them with field measurements and TLS. The calculation of carbon in forest stands using TLS revealed an average carbon of 151.35 ton/Ha. In contrast, in the field measurements, carbon was measured at 149.81 ton/Ha, and statistically, there was no difference between these two measurements. The relationship between the vegetation index and field measurements showed the best coefficient of correlation of TVI with (r) = 0.784 and (R2) = 0.524. The relationship between the vegetation index and TLS measurements showed the best coefficient of correlation of TVI with (r) = 0.759 and the coefficient of determination (R2) = 0.577.

Optimized Deep Learning Model for Flood Detection Using Satellite Images

The increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection models, deep learning techniques are extensively used in flood control. Therefore, a novel deep hybrid model for flood prediction (DHMFP) with a combined Harris hawks shuffled shepherd optimization (CHHSSO)-based training algorithm is introduced for flood prediction. Initially, the input satellite image is preprocessed by the median filtering method. Then the preprocessed image is segmented using the cubic chaotic map weighted based k-means clustering algorithm. After that, based on the segmented image, features like difference vegetation index (DVI), normalized difference vegetation index (NDVI), modified transformed vegetation index (MTVI), green vegetation index (GVI), and soil adjusted vegetation index (SAVI) are extracted. The features are subjected to a hybrid model for predicting floods based on the extracted feature set. The hybrid model includes models like CNN (convolutional neural network) and deep ResNet classifiers. Also, to enhance the prediction performance, the CNN and deep ResNet models are fine-tuned by selecting the optimal weights by the combined Harris hawks shuffled shepherd optimization (CHHSSO) algorithm during the training process. This hybrid approach decreases the number of errors while improving the efficacy of deep neural networks with additional neural layers. From the result study, it clearly shows that the proposed work has obtained sensitivity (93.48%), specificity (98.29%), accuracy (94.98%), false negative rate (0.02%), and false positive rate (0.02%) on analysis. Furthermore, the proposed DHMFP–CHHSSO displays better performances in terms of sensitivity (0.932), specificity (0.977), accuracy (0.952), false negative rate (0.0858), and false positive rate (0.036), respectively.

Aboveground biomass of forest and oil palm land cover mapping using Sentinel 2A imagery, Asahan-Indonesia

Forests are the largest carbon sink, play an important role in the global carbon cycle, and can store at least ten times more carbon than other vegetation types. The study aims to model the aboveground biomass of forest and palm oil land cover, to map the amount of aboveground biomass in the Asahan district. Tree biomass is measured non-destructive or without cutting a tree sample. The independent variables from the biomass estimation model are the normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and transformed vegetation index (TVI) from Sentinel 2A images. The coefficient of determination selects the best model from the equation. The selected estimating model of forest coverage biomass is y = 3830,8ln(x) + 2356,6 with a predicted variable of the NDVI by a coefficient of determination of 79 %; palm oil coverage biomass is y = 0,0018x6,8778 with TVI variable by a coefficient of determination of 21%. The total aboveground biomass for forest coverage is 34,57 tons/ha, and oil palm coverage is 0,07 tons/ha

Analysis of the level of Vegetation Density in the City of Ambon Based on NDVI Using Landsat 8 OLI Imagery

Vegetation can be interpreted as a combination of several plants with different types living together in a place that forms a unit that interacts with each other, both among individuals from the plants themselves and the interaction of environmental factors. Vegetation density is the percentage of a group of plants or vegetation that live in an area. Vegetation index transformation (NDVI) is one of the data processing techniques to determine vegetation density. This study aims to determine the level of vegetation density in Ambon City using the NDVI Landsat 8 OLI imagery transformation. The method used in this research was the Normalized Difference Vegetation Index (NDVI) transformation of Landsat 8 OLI imagery. Based on the results of the study, there were five levels of vegetation density, namely: non-vegetation with a value of -0.6320-0.3660, a very low-density level with a value of 0.3661-0.5562, a low-density level with a value of 0.5563-0.7464, medium density level with value of 0.7465-0.8732 and a high-density level with value of 0.8733-1.00000. The land covers found were built-up areas, shrubs, mixed plantations, mixed crops, and secondary dryland forests. The non-vegetation class has an area of 761.95 ha (2.37%), the very low-density level has an area of 1234 ha (3.83%), the low-density level has an area of 2333.87 ha (7.25%), the medium density level has an area 3689.10 (11.45%), and the high-density level has an area 24194.29 ha (75.10%). The accuracy of the analysis of the research was a very good category because the Kappa value was 91% and overall accuracy was 93%.

Monitoring and Integrating the Changes in Vegetated Areas with the Rate of Groundwater Use in Arid Regions

Frequent water table measurements are crucial for sustainable groundwater management in arid regions. Such monitoring is more important in areas that are already facing an acute problem with excessive groundwater withdrawal. In the majority of these locations, continuous readings of groundwater levels are lacking. Therefore, an approximate estimate of the rate of increase or decrease in water consumption over time may serve as a proxy for the missing data. This could be achieved by tracking the changes in vegetated areas that generally correlate with changes in the rate of water use. The technique proposed in this paper is based on two remote sensing datasets: Landsat 7 and 8 from 2001 to 2021, and Sentinel 2A from 2015 to 2021, as well as five vegetation indices: Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and Transformed Vegetation Index (TVI). The findings have shown that the datasets chosen performed best for small-scale land farms at the research location, which was chosen to be the El-Qaa plain, in the southwestern corner of the Sinai Peninsula in Egypt. Landsat 7 data with a resolution of 30 m revealed a substantial increase in land farms from 2.9 km2 in 2001 to 23.3 km2 in 2021. By using the five indices based on Sentinel 2A data, vegetated areas were categorized as heavy, moderate, or light. In addition, the expansion of each class area from 2015 to 2021 was tracked. Additionally, the NDVI index was modified to better reflect the arid environment (subsequently naming this new index as the Arid Vegetation Index: AVI). Rough scenarios of the increase in water consumption rate at the research site were generated by observing the increase in vegetated areas and collecting rough information from the farmers regarding the crop types.

A Phenology Analysis of Palm Oil (Elaeis Guineensis Jacq) Growth Using Satellite Imagery Approach (A Case Study in Darul Makmur Sub-District, Nagan Raya Regency, Aceh)

Abstract. Vegetation index values derived from the Landsat-8 OLI images have been used widely to assess plant growth, especially in oil palm plantations. This study intends to estimate growth rates and establish the phenology of oil palm growth based on spectral reflection patterns. This study employed multitemporal Landsat-8 OLI images from 2013, 2014, 2017, 2020, and 2021 to visually interpret satellite imagery based on spatial and digital detection of object attributes. The phenology of oil palm growth is grouped into the Seed-0 Phase (0-1 Years), Seed Phase (1-3 Years), Young Phase (4-8 years), Teenage Phase (9-14 years), Mature Phase (15-20 years) and Old Phase ( 20 years). The results of the digital analysis of Landsat 8 OLI show that the spectral reflection pattern has almost the same shape at the same wavelength and varies in each phase of oil palm growth. The average NDVI vegetation index value shows a green peak in the teenage phase (0.908). The results of a simple linear regression averaging the NDVI vegetation index value with the average age data of each oil palm growth phase from the TBM-0 phase (0-1 year) to the old phase ( 20 years) yields a moderate relationship for the transformation of the vegetation index NDVI. The transformation of the NDVI vegetation index produces an R² with a value of 0.483 that the regression results formed above the adolescent phase (9-13 years) do not form a pattern

Evaluation of the Influence of Field Conditions on Aerial Multispectral Images and Vegetation Indices

Remote sensing is a method used for monitoring and measuring agricultural crop fields. Unmanned aerial vehicles (UAV) are used to effectively monitor crops via different camera technologies. Even though aerial imaging can be considered a rather straightforward process, more focus should be given to data quality and processing. This research focuses on evaluating the influences of field conditions on raw data quality and commonly used vegetation indices. The aerial images were taken with a custom-built UAV by using a multispectral camera at four different times of the day and during multiple times of the season. Measurements were carried out in the summer seasons of 2019 and 2020. The imaging data were processed with different software to calculate vegetation indices for 10 reference areas inside the fields. The results clearly show that NDVI (normalized difference vegetation index) was the least affected vegetation index by the field conditions. The coefficient of variation (CV) was determined to evaluate the variations in vegetation index values within a day. Vegetation index TVI (transformed vegetation index) and NDVI had coefficient of variation values under 5%, whereas with GNDVI (green normalized difference vegetation index), the value was under 10%. Overall, the vegetation indices that include near-infrared (NIR) bands are less affected by field condition changes.

Sago Forest Density Analysis Using Vegetation Index Transformation (Case Study in Tawiri Village, Teluk Ambon District, Ambon City)

Ecologically, sago palms can grow, develop and produce on marginal land where other plants are difficult to grow. The research was conducted in a sago forest in Tawiri Village, Teluk Ambon District, Ambon City, Maluku Province. The purpose of the study was to map the density level of sago forest in Tawiri Village at 2016 based on the transformation of the vegetation index on the Landsat imagery. The method used in the research was Normalized Difference Vegetation Index (NDVI) transformation Landsat-7 ETM+. The results showed that there were three levels of sago forest density: a medium density level of 13.3%, a low density of 3.4%, and a high density of 1.6% of the research area. In the study area, non-sago area covered 81% of the research area. The accuracy of the sago forest density analysis at 2016 was 92%, which means there was a similarity between the image classification and the actual situation in the field.

Tourism Infrastructure Development and Transformation of Vegetation Index in Dodola Island of Morotai Island Regency

The development of tourism infrastructure without control can cause changes in the environmental vegetation index and harm a tourism destination. This study aims to analyze changes in the vegetation index on Dodola Island from 2013-2021 to develop tourism infrastructure. The approach used is remote sensing, namely the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) using Landsat 8 Operational Land Imager (OLI). NDVI is an index of the greenery or photosynthetic activity of vegetation. Vegetation actively carrying out photosynthesis will absorb most of the red waves of sunlight and reflect higher near-infrared waves. In addition, SAVI is a soil vegetation index adjusted for soil pixels' effect using a vegetation density function. The results of this study indicate a perfect correlation between the NDVI average value and the SAVI average value of 0.996 so that it can be used as data to analyze changes in the vegetation index value on Dodola Island from 2013-2021. The average NDVI value of Dodola Island in 2017 showed a significant decrease to 0.16 from 0.35 in 2016. Likewise, the average SAVI value of Dodola Island in 2017 showed a reduction of 0.12 from 0 .27 in 2016. Although in 2021, the average NDVI value will increase to 0.32 and the average SAVI value to 0.27, stakeholders need to form institutions and establish policies as a preventive measure for the decline in the vegetation index worse than in 2017. Based on the Decree of the State Minister of the Environment No. 201 of 2004 concerning the characteristics of the damage to mangrove forests, the condition of the mangrove area of ​​Dodola Island in 2017 is categorized as damaged and sparse, so that mangrove rehabilitation is necessary. Based on the results of this study, a community-based mangrove ecotourism approach is recommended as an alternative method of sustainable tourism development on Dodola Island.

Mangrove Health Analysis Using Sentinel-2A Image with NDVI Classification Method

<p><em>This study aims to determine 1) the mangrove vegetation density index, 2) the health of mangrove plants in Sungai Batang Village to Kuala Secapah. The data used in this study is the image of Sentinel-2A, dated June 8, 2020. The data taken are vegetation density (NDVI) and mangrove health. The method in this study uses the vegetation index transformation (NDVI). Data analysis used the supervised classification method and the vegetation density index (NDVI). The results showed that the NDVI value of -1 – 0.32 indicates a sparse vegetation density, a value of 0.33 – 0.42 indicates a medium density and 0.43 – 1 indicates a dense density. From this NDVI index value, it can be used as a basis for classifying the health of mangrove vegetation. The health of mangrove vegetation based on the vegetation index value of 0.43 – 1 (meet) indicates that the health of the mangrove vegetation is very good. Vegetation value 0.33 – 0.42 (moderate) indicates good health of mangrove vegetation and vegetation index value -1 – 0.32 (rare) indicates poor vegetation health. Mangrove health level is very good with an area of 3.0314 km<sup>2</sup>, healthy has an area of 0.204806 km<sup>2</sup> and poor health has an area of 0.625875 km<sup>2</sup>.</em></p>

Hyperspectral image-based vegetation index (HSVI): A new vegetation index for urban ecological research

Accurately monitoring the quantity and quality of urban vegetation contributes to regional greening efforts and improves the understanding of vegetation's impact on the environment. However, factors such as building shadows and synthetic materials can greatly obstruct vegetation estimates. Additionally, vegetation indices (VIs) saturate quickly in high biomass conditions, complicating vegetation quality assessments. To address these issues, we propose a new vegetation index, namely the hyperspectral image-based vegetation index (HSVI). HSVI is built in three steps: band selection, saturable band reconstruction, and index structure redefinition. Firstly, we select four representative bands and construct an enhanced vegetation index (EVI) to eliminate the interference of complex urban surface factors. Secondly, we reconstruct the easily saturable band (760 nm) through an exponential function to form an optimized enhanced vegetation index (OEVI). Finally, the index structure is redefined by adding the denominator of the sum of the red edge (689 nm) and green (520 nm) bands to OEVI to further enhance the spectral information of vegetation and eliminate the saturation problem typical of VIs. Three datasets with different geomorphological features (Shanghai Theatre Academy, Dazhu Mountain, and the University of Houston) are used to compare the performance of HSVI with six VIs widely adopted in urban ecological research, i.e., the difference vegetation index (DVI), the normalized difference vegetation index (NDVI), the simple ratio (SR), the optimized soil-adjusted vegetation index (OSVAI), the modified transformed vegetation index (MTVI2) and the wide-dynamic-range vegetation index (WDRVI). The results show that the vegetation extraction accuracy of HSVI is more than 90%, which is significantly higher than those of the other VIs. Additionally, HSVI also solves the VIs saturation issue. Therefore, HSVI shows high potential usefulness for urban ecological research applications.

Mangrove Health Analysis Using Sentinel-2A Image with NDVI Classification Method

This study aims to determine 1) the mangrove vegetation density index, 2) the health of mangrove plants in Sungai Batang Village to Kuala Secapah. The data used in this study is the image of Sentinel-2A, dated June 8, 2020. The data taken are vegetation density (NDVI) and mangrove health. The method in this study uses the vegetation index transformation (NDVI). Data analysis used the supervised classification method and the vegetation density index (NDVI). The results showed that the NDVI value of -1 – 0.32 indicates a sparse vegetation density, a value of 0.33 – 0.42 indicates a medium density and 0.43 – 1 indicates a dense density. From this NDVI index value, it can be used as a basis for classifying the health of mangrove vegetation. The health of mangrove vegetation based on the vegetation index value of 0.43 – 1 (meet) indicates that the health of the mangrove vegetation is very good. Vegetation value 0.33 – 0.42 (moderate) indicates good health of mangrove vegetation and vegetation index value -1 – 0.32 (rare) indicates poor vegetation health. Mangrove health level is very good with an area of 3.0314 km 2 , healthy has an area of 0.204806 km 2 and poor health has an area of 0.625875 km 2 .

Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions

Meeting food demand for the growing population will require an increase to crop production despite climate changes and, more particularly, severe drought episodes. Sorghum is one of the cereals most adapted to drought that feed millions of people around the world. Valorizing its genetic diversity for crop improvement can benefit from extensive phenotyping. The current methods to evaluate plant biomass, leaves area and plants height involve destructive sampling and are not practical in breeding. Phenotyping relying on drone based imagery is a powerful approach in this context. The objective of this study was to develop and validate a high throughput field phenotyping method of sorghum growth traits under contrasted water conditions relying on drone based imagery. Experiments were conducted in Bambey (Senegal) in 2018 and 2019, to test the ability of multi-spectral sensing technologies on-board a UAV platform to calculate various vegetation indices to estimate plants characteristics. In total, ten (10) contrasted varieties of West African sorghum collection were selected and arranged in a randomized complete block design with three (3) replicates and two (2) water treatments (well-watered and drought stress). This study focused on plant biomass, leaf area index (LAI) and the plant height that were measured weekly from emergence to maturity. Drone flights were performed just before each destructive sampling and images were taken by multi-spectral and visible cameras. UAV-derived vegetation indices exhibited their capacity of estimating LAI and biomass in the 2018 calibration data set, in particular: normalized difference vegetative index (NDVI), corrected transformed vegetation index (CTVI), seconded modified soil-adjusted vegetation index (MSAVI2), green normalize difference vegetation index (GNDVI), and simple ratio (SR) (r2 of 0.8 and 0.6 for LAI and biomass, respectively). Developed models were validated with 2019 data, showing a good performance (r2 of 0.92 and 0.91 for LAI and biomass accordingly). Results were also promising regarding plant height estimation (RMSE = 9.88 cm). Regression plots between the image-based estimation and the measured plant height showed a r2 of 0.83. The validation results were similar between water treatments. This study is the first successful application of drone based imagery for phenotyping sorghum growth and development in a West African context characterized by severe drought occurrence. The developed approach could be used as a decision support tool for breeding programs and as a tool to increase the throughput of sorghum genetic diversity characterization for adaptive traits.

Crop growth inhibited by over-liming in tea plantations

Liming is a common strategy applied to attain optimal pH for tea growth in severely acidic soils. Tea however is a calciphobous plant, and the effects of liming on its growth and nutrient uptake remain poorly understand. Therefore, it is necessary to better understand the effects of liming on soil chemical properties and tea nutrient content. In this study, a tea plantation that had exhibited large variation in growth after liming was selected as a study site. We categorized plots into two growth condition groups: Plot 1 (poor growth) and Plot 2 (excellent growth). Tea nutrient levels, and soil chemical properties were then compared between the two groups. Normalized difference vegetation index (NDVI) and transformed vegetation index (TVI) values were significantly higher and lower, respectively, in Plot 2 than in Plot 1. Yield, number of buds per m², and 100-bud weight were significantly higher in Plot 2. These results were attributed to higher N, K, and Al concentrations and lower Ca concentrations in leaves, and lower pH and available Ca and higher available Al in soil. Leaf concentrations of K and Al were significantly negatively correlated with leaf concentrations of Ca and soil pH. A positive relationship was observed between leaf concentrations of K and Al, indicating inhibited K and Al uptake due to over-liming, restricting tea growth. In conclusion, our results show that tea growth will be restricted by over-liming, as a result of the high soil pH and Ca concentration inhibiting the K and Al uptake.

Remote Sensing Analysis of Agricultural Drone

Farmers have more requirements for the completion of cultivations. Remote sensing is a big technology for reducing this requirement. Now, we need an organic spraying system at a low cost. We have two methods, first one neural network algorithm of quantum geographic information system (QGIS) and another one global positioning system (GPS) with drone. This paper describes the analysis of drone remote sensing using the normalized difference vegetation index (NDVI)/Near-infrared band (NIR) sensor in a multispectral view of agricultural land. NIR and NDVI images had water content values and precision values which is mixed in managing water resources. NDVI sensors are loaded to produce high-density images. Real-time monitoring coupled in NIR imaging geometrically and radiometrically adjusted to measure temperature. Multispectral and hyperspectral views had used for analyzing the tested data. Standard irrigation level is 60% to produce the plant growing. Irrigation techniques followed the treatment of the plant within continuous data per second. The implemented view focused only on growth controlling of plant in-depth irrigation between 30 and 90 cm in 60% deviation. NDVI, green normalized difference vegetation index (GNDVI), soil brightness index (SBI), green vegetation index (GVI), degree of yellow vegetation index (YVI), nitrogen sufficiency index (NSI), perpendicular vegetation index (PVI), transformed vegetation index (TVI), soil adjusted vegetation index (SAVI) and vegetation condition index (VCI) vegetation indices are used to the correlation of plant growth control with managing leaf strength and import python packages display the Vegetation various Real-time value in QGIS. Correlation of plant growth p ≤0.01, r = 0.77 and − 0.77 with conductance. It measured degree and demonstrated GPS view using irrigation techniques to control water stress. It had used to estimate the leaf conductance rate with the variation of atmospherically changing. It can calculate real-time leaf stress analysis. This report provided a drone survey analysis of compost percentage and vegetation indices of agricultural land.

Assessment of pine aboveground biomass within Northern Steppe of Ukraine using Sentinel-2 data

The present study offers the results of the spectral characteristics, calculated vegetative indices and biophysical parameters of pine stands of the Northern Steppe of Ukraine region obtained using Sentinel-2 data. For the development of regression models with the prediction of the biomass of pine forests using the obtained spectral characteristics, we used the results of the assessment of the aboveground biomass by the method of field surveys. The results revealed the highest correlation relations between the parameters of the general and trunk biomass with the normalised difference vegetation index (NDVI) and transformed vegetation index (TVI) vegetative indices and the fraction of absorbed photosynthetic active radiation (FARAP) and fraction of vegetation cover (FCOVER) biophysical parameters. To generate the models of determining the forest aboveground biomass (AGB), we used both the single- and two-factor models, the most optimum of which were those containing the NDVI predictor separately and in combination with the FCOVER predictor. The predicted values of the total AGB for the mentioned models equalled 32.5 to 236.3 and 39.9 to 253.4 t·ha<sup>–1</sup>. We performed mapping of the AGB of pine stands of the Northern Steppe using multi-spectral Sentinel-2 images, particularly the spectral characteristics of their derivatives (vegetative indices, biophysical parameters). This study demonstrated promising results for conducting an AGB-mapping of pine woods in the studied region using free-access resources.

Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas

Agricultural land abandonment is an important environmental issue in Europe. The proper management of agricultural areas has important implications for ecosystem services (food production, biodiversity, climate regulation and the landscape). In the coming years, an increase of abandoned areas is expected due to socio-economic changes. The identification and quantification of abandoned agricultural plots is key for monitoring this process and for applying management measures. The Valencian Region (Spain) is an important fruit and vegetable producing area in Europe, and it has the most important citrus industry. However, this agricultural sector is highly threatened by diverse factors, which have accelerated land abandonment. Landsat and MODIS satellite images have been used to map land abandonment. However, these images do not give good results in areas with high spatial fragmentation and small-sized agricultural plots. Sentinel-2 and airborne imagery shows unexplored potential to overcome this thanks to higher spatial resolutions. In this work, three models were compared for mapping abandoned plots using Sentinel-2 with 10 m bands, Sentinel-2 with 10 m and 20 m bands, and airborne imagery with 1 m visible and near-infrared bands. A pixel-based classification approach was used, applying the Random Forests algorithm. The algorithm was trained with 144 plots and 100 decision trees. The results were validated using the hold-out method with 96 independent plots. The most accurate map was obtained using airborne images, the Enhanced Vegetation Index (EVI) and Thiam’s Transformed Vegetation Index (TTVI), with an overall accuracy of 88.5%. The map generated from Sentinel-2 images (10 m bands and the EVI and TTVI spectral indices) had an overall accuracy of 77.1%. Adding 20 m Sentinel-2 bands and the Normalized Difference Moisture Index (NDMI) did not improve the classification accuracy. According to the most accurate map, 4310 abandoned plots were detected in our study area, representing 32.5% of its agricultural surface. The proposed methodology proved to be useful for mapping citrus in highly fragmented areas, and it can be adapted to other crops.

Evaluation of the saturation property of vegetation indices derived from sentinel-2 in mixed crop-forest ecosystem

The saturation property of vegetation indices posed a known limitation and this study was motivated to understand the saturation property of three widely used vegetation indices in mixed crop-forest ecosystem where limited knowledge existed. Normalized Difference Vegetation Index (NDVI), Simple Ratio Index (SRI) and Transformed Vegetation Index (TVI) were computed from sentinel-2 bands and; variations among bands and among vegetation indices were evaluated. The study employed green Leaf Area Index (gLAI) Version 1 product, derived from PROBA-V daily data for discriminating the saturation property of the indices. Although the study applied various methods of image preprocessing and processing, best curve fitting and correlation analysis were the key ones. The three vegetation indices: NDVI, SRI, and TVI computed from sentinel-2 bands: four (red) and five (red edge) coupled with bands 8 and 8a showed some levels of saturation. Nonetheless, TVI computed from bands 8a and 4 is the best outperforming combination, i.e., the least saturated one and it is an interesting output in a sense that a single index with significantly lower values of noise equivalent green Leaf Area Index as well as having strong association with gLAI is obtained that could be very useful for quantification of gLAI in similar ecosystems. For the rest of the bands and vegetation indices combination of the indices via setting thresholds could be one possible solution.