Normalized Difference Vegetation Index Approach Research Articles

Enhancing land cover and carbon storage through rehabilitation of degraded lands into Wono: a case study of a small-scale private forest in Dengok Village, Gunungkidul, Yogyakarta, Indonesia (1972-2021)

The implementation of degraded land rehabilitation programs and community-based forest management certification is widely recognised to enhance the status of wono land cover and aid in climate change mitigation. The expansion of the restoration efforts can be monitored using satellite images to track the progress of operations by observing changes in land cover area. Community forest certification also can enhance the success of land restoration endeavours. Therefore, this study aimed to show the change in forest land cover and the amount of carbon stored from 1972 to 2021. Landsat imagery was used to depict land cover conditions in 1972, 1982, 1993, 2000, 2008, 2014, and 2021, before and after the implementation of restoration and community forest certification programs. The Normalized Difference Vegetation Index approach was employed for satellite images captured in 1972 and 1982, while the Forest Canopy Density method was employed for images in 1993, 2000, 2008, 2014, and 2021, to illustrate the progressive increase in wono land cover. Meanwhile, carbon storage was analysed from 1972 to 2021 using national carbon storage conversion. The results indicated that restoration activity and the establishment of community forest certification increased wono area and carbon storage, which totalled 55.90 ha and 5,499.04 t C between 1972 and 2021. Augmenting carbon storage within wono land cover constituted an effective strategy for mitigating climate change.

Utilizing Electromagnetic Radiation in Remote Sensing for Vegetation Health Analysis Using NDVI Approach with Sentinel-2 Imagery

The process of utilizing electromagnetic radiation from the radiometric values (Near-Infrared/NIR) and RED in the Normalized Difference Vegetation Index (NDVI) on Sentinel-2 imagery involves measuring light reflection at specific wavelengths reflected by vegetation. NDVI is computed by taking the difference between NIR and RED reflectance, then dividing it by their sum. In-depth analysis of electromagnetic wave interaction and plant spectral characteristics, such as NIR light reflection in healthy leaves, enables mapping vegetation health levels. The research aims to process image data and create a model visualizing vegetation health in the Sentinel-2 research area. Across the total area of 601,971,281.26 m2, 30.37% is healthy vegetation, 12.52% is moderate, and 53.33% is low or unhealthy. The least healthy area has an NDVI of -0.2006, while the healthiest area has a value of 0.6043.

Spatiotemporal variability in the C-factor: An analysis using high resolution satellite imagery

Estimating the cover and management factor (C-factor) for Universal Soil Loss Equation (USLE) that varies spatially and temporally within a watershed is time-consuming and resource-intensive. The Normalized Difference Vegetation Index (NDVI) approach can offer a potential alternative for this process. The current study examines nine NDVI models to compare and evaluate their performance in estimating the C-factor values for an agricultural watershed in southwestern Ontario, Canada. Satellite imagery from 2013 to 2020 was used to analyze the models’ similarities and differences on a detailed spatial and temporal scale. The results showed different C-factor values for each model, reflecting that they were developed for different geographical areas and purposes. While the Karaburun model differed from all other models on an annual basis, a detailed combined analysis of different spatial and temporal scales revealed that it was similar to other models. Seasonal analysis was found to be adequate for the current study, as it reduced the resources required and provided an overall view of the vegetation situation. However, a detailed monthly analysis may be necessary when investigating a specific season. The current analysis found that the summer months of June, July, and August have similar trends when comparing different models for different land uses and individual months, which aligns with the seasonal analysis. In conclusion, the current study highlights the importance of incorporating spatial and temporal scales in hydrological modeling and provides valuable insight into the applicability of different NDVI models for estimating the C-factor for southwestern Ontario watersheds. These findings can help inform future research and aid in developing accurate models for estimating soil erosion in this region. The results also emphasize that the NDVI approach has the potential for estimating the USLE C-factor and improving the estimation of soil erosion from agricultural watersheds by incorporating a variable C-factor over time and space. However, further research is needed to validate each model and determine which model best suits the study area.

Geo-environmental monitoring and assessment of land degradation and desertification in the semi-arid regions using Landsat 8 OLI / TIRS, LST, and NDVI approach

This research aims to find a relation between measured Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) for assessing land degradation and desertification in semi-arid regions using LANDSAT 8. In the twenty-first century, soil loss has been a serious environmental problem due to anthropogenic pressure and climate change. Monitoring and assessing land degradation vulnerability is becoming increasingly important for sustainable land resource management, especially in semi-arid ecosystems. The purpose of this research is to retrieve the LST and examine the relationship between LST and NDVI, in addition to LST and Landsat 8 bands. The association between LST and Landsat 8 bands is found to be positive, as well as the regression coefficient from NDVI to LST is also found to be strong Positive. The resultant of the NDVI is further classified into five categories based on the pixel values, and they are reclassified into five categories for land cover assessment, they are namely waterbodies, degraded lands, desertified lands, vegetation, and built-up lands. The results reveal that the desertified lands are occupying an area of 13.62%, degraded lands occupy 48.91%, built-up lands occupy 8.39%, vegetation lands occupy 27.34%, and water bodies occupy 1.71% in the study region. These research findings can be used to optimize the technique for future research in the field of degradation assessment, thermal comfort zone assessment, and sustainable agricultural growth.

Semiautomated Detection and Mapping of Vegetation Distribution in the Antarctic Environment Using Spatial-Spectral Characteristics of WorldView-2 Imagery

Effective monitoring of changes in the geographic distribution of cryospheric vegetation requires high-resolution and accurate baseline maps. The rationale of the present study is to compare multiple feature extraction approaches to remotely mapping vegetation in Antarctica, assessing which give the greatest accuracy and reproducibility relative to those currently available. This study provides precise, high-resolution, and refined baseline information on vegetation distribution as is required to enable future spatiotemporal change analyses of the vegetation in Antarctica. We designed and implemented a semiautomated customized normalized difference vegetation index (NDVI) approach for extracting cryospheric vegetation by incorporating very high resolution (VHR) 8-band WorldView-2 (WV-2) satellite data. The viability of state-of-the-art target detection, spectral processing/matching, and pixel-wise supervised classification feature extraction techniques are compared with the customized NDVI approach devised in this study. An extensive quantitative and comparative assessment was made by evaluating four semiautomatic feature extraction approaches consisting of 16 feature extraction standalone methods (four customized NDVI plus 12 existing methods) for mapping vegetation on Fisher Island and Stornes Peninsula in the Larsemann Hills, situated on continental east Antarctica. The results indicated that the customized NDVI approach achieved superior performance (average bias error ranged from ~6.44 ± 1.34% to ~11.55 ± 1.34%) and highest statistical stability in terms of performance when compared with existing feature extraction approaches. Overall, the accuracy analysis of the vegetation mapping relative to manually digitized reference data (supplemented by validation with ground truthing) indicated that the 16 semi-automatic mapping methods representing four general feature extraction approaches extracted vegetated area from Fisher Island and Stornes Peninsula totalling between 2.38 and 3.72 km2 (2.85 ± 0.10 km2 on average) with bias values ranging from 3.49 to 31.39% (average 12.81 ± 1.88%) and average root mean square error (RMSE) of 0.41 km2 (14.73 ± 1.88%). Further, the robustness of the analyses and results were endorsed by a cross-validation experiment conducted to map vegetation from the Schirmacher Oasis, East Antarctica. Based on the robust comparative analysis of these 16 methods, vegetation maps of the Larsemann Hills and Schirmacher Oasis were derived by ensemble merging of the five top-performing methods (Mixture Tuned Matched Filtering, Matched Filtering, Matched Filtering/Spectral Angle Mapper Ratio, NDVI-2, and NDVI-4). This study is the first of its kind to detect and map sparse and isolated vegetated patches (with smallest area of 0.25 m2) in East Antarctica using VHR data and to use ensemble merging of feature extraction methods, and provides access to an important indicator for environmental change.

Influence of vegetation cover on the assessment of erosion and erosive potential in the Isser marly watershed in northwestern Algeria—comparative study of RUSLE and PAP/RAC methods

The present article aims to evaluate soil losses due to water erosion and to contribute to the knowledge about the impact of vegetation cover on the Isser watershed that is located in northwestern Algeria. For this purpose, two predictive models were used, namely the Priority Actions Program by the Regional Activity Center (PAP/RAC) model and the revised universal soil loss equation (RUSLE) model, using the geographic information system (GIS). To illustrate the level of protection in that watershed, it was decided to use two approaches for the estimation of factor C; the first one is the classical method and the second consists of calculating the normalized difference vegetation index (NDVI) during two periods (dry and wet). The map of erosive states, which was obtained from using by the PAP/RAC model, shows the areas that are potentially most vulnerable to erosion and which cover more than 60% of the total area of the basin. The results for vegetation cover (factor C) by the conventional method during the dry period present an average land loss equal to 14 (t ha−1 year−1), whereas this rate decreases to 7.9 (t ha−1 year−1) during the wet period. According to the NDVI approach, the factor C results for the dry season are 25.53 (t ha−1 year−1) with an annual land loss of up to 3 million tons. This study provided the qualitative and quantitative estimates needed for the development of the environmental management; it also helps to control the different impacts on that watershed.

A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems

Vegetative spring green-up date (GUD), an indicator of plants' sensitivity to climate change, exerts an important influence on biogeochemical cycles. Conventionally, large-scale monitoring of spring phenology is primarily detected by satellite-based vegetation indices (VIs), e.g. the Normalized Difference Vegetation Index (NDVI). However, these indices have long been criticized, as the derived GUD can be biased by snowmelt. To minimize the snowmelt effect in monitoring spring phenology, we developed a new index, Normalized Difference Phenology Index (NDPI), which is a 3-band VI, designed to best contrast vegetation from the background (i.e. soil and snow in this study) as well as to minimize the difference among the backgrounds. We examined the rigorousness of NDPI in three ways. First, we conducted mathematical simulations to show that NDPI is mathematically robust and performs superior to NDVI for differentiating vegetation from the background, theoretically justifying NDPI for spring phenology monitoring. Second, we applied NDPI using MODIS land surface reflectance products to real vegetative ecosystems of three in-situ PhenoCam sites. Our results show that, despite large snow cover in the winter and snowmelt process in the spring, the temporal trajectories of NDPI closely track the vegetation green-up events. Finally, we applied NDPI to 11 eddy-covariance tower sites, spanning large gradients in latitude and vegetation types in deciduous ecosystems, using the same MODIS products. Our results suggest that the GUD derived by using NDPI is consistent with daily gross primary production (GPP) derived GUD, with R (Spearman's correlation)=0.93, Bias=2.90days, and RMSE (the root mean square error)=7.75days, which outcompetes the snow removed NDVI approach, with R=0.90, Bias=7.34days, and RMSE=10.91days. We concluded that our newly-developed NDPI is robust to snowmelt effect and is a reliable approach for monitoring spring green-up in deciduous ecosystems.

Optical properties of developing pip and stone fruit reveal underlying structural changes.

Analyzing the optical properties of fruits represents a powerful approach for non-destructive observations of fruit development. With classical spectroscopy in the visible and near-infrared wavelength ranges, the apparent attenuation of light results from its absorption or scattering. In horticultural applications, frequently, the normalized difference vegetation index (NDVI) is employed to reduce the effects of varying scattering properties on the apparent signal. However, this simple approach appears to be limited. In the laboratory, with time-resolved reflectance spectroscopy, the absorption coefficient, μa , and the reduced scattering coefficient, μs ', can be analyzed separately. In this study, these differentiated optical properties were recorded (540-940 nm), probing fruit tissue from the skin up to 2 cm depth in apple (Malus × domestica 'Elstar') and plum (Prunus domestica 'Tophit plus') harvested four times (65-145 days after full bloom). The μa spectra showed typical peak at 670 nm of the chlorophyll absorption. The μs ' at 670 nm in apple changed by 14.7% (18.2-15.5 cm(-1) ), while in plum differences of 41.5% (8.5-5.0 cm(-1) ) were found. The scattering power, the relative change of μs ', was zero in apple, but enhanced in plum over the fruit development period. This mirrors more isotropic and constant structures in apple compared with plum. For horticultural applications, the larger variability in scattering properties of plum explains the discrepancy between commercially assessed NDVI values or similar indices and the absolute μa values in plum (R < 0.05), while the NDVI approach appeared reasonable in apple (R ≥ 0.80).

Determination of coastal border line: an integrated approach for a part of Antalya coast (Turkey)

Sustainable development in coastal areas requires mapping of geological and geomorphological features of the area and precise determination of coastal border line. Such a study was carried out in the city of Antalya, which is undergoing intense infrastructural development in order to accommodate touristic activities. In the field, high water level, which is determined from moist-dry sand border and current berm crest, was taken as the indicator for shoreline, while vegetation line and topographic rises were taken as indicators of coastal border line. The geographic coordinates of all of the features were determined using differential Global Positioning System with application of “real-time kinematic measurement” technique. Quickbird satellite images of 2002 and 2009 were used in order to observe natural state of the coastal area before the constructions, to complete geomorphological map, and to confirm the coastal border line determined in the field. In addition, the natural vegetation line before the infrastructural development was determined using normalized difference vegetation index approach. Using grain size parameters for identification of coastal border line, high and low water levels were tested. The results showed that from low water level to coastal border line, D 50 (median grain size) and coefficient of uniformity decrease while coefficient of curvature increases systematically. This study showed that use of only one approach to determine coastal border line is insufficient, and an integrated methodology is required.

Determination of crop evapotranspiration of table grapes in a semi-arid region of Northwest Mexico using multi-spectral vegetation index

The main goal of this research is to develop and to evaluate a relationship established between Normalized Difference Vegetation Index (NDVI) and crop coefficient (Kc) for estimating crop evapotranspiration (ETc) of table grapes vineyards (Vitis vinifera L., cvs. Perlette and Superior) in the semi-arid region of Northwest Mexico. Two consecutive growing seasons (2005 and 2006) of continuous measurements of ETc with the eddy covariance system were used to test the performance of the Kc–NDVI relationship. An exponential relation relating Kc to NDVI (R2=0.63) is proposed and tested here as the basis for calculating ETc. The obtained results indicate that the Kc–NDVI approach estimates ETc reasonably well over two growing seasons. The root mean square error (RMSE) between measured and derived ETc from NDVI during 2005 and 2006 were respectively about 0.45 and 0.76mmday−1. Some discrepancies between measured and simulated ETc occurred when NDVI saturates at high values, causing the under-estimation of evapotranspiration.

Monitoring of cropland practices for carbon sequestration purposes in north central Montana by Landsat remote sensing

We used an object-oriented approach in conjunction with the Random Forest algorithm to classify agricultural practices, including tillage (till or no-till (NT)), crop intensity, and grassland-based conservation reserve (CR). The object-oriented approach allowed for per-field classifications and the incorporation of contextual elements in addition to spectral features. Random Forest is a classification tree-based advanced classifier that avoids data over-fitting associated with many tree-based models and incorporates an unbiased internal classification accuracy assessment. Landsat satellite imagery was chosen for its continuous coverage, cost effectiveness, and image accessibility. Classification results for 2007 included producer's accuracies of 91% for NT and 31% for tillage when applying Random Forest to image objects generated from a May Landsat image. Low classification accuracies likely were attributed to the misclassification of conservation-based tillage practices as NT. Results showed that the binary separation of tillage from NT management is likely not appropriate due to surface spectral and textural similarities between NT and conservation-type tillage practices. Crop and CR lands resulted in producer's accuracies of 100% and 90%, respectively. Crop and fallow producer's accuracies were 95% and 82% in the 2007 classification, despite post-senesced vegetation; misclassification within the fallow class was attributed to pixel-mixing problems in areas of narrow (< 100 m) strip management. A between-date normalized difference vegetation index approach was successfully used to detect areas having “changed” in vegetation status between the 2007 and prior image dates; classified “changed” objects were then merged with “unchanged” objects to produce crop status maps. Field crop intensity was then determined from the multi-year analysis of generated crop status maps.

Estimating Impervious Surfaces Area of Urban Watersheds Using ASTER Data

In this paper we present and compare two approaches in estimating impervious surfaces area for an urban watershed in northern New Jersey, United States using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. The first approach is to use the spectral mixture analysis (SMA) to separate image pixels into a linear combination of three typical urban land covers: vegetation, impervious surface and soil. The other is to use the normalized difference vegetation index (NDVI) to estimate imperviousness from the same imagery. The accuracies of the estimated imperviousness were assessed using a high-resolution color-infrared orthophoto. In total, 100 polygons with areas between 3 and 6 hectares were randomly selected from five distinct land use/cover categories and the percentage of impervious surface of each polygon was digitized and calculated. The results showed that nearly 90 percent of the variation in actual impervious surfaces in this watershed can be explained by the estimated impervious surfaces by a linear regression model (R 2 = 0.898). The NDVI approach is recommended for urban environments with small proportion of barren soils for its simplicity, while the SMA is suitable for urban environments with approximately equally-distributed vegetation, impervious surfaces and barren soil.

A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery

Plant foliage density expressed as leaf area index (LAI) is used in many ecological, meteorological, and agronomic models, and as a means of quantifying crop spatial variability for precision farming. LAI retrieval using spectral vegetation indices (SVI) from optical remotely sensed data usually requires site-specific calibration values from the surface or the use of within-scene image information without surface calibrations to invert radiative transfer models. An evaluation of LAI retrieval methods was conducted using (1) empirical methods employing the normalized difference vegetation index (NDVI) and a new SVI that uses green wavelength reflectance, (2) a scaled NDVI approach that uses no calibration measurements, and (3) a hybrid approach that uses a neural network (NN) and a radiative transfer model without site-specific calibration measurements. While research has shown that under a variety of conditions NDVI is not optimal for LAI retrieval, its continued use for remote sensing applications and in analysis seeking to develop improved parameter retrieval algorithms based on NDVI suggests its value as a “benchmark” or standard against which other methods can be compared. Landsat-7 ETM+ data for July 1 and July 8 from the Soil Moisture EXperiment 2002 (SMEX02) field campaign in the Walnut Creek watershed south of Ames, IA, were used for the analysis. Sun photometer data collected from a site within the watershed were used to atmospherically correct the imagery to surface reflectance. LAI validation measurements of corn and soybeans were collected close to the dates of the Landsat-7 overpasses. Comparable results were obtained with the empirical SVI methods and the scaled SVI method within each date. The hybrid method, although promising, did not account for as much of the variability as the SVI methods. Higher atmospheric optical depths for July 8 leading to surface reflectance errors are believed to have resulted in overall poorer performance for this date. Use of SVIs employing green wavelengths, improved method for the definition of image minimum and maximum clusters used by the scaled NDVI method, and further development of a soil reflectance index used by the hybrid NN approach are warranted. More importantly, the results demonstrate that reasonable LAI estimates are possible using optical remote sensing methods without in situ, site-specific calibration measurements.