Comparison Of Vegetation Indices Research Articles

Comparison of vegetation indices and image classification methods for mangrove mapping at semi-detailed scale in southwest of Rio de Janeiro, Brazil

This paper presents the mangrove mapping carried out in the Rio de Janeiro City, Brazil, using two remote sensing data processing approaches in order to evaluate their potentialities as a complementary tool for oil spill sensitivity mapping. Ten vegetation indices were computed using the Landsat 8 imagery available in Google Earth Engine, and subsequently their spectral patterns were classified through three supervised and five unsupervised methods. Additionally, one pre-processed Landsat 8 OLI bands composition were classified by these eight classification algorithms. To role as a ground-truth for the comparison of 88 automatically produced maps, a mangrove map was prepared based on the methodological guidelines of Oceanic Atmospheric Administration of United States of America for Environmental Sensitivity Index. The best results were presented by Cobweb unsupervised classification of Mangrove Vegetation Index, properly identifying a great mangrove habitats diversity, such as inland brackish, riverine fringe and seaward forests.

Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach

Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this study performed a comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) < 0.86) and CCC (R2 > 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE ≈ 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived from FAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas.

Comparison of Vegetation Indices from Two Ground Based Sensors

The best and commonly used ground-based sensor to monitor crop growth, ASD FieldSpecPro Spectroradiometer (Analytical Spectral Devices, Boulder, CO, USA) is a passive sensor, which can be used under adequate light condition. However, now-a-days active sensors such as GreenSeeker™ (GS) handheld crop response (Trimble Agriculture division, USA) are used for monitoring crop growth and are flexible in terms of timeliness and illumination conditions besides being cheaper than the ASD. Before its wide use, the suitability and accuracy of GS should be assessed by comparing the NDVI measured by this instrument with that by ASD, under diverse wheat growing conditions of India. Keeping this in view, the present experiment was undertaken with the following objectives: (1) to find out the temporal variation of NDVI measured both by ASD and GS treatments, (2) to find out relationship between the NDVI measured through ASD and GS and, (3) to evaluate the suitability of GS for NDVI measurements. It was observed that the numerical value of NDVI as measured by GS was always significantly (P < 0.05) lower than that measured by ASD for all the experiments under study. The NDVI-ASD and NDVI-GS were significantly positively correlated (P < 0.01) with the correlation coefficients being +0.94, +0.88 and +0.87 for irrigation and nitrogen experiment, irrigation and cultivars experiment, and tillage, residue and nitrogen experiments, respectively. Further, the regression equation developed between the NDVI-ASD and NDVI-GS: [NDVI-GS = 1.070 × (NDVI-ASD − 0.292] can be successfully used to compute the NDVI of ASD from that computed by GS.

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Remote sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered remote-sensing method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet (Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing remote-sensing methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications.Overall, both approaches are promising to remotely estimate LAI and canopy chlorophyll content (RMSE≤10%). In addition, VIs show a great potential to retrieve canopy nitrogen content (RMSE=10%). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs (RMSE=17%). As expected when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively. Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAV-embedded multispectral cameras, with which only a few wavebands are available.

Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: Comparison of vegetation indices and spectral mixture analysis

Tree canopy cover is a major biophysical attribute of dryland ecosystems. Monitoring its long-term changes over large spatial extents is critical for understanding woody vegetation response to climate variability and global change. However, quantifying tree canopy cover with remotely sensed data remains a challenge for dryland ecosystems where vegetation is sparse and trees, shrubs, and grasses often co-exist at fine spatial scales. In this study, we developed a full SMA (spectral mixture analysis) method that regressed photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and shade components of the SMA with dryland tree cover to monitor tree cover dynamics on a pinyon–juniper woodland landscape in Nevada, USA using Landsat TM data. We assessed 1) how well this method could estimate tree cover in both disturbed (chained and burned) and non-disturbed woodland patches and 2) how sensitive this method was to the confounding effects of climatic variations. The assessment was conducted in comparison with two other more commonly used methods that regressed NDVI or PV with tree cover. Our results showed that although PV performed better than NDVI, both methods overestimated tree canopy cover within recently disturbed woodland patches where the confounding effects of shrubs on greenness index were higher than in non-disturbed patches. The full SMA efficiently quantified variations within post-chaining patches in addition to non-disturbed patches, but overestimated tree cover within burned patches. Of the three methods tested, only full SMA showed promising capability for mitigating the confounding effects of interannual climatic variations on monitoring the woodland recovery process. Our results are generalizable to other semi-arid landscapes comprising a mosaic of small-statured trees intermixed with shrub steppe vegetation.

Comparison of vegetation indices for practicable homology

Vegetation indices are widely used to assess quantitatively the biophysical characteristics of vegetation from remote sensing measurements. Different indices have their own advantages in retrieving vegetation information. It is very difficult to precisely attribute any vegetation index to any particular vegetation biophysical parameter. This study examines the correlations among different vegetation indices derived from a set of mustard, gram and wheat fields at three different phenological growth stages. The results are presented as correlation matrices along with correlation scatter plots. Homologous (equi-magnitude) vegetation information is represented by NDVI, PVI and AtRVI for wheat crop with leaf area index less than 1.

A comparison of vegetation indices over a global set of TM images for EOS-MODIS

A set of Landsat Thematic Mapper images representing a wide range of vegetation conditions from the NASA Landsat Pathfinder, global land cover test site (GLCTS) initiative were processed to simulate the Moderate Resolution Imaging Spectroradiometer (MODIS), global vegetation index imagery at 250 m pixel size resolution. The sites included boreal forest, temperate coniferous forest, temperate deciduous forest, tropical rainforest, grassland, savanna, and desert biomes. Differences and similarities in sensitivity to vegetation conditions were compared among various spectral vegetation indices (VIs). All VIs showed a qualitative relationship to variations in vegetation. However, there were significant differences among the VIs over desert, grassland, and forested biomes. The normalized difference vegetation index (NDVI) was sensitive to and responded primarily to the highly absorbing red reflectance band, while other indices such (is the soil and atmosphere resistant vegetation index (SARVI) were more responsive to variations in the near-infrared (NIR) band. As a result, we found the NDVI to mimic red reflectances and saturate over the forested sites while the SARVI, by contrast, did not saturate and followed variations in NIR refleetances. In the arid and semiarid biomes, the NDVI was much more sensitive to canopy background variations than the SARVI. Maximum differences among vegetation index behavior occurred over the evergreen needleleaf forest sites relative to the deciduous broadleaf forests and drier, grassland, and shrub sites. These differences appear to be useful in complementing the NDVI for improved monitoring of vegetation, with the NDVI sensitive to fraction of absorbed photosynthetic active radiation and the SARVI more sensitive to structural canopy parameters such as leaf area index and leaf morphology.

Development of vegetation and soil indices for MODIS-EOS

One of the primary interests of the NASA Earth Observing System (EOS) program is to study the role of vegetation in biospheric processes. Currently, the normalized difference vegetation index (NDVI) is utilized to study and monitor vegetation activity on an operational basis at global scales. Recently developed variants to the NDVI equation, based on improved knowledge of atmosphere, canopy background, and sensor-view geometry, are being considered for use with the EOS Moderate-Resolution Imaging Spectrometer (MODIS) sensor. A set of criteria is established to evaluate their performance with respect to the vegetation signal and atmospheric and soil sources of noise. These include the vegetation signal-to-noise ratio (S/N), % relative error, and vegetation equivalent noise (VEN), which form the basis for the evaluation and comparison of vegetation indices (VIs) across a wide range in vegetation covers. The NDVI variant equations outperformed the NDVI by minimizing atmospheric and/or soil background sources of contamination as well as increasing vegetation signal sensitivity. Preliminary estimates, based on the data sets tested here, indicate an overall level of uncertainty of ± 0.4 LAI (≈ 15% cover) with the use of the soil adjusted and atmospherically resistant vegetation index (SARVI), compared with ± 0.8 LAI (30% cover) for the NDVI. Eventually, the final evaluation and validation of VIs for MODIS will include a field campaign strategy, simulation studies, and a worldwide establishment of test sites that collectively cover a diverse range of biomes. Lastly, the retrieval of canopy biophysical information from these VIs are discussed.

Calibration of satellite radiometers and the comparison of vegetation indices

Satellite technology provides a steadily improving capability to monitor surface land use and vegetation. However, the increasing number of satellite sensors has led to a variety of spectral indices which may be used to characterize vegetation. A basis is developed for comparing results from different sensors using instrument calibration coefficients, and the derived radiances are related to reflectances, principal component variables such as greenness, and spectral vegetation indices.