Monitoring and mapping of seasonal vegetation trend in Tamil  Nadu using NDVI and NDWI imagery

Prajesh, P.J.; Kannan, Balaji; Pazhanivelan, S.; Ragunath, K.P.

doi:10.31018/jans.v11i1.1964

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Specifically, the minimum of NDWI2 (10 th NDWI2 ) and its temporal diversity (Q NDWI2 ) assumed particularly high values and emerged as the most important variables in the first RF cycle. As previously observed, the spectral responses of water and dryland are very different [94,95]. Such differences clearly emerged at coarser classification levels on the transition area we analyzed here.…”

Section: Discussionsupporting

confidence: 78%

“…Furthermore, the implemented RF classification organized in sequential cycles ensured accurate and ecologically coherent results. For instance, water presence and seasonality evidenced by the yearly behavior of NDWI2 were important variables in the first cycle of classification implemented on overall coastal area extent, which confirmed its potential for discriminating water from emerged areas [94,95] and suggested its role when mapping transition systems between terrestrial and marine realms. Similarly, our results confirmed that biomass and bare soil indexes and their temporal variability are important variables for mapping terrestrial cover classes [16,28,92,101].…”

Section: Discussionmentioning

confidence: 67%

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Mapping Coastal Dune Landscape through Spectral Rao’s Q Temporal Diversity

et al. 2020

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Coastal dunes are found at the boundary between continents and seas representing unique transitional mosaics hosting highly dynamic habitats undergoing substantial seasonal changes. Here, we implemented a land cover classification approach specifically designed for coastal landscapes accounting for the within-year temporal variability of the main components of the coastal mosaic: vegetation, bare surfaces and water surfaces. Based on monthly Sentinel-2 satellite images of the year 2019, we used hierarchical clustering and a Random Forest model to produce an unsupervised land cover map of coastal dunes in a representative site of the Adriatic coast (central Italy). As classification variables, we used the within-year diversity computed through Rao’s Q index, along with three spectral indices describing the main components of the coastal mosaic (i.e., Modified Soil-adjusted Vegetation Index 2—MSAVI2, Normalized Difference Water Index 2—NDWI2 and Brightness Index 2—BI2). We identified seven land cover classes with high levels of accuracy, highlighting different covariates as the most important in differentiating them. The proposed framework proved effective in mapping a highly seasonal and heterogeneous landscape such as that of coastal dunes, highlighting Rao’s Q index as a sound base for natural cover monitoring and mapping. The applicability of the proposed framework on updated satellite images emphasizes the procedure as a reliable and replicable tool for coastal ecosystems monitoring.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 67%

Mapping Coastal Dune Landscape through Spectral Rao’s Q Temporal Diversity

et al. 2020

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“…In particular, NDVI [109] is considered the most popular vegetation index in crop monitoring and classification studies [110], being indicative of vegetation's status and photosynthetic activity [111]. NDWI proposed by Gao (1996) [112], is an effective indicator of vegetation's water content [113]. PSRI, defined by Merzlyak (1999) [114] exhibits great sensitivity in the aging stage of plant development.…”

Section: Sentinel-2 Datamentioning

confidence: 99%

Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification

et al. 2022

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Crop classification is an important remote sensing task with many applications, e.g., food security monitoring, ecosystem service mapping, climate change impact assessment, etc. This work focuses on mapping 10 crop types at the field level in an agricultural region located in the Spanish province of Navarre. For this, multi-temporal Synthetic Aperture Radar Polarimetric (PolSAR) Sentinel-1 imagery and multi-spectral Sentinel-2 data were jointly used. We applied the Cloude–Pottier polarimetric decomposition on PolSAR data to compute 23 polarimetric indicators and extracted vegetation indices from Sentinel-2 time-series to generate a big feature space of 818 features. In order to assess the relevance of the different features for the crop mapping task, we run a number of scenarios using a Support Vector Machines (SVM) classifier. The model that was trained using only the polarimetric data demonstrates a very promising performance, achieving an overall accuracy over 82%. A genetic algorithm was also implemented as a feature selection method for deriving an optimal feature subset. To showcase the positive effect of using polarimetric data over areas suffering from cloud coverage, we contaminated the original Sentinel-2 time-series with simulated cloud masks. By incorporating the genetic algorithm, we derived a high informative feature subset of 120 optical and polarimetric features, as the corresponding classification model increased the overall accuracy by 5% compared to the model trained only with Sentinel-2 features. The feature importance analysis indicated that apart from the Sentinel-2 spectral bands and vegetation indices, several polarimetric parameters, such as Shannon entropy, second eigenvalue and normalised Shannon entropy are of high value in identifying crops. In summary, the findings of our study highlight the significant contribution of Sentinel-1 PolSAR data in crop classification in areas with frequent cloud coverage and the effectiveness of the genetic algorithm in discovering the most informative features.

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