Efficient Recognition of Forest Species Biodiversity by Inventory-Based Geospatial Approach Using LISS IV Sensor
TL;DR: In this paper, the authors used field-based surveys along with remote sensing technologies using a regression model to estimate and recognize different species diversity in Sariska Tiger Reserve, where a positive correlation was found in the infrared band even negative correlation has been found in other bands.
Abstract: Tropical forest is one of the great biodiversity repositories of the world ecosystem. Biodiversity is depleting very fast due to conversion of forest region into agricultural or other land use. Here comes the role of biodiversity assessment and evaluation of spatial data of species to prioritize the conservation purposes. Traditionally, ground-based plots were used to assess different biodiversity. Later on, remote sensing approaches were also incorporated along with field-based studies to quantify the results accurately. Assessment of biodiversity constitutes estimation of various indices that were obtained using ground-based plot or survey. With the advancement of the remote sensing technology, spatial information about tree species was collected using field sample and satellite data and field sample plots within the Sariska Tiger Reserve. Different diversity indices were calculated like α, β, diversity, and others, i.e., Pilot's index (J), Shannon-Wiener index (SR), Margalef index (E
w
), and Whittaker's index (H'). The multistage statistical techniques, which integrate high spatial resolution and spectral characteristics of satellite data (LISS IV), will help in providing precise information about tree species. Regression analysis provides better results to identify forest species among different bands. A positive correlation has been found in the infrared band even negative correlation has been found in other bands. This paper incorporates field-based surveys along with remote sensing technologies using a regression model (r
2
= 0.636) to estimate and recognize different species diversity in Sariska Tiger Reserve.
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TL;DR: Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment as mentioned in this paper, and Earth observation (EO) technology provides an in...
Abstract: Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment. Earth observation (EO) technology provides an in...
92 citations
Cites background from "Efficient Recognition of Forest Spe..."
...…in LULC domain (Thenkabail and Lyon 2016) to accurately identify different features using unique spectral information (St-Louis et al. 2009; Kumar et al. 2015), attributed to their unique signature due to chemical and physical properties (Gould 2000; Gillespie et al. 2008; Palmer et al.…...
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...…ob er 20 18 _2 01 81 00 5a .p df . e T IR m ea su re s bo th da y an d ni gh t da ta w ith 1 da yt im e im ag e an d 1 ni gh ttim e im ag e ev er y 5 da ys . multispectral images have high spatial resolution but they are unable to identify different feature in the similar group (Kumar et al. 2015)....
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...For example, in plants, they differ due to pigments, structure and water content (Kalacska et al. 2007; White et al. 2010; Kumar et al. 2015; Thenkabail and Lyon 2016; Pandey et al. 2019) and soil have different spectral signature due to variation in iron oxides, organic matter, clays, calcite,…...
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TL;DR: In this article, the authors used spectral angle mapper to identify species, provide spatial distribution of the species and estimate the biomass in the mangrove forest, Bhitarkanika India.
Abstract: The objective of this research is to identify species, provide spatial distribution of the species and estimate the biomass in the mangrove Forest, Bhitarkanika India. Mangrove ecosystems play an important role in regulating carbon cycling, thus having a significant impact on global environmental change. Extensive studies have been conducted for the estimation of mangrove species identification and biomass estimation. However, estimation at a regional level with species-wise biomass distribution has been insufficiently investigated in the past because either research focuses on the species distribution or biomass assessment. Study shows that good relationship has been achieved between stem volume (field measured data) and Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from satellite image and further these two indices are employed to estimate the biomass in the study site. Three models- linear, logarithmic and polynomial (second degree) are used to estimate biomass derived from EVI and NDVI. The hyperspectral data (spatial resolution ~ 30 m) is utilised to identify ten mangrove plant species. We have prepared the spatial distribution map of these species using spectral angle mapper. We have also generated mangrove species-wise biomass distribution map of the study site along with areal coverage of each species. The results indicate that the Sonneratia apetala Buch.-Ham. and Cynometra iripa Kostel has the highest biomass among all ten identified species, 643.12 Mg ha−1 and 652.14 Mg ha−1. Our study provided a positive relationship between NDVI, EVI, and the estimated biomass of Bhitarkanika Forest Reserve Odisha India. The study finds a similar results for both NDVI and EVI derived biomass, while linear regression has more significant results than the polynomial (second degree) and logarithmic regression derived biomass. The polynomial is found slightly better than the logarithmic when using the EVI as compared to NDVI derived biomass. The spatial distribution of species-wise biomass map obtained in this study using both, EVI and NDVI could be used to provide useful information for biodiversity assessment along with the sustainable solutions to different problems associated with the mangrove forest biodiversity. Thus, biomass assessment of larger regions can be achieved by utilization of remote sensing based indices as concluded in the present study.
57 citations
TL;DR: In this paper, the authors assessed distribution of soil organic carbon (SOC) using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India.
Abstract: Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R
2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.
32 citations
Cites background from "Efficient Recognition of Forest Spe..."
...In terms of the succession, and concept of continuum of vegetation, the large-scale formations in the area are Acacia catechu and Anogeissu spendula vegetation types (Jain et al. 2016; Kumar et al. 2015)....
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TL;DR: The spatiotemporal dynamics of SOM highlighted the necessity of modeling with fused remote sensing images, and more effective modeling could be expected with the continued increase in SOM in future.
31 citations
TL;DR: In this paper, the authors used quadrat sampling in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, which was used to compute Shannon-Weiner Diversity Index (H′), above ground biomass (AGB) was calculated measuring the Height and Diameter at Breast Height (DBH) of different trees in the sampling plots.
Abstract: Biodiversity loss in tropical forests is rapidly increasing, which directly influence the biomass and productivity of an ecosystem. In situ methods for species diversity assessment and biomass in synergy with hyperspectral data can adeptly serve this purpose and hence adopted in this study. Quadrat sampling was carried out in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, which was used to compute Shannon–Weiner Diversity Index (H′). Above ground biomass (AGB) was calculated measuring the Height and Diameter at Breast Height (DBH) of different trees in the sampling plots. Four spectral indices, namely Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Photochemical Reflectance Index (PRI), and Structure Insensitive Pigment Index (SIPI) were derived from the EO-1 Hyperion Data. Spearman and Pearson’s correlation analysis was performed to examine the relationship between H′, AGB and spectral indices. The best fit model was developed by establishing a relationship between H′ and AGB. Fifteen models were developed by performing multiple linear regression analysis using all possible combinations of spectral indices and H′ and their validation was performed by relating observed H′ with model predicted H′. Pearson’s correlation relation showed that SIPI has the best relationship with the H′. Model 15 with a combination of NDVI, PRI and SIPI was determined as the best model for retrieving H′ based on its statistics performance and hence was used for generating species diversity map of the study area. Power model showed the best relationship between AGB and H′, which was used for the development of AGB map.
17 citations
References
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TL;DR: In this article, the authors investigated the use of satellite remote sensing data in forest-type mapping and change detection in a part of the Western Ghats in Karnataka, India.
Abstract: This study was undertaken to investigate the use of satellite remote sensing data in forest-type mapping and change detection in a part of the Western Ghats in Karnataka, India. Using Landsat MSS data, it could be possible to map various types of forests viz., evergreen, semi-evergreen, moist deciduous, dry deciduous, degraded and scrub, and to detect changes in their spatial distribution over a period of time. A reduction of 5.66% in total forest area was noted from 1973 to 1985. The decrease was pronounced in the case of semi-evergreen and moist deciduous forests. This study also demonstrated (a) that temporal, low-resolution satellite data could be conveniently used in forest-type mapping and temporal monitoring on an operational level and (b) that such a survey is time- and cost-effective.
44 citations
"Efficient Recognition of Forest Spe..." refers background in this paper
...It helps in achieving higher accuracy and more comprehensive results for classifications [8]....
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TL;DR: The relationship among alpha and beta diversity indices, computed from 141 randomly sampled quadrats, and the vegetation classes obtained by multi-spectral satellite image classification, were used as a strategy for mapping plant diversity in a tropical landscape mosaic.
Abstract: The relationships among alpha and beta diversity indices, computed from 141 randomly sampled quadrats, and the vegetation classes obtained by multi-spectral satellite image classification, were used as a strategy for mapping plant diversity in a tropical landscape mosaic. A relatively high accuracy of the land cover map was revealed by the overall accuracy assessment and the Cohen's Kappa statistic. Species accumulation models were used to evaluate how representative the sample size was of the different vegetation types. A standard one-way, between-subjects ANOVA confirmed a significant reduction of the within-class variance of plant diversity with respect to their total variance across the landscape. Computed uniformity indices, to assess the internal uniformity of vegetation classes on the diversity indices, confirmed the goodness of the mapped classes in stratifying variability of plant di- versity. This allowed for the use of the mapped classes as spatial interpolators of plant diversity values for estimation and up-scaling purposes. Finally, it was revealed that the plant diversity of the landscape depends, to a large extent, on the diversity contained in the most mature forest class, which is also the most diverse community in the studied area. High and moderate beta diversity values between mature forests and both the secondary associations and the first stages of succession, respectively, indicated that there is a significant contribution to the diversity of the landscape by those vegetation classes.
42 citations
"Efficient Recognition of Forest Spe..." refers methods in this paper
...Multi-satellite data uses the classification of plants for the spatial distribution of biodiversity with field data [11]....
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TL;DR: In this paper, a hybrid classification approach is proposed for land use and land cover (LU/LC) classification, which is found highly useful in achieving high accuracy for areas where spectral classes of images are inseparable.
Abstract: Land-use and land-cover (LU/LC) studies help in assessing and monitoring the status of the natural resources, detecting the changes in spatial and temporal scale and predict them for the future. Due to changing environments and increasing anthropogenic pressures, the demand for a LU/LC database at the global level is increasing. Therefore, a comprehensive understanding of LU/LC at both local and regional scales is important since it plays a pivotal role in socioeconomic development and global environmental changes. There are many approaches for LU/LC analysis such as supervised classification, unsupervised classification and onscreen digitization but simplest and most popular approach on IRS LISS-III and ${\rm Landsat\hbox{-}ETM}{+}$ satellite data revealed a serious problem in some semidesert areas caused by spectral confusion because of the similar radiometric response like scrub land with harvested land, built-up with bare hills and many other. Present study suggests hybrid classification approach for LU/LC classification, which is found highly useful in achieving high accuracy for areas where spectral classes of images are inseparable.
39 citations
"Efficient Recognition of Forest Spe..." refers methods in this paper
...All the spatial data from the forest plan were scanned using a scanner and transformed into computer-readable digital maps [32], [33]....
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TL;DR: PP in concentrations of 5–10 μm/ml was found to selectively antagonise the actions of F prostaglandins on the jird colon.
39 citations
01 Jan 1965
38 citations
"Efficient Recognition of Forest Spe..." refers background in this paper
...In a stratified random design, the study area is first subdivided into homogeneous stands or units, and then place samples in each unit randomly [29]....
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