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: For the 25 species with N≥ 10, habitat specialization as a function of the level of annual inundation was demonstrated, and for five of these species that occurred on plot 1, further refinement of niche as afunction of gradient was demonstrated.
173 citations
"Efficient Recognition of Forest Spe..." refers background in this paper
...The high-density values of trees were 639–713 stems ha−1 at Central Amazon upland forest [42], [43]....
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TL;DR: In this paper, a new methodology to estimate the biomass (organic matter) of conifer-dominated boreal forests is developed, which aims to estimate biomass of extensive areas where ground data are limited.
Abstract: In this paper, a new methodology to estimate the biomass (organic matter) of conifer-dominated boreal forests is developed. It aims to estimate biomass of extensive areas where ground data are limited. First, the principal models are computed using ground measurements and high resolution satellite images. Spectral models are then applied directly to a calibrated AVHRR image mosaic covering the entire area of interest. This methodology was tested quantitatively in Finland, where detailed forest measurement data are available, on an area reaching from the west coast of Norway to the Ural mountains. The methodology appeared to perform beyond pre-test expectation.
119 citations
TL;DR: There are still an inadequate number of inventories of Amazonian terra firme forests to elucidate the major floristic pattern a both regional and local levels and it was found that, in the study area, there was a greater proportion of trees of 60cm diameter or more and consequently a considerably higher total basal area.
Abstract: Four hectares were inventoried for all trees with diameter at breast height (DBH) of 10cm or greater in a terra firme forest 200km Northeast of Manaus, Central Amazonia. The number of species varied from 137 to 168, the number of individuals from 639 to 713, total basal area from 32.8 to 40.2 and total biomass from 405 to 560 tons per hectare. The majority of trees, 90%, had a DBH between 10 and 30 cm. Leguminosae, Lauraceae, Sapotaceae, Chrysobalanaceae and Moraceae were the most rich families (number of species) in all sampled hectares. The most abundant families in all sampled hectares (number of trees) were Leguminosae, Burseraceae, Myristicaceae, Moraceae and Chrysobalanaceae. The most dominant families in all sampled hectares (basal area and biomass) were Leguminosae, Lecythidaceae, Chrysobalanaceae, Bombacaceae and Moraceae. Similarity indexes at family level varied from 67 to 86% between the four hectares sampled. Alexa grandiflora (Leguminosae) was the most abundant species in the hectares one and three, while Scleronema micranthum (Bombacaceae), and Oenocarpus bacaba (Palmae) were the most abundant species in hectares two and four. S. micranthum was the most dominant species (basal area) in hectares one and two, while Bertholletia excelsa (Lecythidaceae) and Goupia glabra (Celastraceae) were the most dominant species in hectares three and four. S. micranthum (Bombacaceae), Buchenavia sp. 2 (Combretaceae), B. excelsa (Lecythidaceae) Couepia obovata (Chrysobalanaceae) were the most dominant species (biomass) in hectares one to four, respectively. Similarity indexes at species level varied from 26 to 44% between the four sampled hectares. This inventory is compared with previous studies and it was found that, in our study area, there was a greater proportion of trees of 60cm diameter or more and consequently a considerably higher total basal area. It is concluded that there are still an inadequate number of inventories of Amazonian terra firme forests to elucidate the major floristic pattern a both regional and local levels. Since the area is a mosaic of distinct floristic communities it is essential to obtain further standardized inventory data in order to set adequate conservation policies for the region.
87 citations
"Efficient Recognition of Forest Spe..." refers background in this paper
...The forest tree density was 245 stems ha−1 one of the small values in tropical forests [44]....
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TL;DR: In this article, a study was conducted using satellite remote sensing data Landsat MSS (Multi-spectral Scanner), ETM+(Enhanced Thematic Mapper), IRS P-6 (Indian Remote Sensing Satellite), LISS IV (Linear Imaging Self-Scanner), and IRSP-5 Cartosat-1 for the assessment of urban area change dynamics between years 1976 and 2008 in Bhagalpur city in the state of Bihar in India.
Abstract: Land consumption is increasing rapidly with the exponential growth of population. The built-up environment configuration influences the management processes for development and other municipality works. Population growth also affects the availability of land for different purposes in its spatial distribution. The present study was conducted using satellite remote sensing data Landsat MSS (Multi-spectral Scanner), ETM+ (Enhanced Thematic Mapper), IRS P-6 (Indian Remote Sensing Satellite), LISS IV (Linear Imaging Self-Scanner), and IRS P-5 Cartosat-1 for the assessment of urban area change dynamics between years 1976 and 2008 in Bhagalpur city in the state of Bihar in India. The ground truth and coordinate points were collected using a Global Positioning System (GPS) for the location of the built-up themes prepared in the Geographic Information System (GIS). Land Consumption Rate (LCR) and Land Absorption Coefficient (LAC) were introduced to aid in the quantitative assessment changes. The results show a rap...
85 citations
Additional excerpts
...IMAGINE and Arc GIS for LULC classification [30], [31] and spectra collection....
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TL;DR: This work shows that airborne hyperspectral sensing can be very effective at mapping canopy tree diversity, because its high spatial resolution allows within-plot heterogeneity in reflectance to be characterized, making it an effective tool for monitoring forest biodiversity over large geographic scales.
Abstract: Tropical forests are major repositories of biodiversity, but are fast disappearing as land is converted to agriculture. Decision-makers need to know which of the remaining forests to prioritize for conservation, but the only spatial information on forest biodiversity has, until recently, come from a sparse network of ground-based plots. Here we explore whether airborne hyperspectral imagery can be used to predict the alpha diversity of upper canopy trees in a West African forest. The abundance of tree species were collected from 64 plots (each 1250 m(2) in size) within a Sierra Leonean national park, and Shannon-Wiener biodiversity indices were calculated. An airborne spectrometer measured reflectances of 186 bands in the visible and near-infrared spectral range at 1 m(2) resolution. The standard deviations of these reflectance values and their first-order derivatives were calculated for each plot from the c. 1250 pixels of hyperspectral information within them. Shannon-Wiener indices were then predicted from these plot-based reflectance statistics using a machine-learning algorithm (Random Forest). The regression model fitted the data well (pseudo-R(2) = 84.9%), and we show that standard deviations of green-band reflectances and infra-red region derivatives had the strongest explanatory powers. Our work shows that airborne hyperspectral sensing can be very effective at mapping canopy tree diversity, because its high spatial resolution allows within-plot heterogeneity in reflectance to be characterized, making it an effective tool for monitoring forest biodiversity over large geographic scales.
70 citations