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: Results indicate that multispectral information characterized by greater spatial resolution can successfully discriminate between and within forest species, thus providing an accurate framework for commercial forest species mapping.
Abstract: WorldView-2 multispectral wavebands (8 wavebands; 427-908 nm spectral range; 2 m spatial resolution) were utilized to classify six commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) in South Africa using the partial least squares discriminant analysis (PLS-DA) technique. Results indicate that the WorldView-2 imagery produced an overall accuracy of 85.42% and a kappa statistic value of 0.83, with individual forest species accuracies ranging between 63% and 100%. The variable importance in the projection (VIP) method was then used to identify the most important wavebands that were most effective in discriminating the forest species. Four VIP bands were ranked with thresholds greater than one and produced an overall accuracy of 84.38% and kappa value of 0.81, with individual forest species accuracies between 69% and 100%. More specifically, the VIP bands that were found to be important in the classification were the coastal blue (427 nm), blue (478 nm), green (546 nm) and red (659 nm) and confirmed the relative importance of the visible region of the electromagnetic spectrum in discriminating forest species. Overall, results indicate that multispectral information characterized by greater spatial resolution can successfully discriminate between and within forest species, thus providing an accurate framework for commercial forest species mapping.
67 citations
"Efficient Recognition of Forest Spe..." refers methods in this paper
...An attempt is made to distinguish different tree species using Worldview-2 in Kwazulu Natal, South Africa [26]....
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TL;DR: In this article, recent micrometeorological measurements for Amazonian rainforest are reviewed, emphasising those aspects of the radiation and heat balance which are likely to change with deforestation, and possible consequences of such deforestation are considered by examining the sensitivity of the surface energy balance to changes in those parameters which would be most drastically altered.
Abstract: Recent micrometeorological measurements for Amazonian rainforest are reviewed, emphasising those aspects of the radiation and heat balance which are likely to change with deforestation. The possible consequences of such deforestation are considered by examining the sensitivity of the surface energy balance to changes in those parameters which would be most drastically altered.
62 citations
"Efficient Recognition of Forest Spe..." refers background in this paper
...The need is particularly great for tropical forests, because they are major repositories of plant diversity [4], [5]....
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TL;DR: This paper presents the implementation of a Geospatial approach for improving the Municipal Solid Waste disposal suitability site assessment in growing urban environment using Multi Criteria Geographical Information System and Remote Sensing for selection of suitable disposal sites.
Abstract: This paper presents the implementation of a Geospatial approach for improving the Municipal Solid Waste (MSW) disposal suitability site assessment in growing urban environment. The increasing trend of population growth and the absolute amounts of waste disposed of worldwide have increased substantially reflecting changes in consumption patterns, consequently worldwide. MSW is now a bigger problem than ever. Despite an increase in alternative techniques for disposing of waste, land-filling remains the primary means. In this context, the pressures and requirements placed on decision makers dealing with land-filling by government and society have increased, as they now have to make decisions taking into considerations environmental safety and economic practicality. The waste disposed by the municipal corporation in the Bhagalpur City (India) is thought to be different from the landfill waste where clearly scientific criterion for locating suitable disposal sites does not seem to exist. The location of disposal sites of Bhagalpur City represents the unconsciousness about the environmental and public health hazards arising from disposing of waste in improper location. Concerning about urban environment and health aspects of people, a good method of waste management and appropriate technologies needed for urban area of Bhagalpur city to improve this trend using Multi Criteria Geographical Information System and Remote Sensing for selection of suitable disposal sites. The purpose of GIS was to perform process to part restricted to highly suitable land followed by using chosen criteria. GIS modeling with overlay operation has been used to find the suitability site for MSW.
54 citations
Additional excerpts
...IMAGINE and Arc GIS for LULC classification [30], [31] and spectra collection....
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TL;DR: In this paper, the changes that occurred in land use/land covers (LULCs) over a time span from 1990 to 2005 using multi date data of a part of Punjab.
Abstract: The monitoring of land use/land covers (LULCs) is an indispensable exercise for all those involved in executing policies to optimize the use of natural resources and minimize the ill impacts on the environment. The study here aims at analyzing the changes that occurred in LULC over a time span from 1990 to 2005 using multi date data of a part of Punjab. The digital data consisted of two sets of Landsat Thematic Mapper (TM) data and one set of IRS-1C data. Utilizing hybrid classification technique for interpretation and on field validation, it has been found that canal irrigation leads to changes in LULC as there is a change in cropping pattern as well as increase in water logged area.
48 citations
Additional excerpts
...ecosystem by effect of the canals [16]....
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TL;DR: This study focus on the biomass estimation of Sariska Wildlife Reserve using forest inventory and geospatial approaches to develop a model based on the statistical correlation between biomass measured at plot level and the associated spectral characteristics.
Abstract: This study focus on the biomass estimation of Sariska Wildlife Reserve using forest inventory and geospatial approaches to develop a model based on the statistical correlation between biomass measured at plot level and the associated spectral characteristics. The multistage statistical technique with incorporated the satellite data of IRS P-6 LISS III gives a precise estimation of biomass. Forest cover, forest stratum, and biomass maps were generated in the study. Spectral signatures along with tonal and textural variations were used to classify different forest types validated with GPS and ground truth data. Altitude dependent vegetation and contour information from toposheets were also considered while classifying imagery during interpretation. Sample plots were laid in study area with 0.1 ha area at intersect of the diagonals of the plots. DBH and height of all the trees inside the plot were measured and converted to biomass using volumetric equations depending upon specific gravity. The specific gravity of each tree species differ from each other and sometimes unique in different regions and varies from forest type of different regions. Estimation of tree biomass can serve as useful benchmark for future studies in related areas. Linear equation obtained was used as the model to generate final biomass map where predicted and estimated biomass were compared for each band of the satellite imageries. Linear, logarithm and power exponential models were compared to each other for correlation coefficient. Correlation between estimated and predicted AGB is 0.835 and coefficient of determination (r2) value is 0.698.
48 citations
"Efficient Recognition of Forest Spe..." refers background or methods in this paper
...The extensive exploitation of advanced geospatial technology and remote sensing satellites in the last decades has offered efficient, reliable and practical way to characterize global ecosystem properties [18]–[25]....
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...Earlier, biomass of the different species were estimated using the satellite data with intensive field surveys and reported some forest species mapping [25]....
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