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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30 Sep 1988
TL;DR: In this paper, the authors define definitions of diversity and apply them to the problem of measuring species diversity, choosing an index and interpreting diversity measures, and applying them to structural and structural diversity.
Abstract: Definitions of diversity. Measuring species diversity. Choosing an index and interpreting diversity measures. Sampling problems. Structural diversity. Applications of diversity measures. Summary.
10,957 citations
TL;DR: It is shown that nature of unit variation is a naajor problenl in systematies, and that whether this variation is diserete, continuous, or in some other form, there is a need for appliGation of (uantitative and statistical methods.
Abstract: INTRODUCTION A renewed interest in objeetive and quantitative approaehes to the elassifieation of plant communities has led, within the past decade, to an extensive exalllination of systematic theory and technique. This examination, ineluding the work of Sorenson (1948), Motyka et al. (1950), Curtis & McIntosh (1951), Brown & Curtis (1952), Ramensky (1952), Whittaker (1954, 1956), Goodall (1953a, 1954b)? deVries (1953), Guinoehet (1954, 1955), Webb (1954), Eughes (1954) and Poore (1956) has acconlpanied theoretie studies in taxonomy [Fisher (1936), Womble (1951), Clifford & Binet (1954), Gregg (1954)] and in statisties (Isaaeson 1954). It is a Gonclusion of many of these studies that nature of unit variation is a naajor problenl in systematies, and that whether this variation is diserete, continuous, or in some other form, there is a need for appliGation of (uantitative and statistical methods. In eeologic elassifieation, an inereased use of ordinate systellls, sr hiGh has been stimulated by the developnlent of more effieient sampling teehniques and the collection of stand data on a large seale, has prompted the proposal of the term \"ordination\" ( Goodall 1953b ) . Goodall (1954a) has defined ordination as \"an arrangenlent of units in a unior multi-dinlensional order\" as synonylllous with \"Ordnung,\" (Ramensky
9,549 citations
"Efficient Recognition of Forest Spe..." refers background in this paper
...Pielou’s Index (J): It is the comparison of actual Shannon-Wiener with Shannon-Wiener if species had an equal proportion (log S) [39], J = H ′ ln (S) (8)...
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4,445 citations
"Efficient Recognition of Forest Spe..." refers methods in this paper
...The Whittaker’s index of β-diversity [38] was calculated using the following equation....
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TL;DR: This product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP, and a supervised classification methodology is used that exploits a global database of training sites interpreted from high-resolution imagery in association with ancillary data.
2,379 citations
TL;DR: In this paper, a 1km spatial resolution land cover classification using data for 1992-1993 from the Advanced Very High Resolution Radiometer (AVHRR) is presented. But the approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted.
Abstract: This paper on reports the production of a 1km spatial resolution land cover classie cation using data for 1992- 1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will be included as an at-launch product of the Moderate Resolution Imaging Spectroradiometer (MODIS) to serve as an input for several algorithms requiring knowledge of land cover type. The methodology was derived from a similar e A ort to create a product at 8km spatial resolution, where high resolution data sets were interpreted in order to derive a coarse-resolution training data set. A set of 37294 O 1km pixels was used within a hierarchical tree structure to classify the AVHRR data into 12 classes. The approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted. Multi- temporal AVHRR metrics were used to predict class memberships. Minimum annual red ree ectance, peak annual Normalized Di A erence Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not. Comparisons of the e nal product with regional digital land cover maps derived from high-resolution remotely sensed data reveal general agreement, except for apparently poor depictions of temperate pastures within areas of agriculture. Distinguishing between forest and non-forest was achieved with agreements ran- ging from 81 to 92% for these regional subsets. The agreements for all classes varied from an average of 65% when viewing all pixels to an average of 82% when viewing only those 1km pixels consisting of greater than 90% one class within the high-resolution data sets.
2,188 citations