Temporal dynamics of above ground biomass of Kaimoor Wildlife Sanctuary, Uttar Pradesh, India: conjunctive use of field and Landsat data

Abstract. Long-term studies using the RAINFOR network of forest plots have generated significant insights into the spatial and temporal dynamics of forest carbon cycling in Amazonia. In this work, we map and explore the landscape context of several major RAINFOR plot clusters using Landsat ETM+ satellite data. In particular, we explore how representative the plots are of their landscape context, and test whether bias in plot location within landscapes may be influencing the regional mean values obtained for important forest biophysical parameters. Specifically, we evaluate whether the regional variations in wood productivity, wood specific density and above ground biomass derived from the RAINFOR network could be driven by systematic and unintentional biases in plot location. Remote sensing data covering 45 field plots were aggregated to generate landscape maps to identify the specific physiognomy of the plots. In the Landsat ETM+ data, it was possible to spectrally differentiate three types of terra firme forest, three types of forests over Paleovarzea geomorphologycal formation, two types of bamboo-dominated forest, palm forest, Heliconia monodominant vegetation, swamp forest, disturbed forests and land use areas. Overall, the plots were generally representative of the forest physiognomies in the landscape in which they are located. Furthermore, the analysis supports the observed regional trends in those important forest parameters. This study demonstrates the utility of landscape scale analysis of forest physiognomies for validating and supporting the finds of plot based studies. Moreover, the more precise geolocation of many key RAINFOR plot clusters achieved during this research provides important contextual information for studies employing the RAINFOR database.

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Influence of landscape heterogeneity on spatial patterns of wood productivity, wood specific density and above ground biomass in Amazonia

Biogeosciences Discussions ◽

10.5194/bgd-6-2039-2009 ◽

2009 ◽

Vol 6 (1) ◽

pp. 2039-2083 ◽

Cited By ~ 3

Author(s):

L. O. Anderson ◽

Y. Malhi ◽

R. J. Ladle ◽

L. E. O. C. Aragão ◽

Y. Shimabukuro ◽

...

Keyword(s):

Temporal Dynamics ◽

Landscape Heterogeneity ◽

Landscape Context ◽

Above Ground Biomass ◽

Biophysical Parameters ◽

Specific Density ◽

Swamp Forest ◽

Mean Values ◽

Ground Biomass ◽

Plot Location

Abstract. Long-term studies using the RAINFOR network of forest plots have generated significant insights into the spatial and temporal dynamics of forest carbon cycling in Amazonia. In this work, we map and explore the landscape context of several major RAINFOR plot clusters using Landsat ETM+ satellite data. In particular, we explore how representative the plots are of their landscape context, and test whether bias in plot location within landscapes may be influencing the regional mean values obtained for important forest biophysical parameters. Specifically, we evaluate whether the regional variations in wood productivity, wood specific density and above ground biomass derived from the RAINFOR network could be driven by systematic and unintentional biases in plot location. Remote sensing data covering 45 field plots were aggregated to generate landscape maps to identify the specific physiognomy of the plots. In the Landsat ETM+ data, it was possible to spectrally differentiate three types of terra firme forest, three types of alluvial terrain forest, two types of bamboo-dominated forest, palm forest, Heliconia monodominant vegetation, swamp forest, disturbed forests and land use areas. Overall, the plots were generally representative of the forest physiognomies in the landscape in which they are located. Furthermore, the analysis supports the observed regional trends in those important forest parameters. This study demonstrates the utility of landscape scale analysis of forest physiognomies for validating and supporting the finds of plot based studies. Moreover, the more precise geolocation of many key RAINFOR plot clusters achieved during this research provides important contextual information for studies employing the RAINFOR database.

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ESTIMATION OF ABOVE GROUND BIOMASS OF THE THREE SITES (GRAZED SITE, PROTECTED SITE AND SEED SOWN SITE) FOR COMPARING THEIR PRODUCTIVITY IN KASHMIR VALLEY

FLORA AND FAUNA ◽

10.33451/florafauna.v23i2pp259-262 ◽

2017 ◽

Vol 23 (2) ◽

Author(s):

AFSHAN ANJUM BABA ◽

SYED NASEEM UL-ZAFAR GEELANI ◽

ISHRAT SALEEM ◽

MOHIT HUSAIN ◽

PERVEZ AHMAD KHAN ◽

...

Keyword(s):

Protected Areas ◽

Aboveground Biomass ◽

Plant Biomass ◽

Summer Season ◽

Above Ground Biomass ◽

Spring Season ◽

Kashmir Valley ◽

Ground Biomass ◽

Maximum Value ◽

Protected Site

The plant biomass for protected areas was maximum in summer (1221.56 g/m2) and minimum in winter (290.62 g/m2) as against grazed areas having maximum value 590.81 g/m2 in autumn and minimum 183.75 g/m2 in winter. Study revealed that at Protected site (Kanidajan) the above ground biomass ranged was from a minimum (1.11 t ha-1) in the spring season to a maximum (4.58 t ha-1) in the summer season while at Grazed site (Yousmarag), the aboveground biomass varied from a minimum (0.54 t ha-1) in the spring season to a maximum of 1.48 t ha-1 in summer seasonandat Seed sown site (Badipora), the lowest value of aboveground biomass obtained was 4.46 t ha-1 in spring while as the highest (7.98 t ha-1) was obtained in summer.

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Parasite commonality at Swamp Deer (Mammalia: Artiodactyla: Cervidae: Rucervus duvaucelii duvaucelii) and livestock interface

Journal of Threatened Taxa ◽

10.11609/jott.4812.12.3.15364-15369 ◽

2020 ◽

Vol 12 (3) ◽

pp. 15364-15369

Author(s):

Animesh Talukdar ◽

Bivash Pandav ◽

Parag Nigam

Keyword(s):

Intestinal Parasites ◽

Uttar Pradesh ◽

Anthropogenic Pressure ◽

Wildlife Sanctuary ◽

Natural Habitats ◽

Coprological Examination ◽

Parasitic Load ◽

Abundance And Diversity ◽

Swamp Deer ◽

Different Levels

Interactions between wildlife and livestock have increased over time with increased anthropogenic pressure on limited available natural habitats. These interactions have resulted in sharing of pathogens between the species resulting in impacting the wild animals’ fitness and reproduction and further influencing their abundance and diversity. The spatial overlap between Swamp Deer and livestock was studied at Jhilmil Jheel Conservation Reserve (JJCR), Uttarakhand and Kishanpur Wildlife Sanctuary (KWLS), Uttar Pradesh in India, having different levels of interaction with livestock. The prevalence, load and commonality of gastro-intestinal parasites in the species was studied through coprological examination. Parasitic ova of Strongyle sp., Trichostrongylus sp., Fasciola sp., and Moniezia sp. Amphistomes were encountered in swamp deer and livestock from both the sites. The parasitic species richness and prevalence however, varied between JJCR and KWLS. The study recorded significant differences between the parasitic load in Swamp Deer with the eggs per gram of 487.5±46.30 at JJCR and 363.64±49.97 at KWLS at varying levels of livestock interactions.

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The climatic imprint of bimodal distributions in vegetation cover for western Africa

Biogeosciences ◽

10.5194/bg-13-3343-2016 ◽

2016 ◽

Vol 13 (11) ◽

pp. 3343-3357 ◽

Cited By ~ 4

Author(s):

Zun Yin ◽

Stefan C. Dekker ◽

Bart J. J. M. van den Hurk ◽

Henk A. Dijkstra

Keyword(s):

Land Cover ◽

Congo Basin ◽

Shortwave Radiation ◽

Mean Annual Precipitation ◽

Above Ground Biomass ◽

Precipitation Regime ◽

Ground Biomass ◽

Western Africa ◽

Precipitation Regimes ◽

Woody Cover

Abstract. Observed bimodal distributions of woody cover in western Africa provide evidence that alternative ecosystem states may exist under the same precipitation regimes. In this study, we show that bimodality can also be observed in mean annual shortwave radiation and above-ground biomass, which might closely relate to woody cover due to vegetation–climate interactions. Thus we expect that use of radiation and above-ground biomass enables us to distinguish the two modes of woody cover. However, through conditional histogram analysis, we find that the bimodality of woody cover still can exist under conditions of low mean annual shortwave radiation and low above-ground biomass. It suggests that this specific condition might play a key role in critical transitions between the two modes, while under other conditions no bimodality was found. Based on a land cover map in which anthropogenic land use was removed, six climatic indicators that represent water, energy, climate seasonality and water–radiation coupling are analysed to investigate the coexistence of these indicators with specific land cover types. From this analysis we find that the mean annual precipitation is not sufficient to predict potential land cover change. Indicators of climate seasonality are strongly related to the observed land cover type. However, these indicators cannot predict a stable forest state under the observed climatic conditions, in contrast to observed forest states. A new indicator (the normalized difference of precipitation) successfully expresses the stability of the precipitation regime and can improve the prediction accuracy of forest states. Next we evaluate land cover predictions based on different combinations of climatic indicators. Regions with high potential of land cover transitions are revealed. The results suggest that the tropical forest in the Congo basin may be unstable and shows the possibility of decreasing significantly. An increase in the area covered by savanna and grass is possible, which coincides with the observed regreening of the Sahara.

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Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2020.100462 ◽

2021 ◽

Vol 21 ◽

pp. 100462

Author(s):

Sadhana Yadav ◽

Hitendra Padalia ◽

Sanjiv K. Sinha ◽

Ritika Srinet ◽

Prakash Chauhan

Keyword(s):

Random Forest ◽

Tropical Forests ◽

Biomass Estimation ◽

Above Ground Biomass ◽

Ground Biomass ◽

X Band

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Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

Environmental Sciences Proceedings ◽

10.3390/iecg2020-07916 ◽

2020 ◽

Vol 5 (1) ◽

pp. 13

Author(s):

Negar Tavasoli ◽

Hossein Arefi

Keyword(s):

Machine Learning ◽

Carbon Stock ◽

Vegetation Indices ◽

Forest Biomass ◽

Principal Component ◽

Optical Data ◽

Above Ground Biomass ◽

Ground Biomass ◽

Texture Characteristics ◽

Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

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