Mapping of the urban tree population in gardens of Ulhasnagar, District Thane, Maharashtra using Geographic information system (GIS)

Urban trees today are a crucial component that defines the healthy and liveable environment of a city. A city’s database includes streets, building, footprints, overhead and underground utilities, workforce areas, pest/disease quarantine zones, parks, and pending development areas in addition to the tree database such as tree location, species, diameter at breast height (DBH), and canopy width. The present study aimed at mapping the tree population of some selected gardens and parks in Ulhasnagar using Geographic Information Systems (GIS). GIS is an integrated system of computer hardware, software, data and trained personnel for analyzing and displaying all forms of geographically referenced information. GIS-based map shows the location for each tree species found in the selected 12 gardens of Ulhasnagar. Green colour represents dense green canopy represented by the above-ground biomass, and yellow represents moderate, while red indicates scarce or limited above-ground biomass. The green colour actually represents the volume of biomass and not the density or the number of trees and shows the concentration of carbon pools in the study area. Updating data in GIS is much more cost-efficient and less time consuming than having to redraw maps manually. Urban foresters and urban planners can work together using GIS for better management of this resource. This study is one of the pioneering footsteps towards appreciative resources and thus enabling the researchers in developing an appropriate management strategy. The data will help us to analyze and interpret better and eventually conceptualize the above-ground biomass in the entire area of gardens.

Download Full-text

Assessing above-ground biomass of open-grown urban trees: A comparison between existing models and a volume-based approach

Urban Forestry & Urban Greening ◽

10.1016/j.ufug.2016.12.011 ◽

2017 ◽

Vol 21 ◽

pp. 239-246 ◽

Cited By ~ 8

Author(s):

Topi Tanhuanpää ◽

Ville Kankare ◽

Heikki Setälä ◽

Vesa Yli-Pelkonen ◽

Mikko Vastaranta ◽

...

Keyword(s):

Urban Trees ◽

Above Ground Biomass ◽

Ground Biomass

Download Full-text

Above ground biomass and leaf area models based on a non destructive method for urban trees of two communes in Central Chile

Bosque (Valdivia) ◽

10.4067/s0717-92002011000300010 ◽

2011 ◽

Vol 32 (3) ◽

pp. 287-296 ◽

Cited By ~ 13

Author(s):

Cynnamon Dobbs ◽

Jaime Hernández ◽

Francisco Escobedo

Keyword(s):

Leaf Area ◽

Urban Trees ◽

Above Ground Biomass ◽

Central Chile ◽

Ground Biomass ◽

Non Destructive

Download Full-text

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.

Download Full-text

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.

Download Full-text

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

Download Full-text

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.

Download Full-text