Geohydrological evaluation of upper agniar and vellar basins, tamil nadu: An integrated approach using remote sensing, geophysical and well inventory data

1996 ◽  
Vol 24 (3) ◽  
pp. 153-168 ◽  
Author(s):  
S Palanivel ◽  
A Ganesh ◽  
T Vasantha Kumaran
Keyword(s):  
Remote Sensing ◽  
Tamil Nadu ◽  
Integrated Approach ◽  
Inventory Data

scholarly journals Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing

2021 ◽  
Vol 13 (8) ◽  
pp. 1592
Author(s):  
Nikolai Knapp ◽  
Andreas Huth ◽  
Rico Fischer
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Canopy Height ◽  
Biomass Estimation ◽  
Inventory Data ◽  
Border Effects ◽  
Tree Crowns ◽  
Forest Inventory Data ◽  
Estimation Uncertainty ◽  
Voxel Space

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.

scholarly journals High-resolution forest carbon flux mapping and monitoring in the Pacific Northwest with time since disturbance and disturbance legacies inferred from remote sensing and inventory data

2016 ◽  
Author(s):  
Huan Gu ◽  
Christopher A. Williams ◽  
Bardan Ghimire ◽  
Feng Zhao ◽  
Chengquan Huang
Keyword(s):  
Remote Sensing ◽  
Pacific Northwest ◽  
Forest Inventory ◽  
Stand Age ◽  
Net Ecosystem Productivity ◽  
Fine Scale ◽  
Ecosystem Productivity ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
The Pacific

Abstract. Assessment of forest carbon storage and uptake is central to understanding the role forests play in the global carbon cycle and policy-making aimed at mitigating climate change. Current U.S. carbon stocks and fluxes are monitored and reported at fine-scale regionally, or coarse-scale nationally. We proposed a new methodology of quantifying carbon uptake and release across forested landscapes in the Pacific Northwest (PNW) at a fine scale (30 m) by combining remote-sensing based disturbance year, disturbance type, and aboveground biomass with forest inventory data in a carbon modelling framework. Time since disturbance is a key intermediate determinant that aided the assessment of disturbance-driven carbon emissions and removals legacies. When a recent disturbance was detected, time since disturbance can be directly determined by remote sensing-derived disturbance products; and if not, time since last stand-clearing was inferred from remote sensing-derived 30 m biomass map and field inventory-derived species-specific biomass regrowth curves. Net ecosystem productivity (NEP) was further mapped based on carbon stock and flux trajectories that described how NEP changes with time following harvest, fire, or bark beetle disturbances of varying severity. Uncertainties from biomass map and forest inventory data were propagated by probabilistic sampling to provide a probabilistic, statistical distribution of stand age and NEP for each forest pixel. We mapped mean, standard deviation and statistical distribution of stand age and NEP at 30 m in the PNW region. Our map indicated a net ecosystem productivity of 5.2 Tg C y−1 for forestlands circa 2010 in the study area, with net uptake in relatively mature (> 24 year old) forests (13.6 Tg C y−1) overwhelming net negative NEP from tracts that have seen recent harvest (−6.4 Tg C y−1), fires (−0.5 Tg C y−1), and bark beetle outbreaks (−1.4 Tg C y−1). The approach will be applied to forestlands in other regions of the conterminous U.S. to advance a more comprehensive monitoring, mapping and reporting the carbon consequences of forest change across the U.S.

Estimation of the North–South Transect of Eastern China forest biomass using remote sensing and forest inventory data

2013 ◽  
Vol 34 (15) ◽  
pp. 5598-5610 ◽  
Author(s):  
Yanhua Gao ◽  
Xinxin Liu ◽  
Chengcheng Min ◽  
Honglin He ◽  
Guirui Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Eastern China ◽  
Forest Biomass ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
The North

Application of remote sensing and GIS for delineating groundwater recharge potential zones of Kovilpatti Municipality, Tamil Nadu using IF technique

2015 ◽  
Vol 9 (2) ◽  
pp. 137-150 ◽  
Author(s):  
S. Selvam ◽  
Farooq A. Dar ◽  
N. S. Magesh ◽  
C. Singaraja ◽  
S. Venkatramanan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Groundwater Recharge ◽  
Tamil Nadu ◽  
Remote Sensing And Gis ◽  
Recharge Potential ◽  
If Technique

Remote sensing and vegetation mapping in South Africa

Bothalia ◽  
1983 ◽  
Vol 14 (2) ◽  
pp. 271-282 ◽  
Author(s):  
M. L. Jarman ◽  
N. G. Jarman ◽  
D. Edwards
Keyword(s):  
Remote Sensing ◽  
South African ◽  
National Level ◽  
Digital Processing ◽  
Integrated Approach ◽  
Vegetation Mapping ◽  
Digital Analysis ◽  
Careful Planning ◽  
Full Investigation ◽  
Field Exercise

The kinds of imagery, types of data and general relationships between scale of study, scale of mapping and scale of remote sensing products that are appropriate to the South African situation for visual and digital analysis are presented. The type of remote sensing product and processing, the type of field exercise appropriate to each, and the purpose of producing maps at each scale are discussed. Lack of repetitive imagery to date has not allowed for the full investigation of monitoring potential and careful planning at national level is needed to ensure availability of imagery for monitoring purposes. Map production processes which are rapid and accurate should be utilized. An integrated approach to vegetation mapping and surveying, which incorporates the best features of both visual and digital processing, is recommended for use.

Remote Sensing and GIS Based Groundwater Potential Zone Mapping in Ariyalur District, Tamil Nadu

2018 ◽  
Vol 92 (4) ◽  
pp. 484-490 ◽  
Author(s):  
G. Gnanachandrasamy ◽  
Yongzhang Zhou ◽  
M. Bagyaraj ◽  
S. Venkatramanan ◽  
T. Ramkumar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tamil Nadu ◽  
Groundwater Potential ◽  
Remote Sensing And Gis ◽  
Groundwater Potential Zone ◽  
Potential Zone

EFFECT OF INDIAN OCEAN CYCLONE ON COASTAL REGION USING REMOTE SENSING AND GIS

2020 ◽  
pp. 2050005
Author(s):  
KHUSHBOO KUMARI ◽  
ASMITA A. DEO
Keyword(s):  
Remote Sensing ◽  
Tamil Nadu ◽  
Andhra Pradesh ◽  
Vegetation Index ◽  
Coastal Region ◽  
Risk Zone ◽  
Sparse Vegetation ◽  
Dense Vegetation ◽  
Risk Zonation ◽  
Before And After

The effect of four different cyclones making land fall on four different coastal regions is studied viz., Nisha (2008, Tamil Nadu), Laila (2010, Andhra Pradesh), Sidr (2007, Bangladesh) and land depression BOB 03 (2008, Orissa). Remote sensing and Geographic Information System (GIS) technique are used to detect change in Land use and Land cover (LU/LC). Change in vegetation cover by Normalized Vegetation Index (NDVI) is also investigated. Further, preparation of slope map, processing of buffer zoning map is exercised. These parameters are analyzed to find the impression of cyclones after hitting the coastal boundaries by considering the images before and after the cyclone has passed. Change detection assessment of LU/LC features provides information for monitoring the trend of change in an area. In almost every considered region, it is found that dense vegetation is changed to sparse vegetation. Also, decrease in the irrigated cropland due to heavy rainfall caused by cyclone is noted. Risk zone is created by buffer ring of cyclone track to spot the area under risk zone. The area calculation suggests the effect of cyclone at the distance of 20–50[Formula: see text]km from the cyclone path which is validated from the slope effect on LU/LC, also. Some of the common features such as dense vegetation, show decrease in the area by 71%, 17%, 67% and 60%, or settlement area also shows decrease by 38%, 15%, 57% and 17% due to Laila, BOB 03, Nisha and Sidr cyclones, respectively. Increase in shrubland mix with rangeland by 18%, 113% and 98% is also seen due to Laila, Nisha and Sidr cyclones. Other LU/LC shows changes such as, water bodies increasing by 6%, 189% due to BOB 03 and Nisha cyclones. Changes are also seen in sparsed vegetation, which is decreased in Orissa and Tamil Nadu and increased in Andhra Pradesh and Bangladesh. It is demonstrated that by preparing risk zonation map, risk assessment can be done.

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