scholarly journals The use of remote sensing tools for accurate charcoal kilns’ inventory and distribution analysis: Comparative assessment and prospective

2021 ◽  
Vol 105 ◽  
pp. 102641
Author(s):  
Cláudia Oliveira ◽  
Stéphanie Aravecchia ◽  
Cédric Pradalier ◽  
Vincent Robin ◽  
Simon Devin
Keyword(s):  
Remote Sensing ◽  
Comparative Assessment ◽  
Distribution Analysis ◽  
Charcoal Kilns

scholarly journals INTEGRATION OF GIS AND REMOTE SENSING FOR HOTSPOT DISTRIBUTION ANALYSIS IN BERBAK SEMBILANG NATIONAL PARK

2019 ◽  
Vol 16 (1) ◽  
pp. 99
Author(s):  
Andita Minda Mora ◽  
Bambang Hero Saharjo ◽  
Lilik Budi Prasetyo
Keyword(s):  
Remote Sensing ◽  
National Park ◽  
Forest Cover ◽  
Soil Science ◽  
Distribution Analysis ◽  
Gis And Remote Sensing ◽  
Fire Monitoring ◽  
The Right ◽  
Mixed Fields ◽  
Time Acquisition

Abstract. Remote sensing is composed of many interrelated processes to be able to consider physical objects such as buildings, land, and plants which are objects that can be discussed by applications discussed in various disciplines that discuss geology, forestry, soil science, and geography. The use of GIS and remote sensing for fire monitoring has been widely used. However, this study is the first study conducted in the TNBS area after the Berbak National Park (TNB) in Jambi to join the Sembilang National Park (TNS) in South Sumatra. Hotspot distribution in this study was obtained using Getis-Ord-Gi * statistics, hotspot data collected from 2000-2018 in the TNBS area. The results of the hotspot distribution during the 2000-2018 recorded by MODIS satellites with time acquisition and statistical analysis using Gi* show the results that the hotspots gathered (80% confidence level) outside the TNBS area, which is a mixed fields area. Further studies on causes of fire in terms of socio-economic and cultural needs to be done to get the right advice in reducing the risk of loss of forest cover and diversity in TNBS. Keywords: mitigation, hydrology, DAS

scholarly journals Statistical Data Collection Methodologies of Irrigated Areas and Their Limitations: A Review

2019 ◽  
Author(s):  
Ali Ajaz ◽  
Poolad Karimi ◽  
Xueliang Cai ◽  
Charlotte De Fraiture ◽  
Muhammad Saleem Akhter
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Density Distribution ◽  
National Level ◽  
Water Security ◽  
Distribution Analysis ◽  
Spatial Spread ◽  
Country Level ◽  
Irrigation Density ◽  
Supplementary Irrigation

Inconsistencies in the statistical datasets of irrigated areas at the national level could have considerable implications for policies developed for food and water security. Remote sensing can address this issue, however, dubieties of its reliability inhibit its protagonist role. Methods that integrate both remote sensing based and statistical datasets seem expedient, and they are more likely to be acknowledged by the policymakers. Therefore, it is important for scientists to know the basis and limitations of statistical datasets which originate at the country level. Data collection methodologies of irrigated areas were reviewed for seven Asian countries, namely China, India, Pakistan, Bangladesh, Nepal, Indonesia, and Thailand. Factors causing the uncertainties in data, and the limitations of data collection methodologies were highlighted. Also, an irrigation density distribution analysis was conducted to understand the relation of spatial spread pattern of irrigated areas and uncertainty in their statistical records. It was found that irrigated areas statistics are mostly based on the information originating from water user associations and farmers, which is either self-reported or it is collected through interviews during surveys and censuses. The main causes of discrepancy were lack of resources to frequently enumerate the irrigated land, inconsistency in the data collection methodologies, unaccounted secondary crops, illegal and unregulated water use, and bureaucratic and political constraints. Irrigation density distribution analysis showed that the largely scattered irrigated areas might be prone to lack of comprehensive and frequent enumeration. Furthermore, dense irrigation regions might have potentially unrecorded irrigated areas where temporary or supplementary irrigation arrangements are made by the marginal farmers.

scholarly journals Composition and distribution of vegetation in the water level fluctuating zone of the Lantsang cascade reservoir system using UAV multispectral imagery

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247682
Author(s):  
Weiwei Jiang ◽  
Lun Liu ◽  
Henglin Xiao ◽  
Song Zhu ◽  
Wentao Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
River Basin ◽  
Dominant Species ◽  
Cynodon Dactylon ◽  
Vegetation Coverage ◽  
Distribution Analysis ◽  
Ageratum Conyzoides ◽  
The Sustainable Development ◽  
Large Water

With the development of a large number of tall dams and large cascade reservoir projects in the Lantsang River Basin, a large water level fluctuating zone (WLFZ) containing cascading reservoirs has formed. This newborn ecosystem is related to the sustainable development of hydropower projects, and has become a new problem to be studied urgently. Taking WLFZs in the Huangdeng, Xiaowan and Nuozhadu Reservoirs in the Lantsang River Basin as study areas, this study used multi-spectral remote-sensing field data obtained with unmanned aerial vehicles (UAVs) to ascertain the species types, coverage, distribution characteristics, dominant species and pioneer species of naturally restored vegetation. The considered data were subjected to UAV data processing, vegetation classification using multi-spectral images and a geographic information system (GIS) terrain-distribution analysis. Results show that: Polygonum Plebeium, Cynodon dactylon, Xanthium sibiricum, Ageratum conyzoides, Eleusine indica, Digitaria sanguinalis and Verbena officinalis are the dominant species of vegetation that could be naturally restored in the WLFZ; the vegetation coverage and the number of species are significantly positively correlated with the age and restoration periods of the WLFZ; the vegetation coverage of each study area increases at first, and then decreases, as a function of elevation; gentle slopes about 0–25°are more suitable for vegetation restoration. This study provides first-hand data on the natural restoration of vegetation in WLFZs, and gives a useful reference for its ecological restoration as a consequence of hydropower cascade development in the Lantsang River Basin. Finally, the study demonstrates that light UAV remote sensing is an attractive choice for investigating vegetation in reservoir WLFZs.

scholarly journals NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology

2020 ◽  
Vol 12 (17) ◽  
pp. 2821
Author(s):  
Zhong Liu ◽  
Chung-Lin Shie ◽  
Angela Li ◽  
David Meyer
Keyword(s):  
Earth Sciences ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Earth Science ◽  
Data Access ◽  
Tropical Meteorology ◽  
Distribution Analysis ◽  
Model Data ◽  
Science Data ◽  
Data Products

Satellite remote sensing and model data play an important role in research and applications of tropical meteorology and climatology over vast, data-sparse oceans and remote continents. Since the first weather satellite was launched by NASA in 1960, a large collection of NASA’s Earth science data is freely available to the research and application communities around the world, significantly improving our overall understanding of the Earth system and environment. Established in the mid-1980s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), located in Maryland, USA, is a data archive center for multidisciplinary, satellite and model assimilation data products. As one of the 12 NASA data centers in Earth sciences, GES DISC hosts several important NASA satellite missions for tropical meteorology and climatology such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) Mission and the Modern-Era Retrospective analysis for Research and Applications (MERRA). Over the years, GES DISC has developed data services to facilitate data discovery, access, distribution, analysis and visualization, including Giovanni, an online analysis and visualization tool without the need to download data and software. Despite many efforts for improving data access, a significant number of challenges remain, such as finding datasets and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. In this article, we list and describe major NASA satellite remote sensing and model datasets and services for tropical meteorology and climatology along with examples of using the data and services, so this may help users better utilize the information in their research and applications.

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