Estimation of daily PM10 concentrations in Italy (2006–2012) using finely resolved satellite data, land use variables and meteorology

2017 ◽  
Vol 99 ◽  
pp. 234-244 ◽  
Author(s):  
Massimo Stafoggia ◽  
Joel Schwartz ◽  
Chiara Badaloni ◽  
Tom Bellander ◽  
Ester Alessandrini ◽  
...  
Keyword(s):  
Land Use ◽  
Satellite Data ◽  
Daily Pm10

scholarly journals Evaluating land use/cover change with temporal satellite data and information systems

2012 ◽  
Vol 1 ◽  
pp. 385-389 ◽  
Author(s):  
Arzu Erener ◽  
Sebnem Düzgün ◽  
Ahmet Cevdet Yalciner
Keyword(s):  
Land Use ◽  
Information Systems ◽  
Satellite Data

scholarly journals Developing Land Use Land Cover Maps for the Lower Mekong Basin to Aid Hydrologic Modeling and Basin Planning

2018 ◽  
Vol 10 (12) ◽  
pp. 1910 ◽  
Author(s):  
Joseph Spruce ◽  
John Bolten ◽  
Raghavan Srinivasan ◽  
Venkat Lakshmi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Reference Data ◽  
Assessment Tool ◽  
Deciduous Forest ◽  
Land Use Land Cover ◽  
Modis Ndvi ◽  
Mekong Basin ◽  
Lower Mekong Basin

This paper discusses research methodology to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB) for basin planning, using both MODIS and Landsat satellite data. The 2010 MODIS MOD09 and MYD09 8-day reflectance data was processed into monthly NDVI maps with the Time Series Product Tool software package and then used to classify regionally common forest and agricultural LULC types. Dry season circa 2010 Landsat top of atmosphere reflectance mosaics were classified to map locally common LULC types. Unsupervised ISODATA clustering was used to derive most LULC classifications. MODIS and Landsat classifications were combined with GIS methods to derive final 250-m LULC maps for Sub-basins (SBs) 1–8 of the LMB. The SB 7 LULC map with 14 classes was assessed for accuracy. This assessment compared random locations for sampled types on the SB 7 LULC map to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data, and Mekong River Commission data (e.g., crop calendars). The SB 7 LULC map showed an overall agreement to reference data of ~81%. By grouping three deciduous forest classes into one, the overall agreement improved to ~87%. The project enabled updated regional LULC maps that included more detailed agriculture LULC types. LULC maps were supplied to project partners to improve use of Soil and Water Assessment Tool for modeling hydrology and water use, plus enhance LMB water and disaster management in a region vulnerable to flooding, droughts, and anthropogenic change as part of basin planning and assessment.

scholarly journals Multi-Temporal Landsat8 Satellite Data Based Crops and Other Land Use Spatial Estimation in Okara District of Pakistan

2018 ◽  
Author(s):  
Ibrar ul Hassan Akhtar ◽  
Athar Hussain ◽  
Kashif Javed ◽  
Hammad Ghazanfar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Urban Areas ◽  
Integrated Approach ◽  
Kappa Statistics ◽  
Digital Information ◽  
Land Use Land Cover ◽  
Land Utilization ◽  
Cropping Patterns

Developing countries like Pakistan is among those where lack of adoption to science and technology advancement is a major constraint for Satellite Remote Sensing use in crops and land use land cover digital information generation. Exponential rise in country population, increased food demand, limiting natural resources coupled with migration of rural community to urban areas had further led to skewed official statistics. This study is an attempt to demonstrate the possible use of freely available satellite data like Landsat8 under complex cropping system of Okara district of Punjab, Pakistan. An Integrated approach has been developed for the satellite data based crops and land use/cover spatial area estimation. The resultant quality was found above 96% with Kappa statistics of 0.95. Land utilization statistics provided detail information about cropping patterns as well as land use land cover status. Rice was recorded as most dominating crop in term of cultivation area of around 0.165 million ha followed by autumn maize 0.074 million ha, Fallow crop fields 0.067 million ha and Sorghum 0.047 million ha. Other minor crops observed were potato, fodder and cotton being cultivated on less than 0.010 million ha. Population settlements were observed over an area of around 0.081 million ha of land. 

scholarly journals Spatial heterogeneity in mangroves assessed by GeoEye-1 satellite data: a case-study in Zhanjiang Mangrove National Nature Reserve (ZMNNR), China

2013 ◽  
Vol 10 (2) ◽  
pp. 2591-2615 ◽  
Author(s):  
K. Leempoel ◽  
C. Bourgeois ◽  
J. Zhang ◽  
J. Wang ◽  
M. Chen ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Heterogeneity ◽  
Satellite Data ◽  
Nature Reserve ◽  
Species Level ◽  
Geometric Distortion ◽  
Mangrove Forests ◽  
Ground Truth Data ◽  
National Nature Reserve ◽  
High Resolution Satellite Images

Abstract. Mangrove forests, which are declining across the globe mainly because of human intervention, require an evaluation of their past and present status (e.g. areal extent, species-level distribution, etc.) to better implement conservation and management strategies. In this paper, mangrove cover dynamics at Gaoqiao (under the jurisdiction of Zhanjiang Mangrove National Nature Reserve – ZMNNR, P. R. China) were assessed through time using 1967 (Corona KH-4B), 2000 (Landsat ETM+), and 2009 (GeoEye-1) satellite imagery. An important decline in mangrove cover (−36%) was observed between 1967 and 2009 due to dike construction for agriculture (paddy) and aquaculture practices. Moreover, dike construction prevented mangroves from expanding landward. Although a small increase of mangrove area was observed between 2000 and 2009 (+24%), the ratio mangrove/aquaculture kept decreasing due to increased aquaculture at the expense of rice culture. In the land-use/cover map based on ground-truth data (5 m × 5 m plot-based tree measurements) (August–September, 2009) and spectral reflectance values (obtained from pansharpened GeoEye-1), both Bruguiera gymnorrhiza and small Aegiceras corniculatum are distinguishable at 73–100% accuracy, whereas tall A. corniculatum is identifiable at only 53% due to its mixed vegetation stands close to B. gymnorrhiza (classification accuracy: 85%). Sand proportion in the sediment showed significant differences (Kruskal-Wallis/ANOVA, P < 0.05) between the three mangrove classes (B. gymnorrhiza and small and tall A. corniculatum). Distribution of tall A. corniculatum on the convex side of creeks and small A.corniculatum on the concave side (with sand) show intriguing patterns of watercourse changes. Overall, the advantage of very high resolution satellite images like GeoEye-1 for mangrove spatial heterogeneity assessment and/or species-level discrimination is well demonstrated, along with the complexity to provide a precise classification for non-dominant species (e.g. Kandelia obovata) at Gaoqiao. Despite the limitations such as geometric distortion and single band information, the 42-yr old Corona declassified images are invaluable for land-use/cover change detections when compared to recent satellite data sets.

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