scholarly journals Spatial heterogeneity of satellite derived land surface parameters and energy flux densities for LITFASS-area

2008 ◽  
Vol 8 (4) ◽  
pp. 16219-16254 ◽  
Author(s):  
A. Tittebrand ◽  
F. H. Berger
Keyword(s):  
Land Use ◽  
Spatial Heterogeneity ◽  
Distribution Pattern ◽  
Spatial Resolution ◽  
Energy Flux ◽  
Atmospheric Correction ◽  
Distribution Functions ◽  
Target Area ◽  
Surface Parameters ◽  
Noaa Avhrr

Abstract. Remote sensing data provide area integrated information of surface properties in different spatial or temporal resolutions according to different sensor features. Landsat ETM+, Terra MODIS and NOAA-AVHRR surface temperature and spectral reflectance were used to infer further surface parameters and radiant- and energy flux densities for LITFASS-area, a 20×20 km2 heterogeneous area in Eastern Germany, mainly characterized by the land use types forest, crop, grass and water. Based on the Penman-Monteith-approach the actual latent heat flux (L.E), as key quantity of the hydrological cycle, is determined for each sensor in the accordant spatial resolution with an improved parametrization. However, using three sensors, significant discrepancies between the inferred parameters can cause flux distinctions resultant from differences of the sensor filter response functions or atmospheric correction methods. The approximation of MODIS- and AVHRR- derived surface parameters to the reference parameters of ETM (via regression lines and histogram stretching, respectively), further the use of accurate land use classifications (CORINE and a new Landsat-classification), and a consistent parametrization for the three sensors were realized to obtain a uniform base for investigations of the spatial variability. For the target area the spatial heterogeneity is analysed investigating frequency distribution functions (PDF) for surface parameters and energy fluxes. PDF is the most promising way to describe subgrid heterogeneity due to the given data in different spatial resolution. Aim of this study is to find typical distribution pattern of parameters (albedo, NDVI) for the determination of L.E determined from the highly resolved ETM data within pixel on coarser scale (MODIS, AVHRR). The analyses for 4 scenes in 2002 and 2003 showed that clear distribution-pattern for forest for NDVI and albedo are found. Grass and crop distributions show higher variability and differ significantly to each other in NDVI but only marginal in albedo. Regarding NDVI-distribution functions NDVI was found to be the key variable for L.E-determination.

scholarly journals Spatial heterogeneity of satellite derived land surface parameters and energy flux densities for LITFASS-area

2009 ◽  
Vol 9 (6) ◽  
pp. 2075-2087 ◽  
Author(s):  
A. Tittebrand ◽  
F. H. Berger
Keyword(s):  
Land Use ◽  
Spatial Resolution ◽  
Energy Flux ◽  
Satellite Data ◽  
Hydrological Cycle ◽  
Atmospheric Correction ◽  
Distribution Patterns ◽  
Distribution Functions ◽  
Surface Parameters ◽  
Noaa Avhrr

Abstract. Based on satellite data in different temporal and spatial resolution, the current use of frequency distribution functions (PDF) for surface parameters and energy fluxes is one of the most promising ways to describe subgrid heterogeneity of a landscape. Objective of this study is to find typical distribution patterns of parameters (albedo, NDVI) for the determination of the actual latent heat flux (L.E) determined from highly resolved satellite data within pixel on coarser scale. Landsat ETM+, Terra MODIS and NOAA-AVHRR surface temperature and spectral reflectance were used to infer further surface parameters and radiant- and energy flux densities for LITFASS-area, a 20×20 km2 heterogeneous area in Eastern Germany, mainly characterised by the land use types forest, crop, grass and water. Based on the Penman-Monteith-approach L.E, as key quantity of the hydrological cycle, is determined for each sensor in the accordant spatial resolution with an improved parametrisation. However, using three sensors, significant discrepancies between the inferred parameters can cause flux distinctions resultant from differences of the sensor filter response functions or atmospheric correction methods. The approximation of MODIS- and AVHRR- derived surface parameters to the reference parameters of ETM (via regression lines and histogram stretching, respectively), further the use of accurate land use classifications (CORINE and a new Landsat-classification), and a consistent parametrisation for the three sensors were realized to obtain a uniform base for investigations of the spatial variability. The analyses for 4 scenes in 2002 and 2003 showed that for forest clear distribution-patterns for NDVI and albedo are found. Grass and crop distributions show higher variability and differ significantly to each other in NDVI but only marginal in albedo. Regarding NDVI-distribution functions NDVI was found to be the key variable for L.E-determination.

scholarly journals Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1737 ◽  
Author(s):  
Jinchao Song ◽  
Tao Lin ◽  
Xinhu Li ◽  
Alexander V. Prishchepov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Images ◽  
Remote Sensing Imagery ◽  
Points Of Interest ◽  
Very High Spatial Resolution ◽  
Functional Zones ◽  
Very High

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

scholarly journals Exploring Urban Spatial Feature with Dasymetric Mapping Based on Mobile Phone Data and LUR-2SFCAe Method

2018 ◽  
Vol 10 (7) ◽  
pp. 2432 ◽  
Author(s):  
Lingbo Liu ◽  
Zhenghong Peng ◽  
Hao Wu ◽  
Hongzan Jiao ◽  
Yang Yu
Keyword(s):  
Land Use ◽  
Spatial Heterogeneity ◽  
Mobile Phone ◽  
Census Data ◽  
Base Station ◽  
Distance Decay ◽  
Base Stations ◽  
Mobile Phone Data ◽  
Dasymetric Mapping ◽  
Spatial Feature

Dasymetric mapping of high-resolution population facilitates the exploration of urban spatial feature. While most relevant studies are still challenged by weak spatial heterogeneity of ancillary data and quality of traditional census data, usually outdated, costly and inaccurate, this paper focuses on mobile phone data, which can be real-time and precise, and also strengthens spatial heterogeneity by its massive mobile phone base stations. However, user population recorded by mobile phone base stations have no fixed spatial boundary, and base stations often disperse in extremely uneven spatial distribution, this study defines a distance-decay supply–demand relation between mobile phone user population of gridded base station and its surrounding land patches, and outlines a dasymetric mapping method integrating two-step floating catchment area method (2SFCAe) and land use regression (LUR). The results indicate that LUR-2SFCAe method shows a high fitness of regression, provides population mapping at a finer scale and helps identify urban centrality and employment subcenters with detailed worktime and non-worktime populations. The work involving studies of dasymetric mapping based on LUR-2SFCAe method and mobile phone data proves to be encouraging, sheds light on the relationship between mobile phone users and nearby land use, brings about an integrated exploration of 2SFCAe in LUR with distance-decay effect and enhances spatial heterogeneity.

Variation in small-scale spatial heterogeneity of soil properties and vegetation with different land use in semiarid grassland ecosystem

2008 ◽  
Vol 310 (1-2) ◽  
pp. 103-112 ◽  
Author(s):  
Zhiyong Zhou ◽  
Osbert Jianxin Sun ◽  
Zhongkui Luo ◽  
Hongmei Jin ◽  
Quansheng Chen ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Heterogeneity ◽  
Soil Properties ◽  
Small Scale ◽  
Grassland Ecosystem ◽  
Semiarid Grassland

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.

scholarly journals Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach

2015 ◽  
Vol 6 (1) ◽  
pp. 61-81 ◽  
Author(s):  
L. Gerlitz ◽  
O. Conrad ◽  
J. Böhner
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
Large Scale ◽  
Local Scale ◽  
Predictor Variables ◽  
High Mountain ◽  
Target Area ◽  
Monthly Precipitation ◽  
Highly Nonlinear

Abstract. The heterogeneity of precipitation rates in high-mountain regions is not sufficiently captured by state-of-the-art climate reanalysis products due to their limited spatial resolution. Thus there exists a large gap between the available data sets and the demands of climate impact studies. The presented approach aims to generate spatially high resolution precipitation fields for a target area in central Asia, covering the Tibetan Plateau and the adjacent mountain ranges and lowlands. Based on the assumption that observed local-scale precipitation amounts are triggered by varying large-scale atmospheric situations and modified by local-scale topographic characteristics, the statistical downscaling approach estimates local-scale precipitation rates as a function of large-scale atmospheric conditions, derived from the ERA-Interim reanalysis and high-resolution terrain parameters. Since the relationships of the predictor variables with local-scale observations are rather unknown and highly nonlinear, an artificial neural network (ANN) was utilized for the development of adequate transfer functions. Different ANN architectures were evaluated with regard to their predictive performance. The final downscaling model was used for the cellwise estimation of monthly precipitation sums, the number of rainy days and the maximum daily precipitation amount with a spatial resolution of 1 km2. The model was found to sufficiently capture the temporal and spatial variations in precipitation rates in the highly structured target area and allows for a detailed analysis of the precipitation distribution. A concluding sensitivity analysis of the ANN model reveals the effect of the atmospheric and topographic predictor variables on the precipitation estimations in the climatically diverse subregions.

scholarly journals Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir

2019 ◽  
Vol 11 (3) ◽  
Author(s):  
Jefferson Francisco Soares ◽  
Gláucia Miranda Ramirez ◽  
Mirléia Aparecida de Carvalho ◽  
Marcelo de Carvalho Alves ◽  
Christiany Mattioli Sarmiento ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Resolution ◽  
Nearest Neighbor ◽  
High Spatial Resolution ◽  
Object Oriented ◽  
Visual Object ◽  
Hydroelectric Power ◽  
K Nearest Neighbor ◽  
High Spatial Resolution Image ◽  
Resolution Image

The maintenance of riparian forests is considered one of the main vegetative practices for mitigating the degradation of water resources and is mandatory by law. However, in Brazil there is still a progressive and constant decharacterization of these areas. Facing this reality, it is necessary to broaden researches that identify the occurring changes and provide efficient solutions at a fast pace and low cost. Remote sensing techniques show great application potential in characterizing natural resources. The objective of this work was to map, to characterize the land use and occupation and to verify the best method of high spatial resolution image classification of the Permanent Preservation Areas of the Funil Hydroelectric Power Plant reservoir, located between the municipalities of Lavras, Perdões, Bom Sucesso, Ibituruna, Ijací and Itumirim, in the state of Minas Gerais. The methods used to classify the high spatial resolution image from the Quickbird satellite were visual, object-oriented and pixel-by-pixel. Results showed the best method for mapping land use and occupation of the study area was object-oriented classification using the K-nearest neighbor algorithm, with kappa coefficient of 0.88 and global accuracy of 91.40%.

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