scholarly journals An estimation of the evapotranspiration of typical steppe areas using Landsat images and the METRIC model

2021 ◽  
Author(s):  
Jun Wang ◽  
Heping Li ◽  
Haiyuan Lu
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Relative Error ◽  
Landsat Images ◽  
Area Index ◽  
Land Cover Types ◽  
Vapor Flux ◽  
Typical Steppe ◽  
Silage Maize ◽  
Metric Model

Abstract Remote sensing excels in estimating regional evapotranspiration (ET). However, most remote sensing energy balance models require researchers to subjectively extract the characteristic parameters of the dry and wet limits of the underlying surfaces. The regional ET accuracy is affected by wrong determined ideal pixels. This study used Landsat images and the METRIC model to evaluate the effects of different dry and wet pixel combinations on the ET in the typical steppe areas. The ET spatiotemporal changes of the different land cover types were discussed. The results show that the surface temperature and leaf area index could determine the dry and wet limits recognition schemes in grassland areas. The water vapor flux data of an eddy covariance system verified that the relative error between the ETd,METRIC and ETd,GES of eight DOYs (day of the year) was 18.8% on average. The ETMETRIC values of the crop growth season and the ETIMS of eight silage maize irrigation monitoring stations were found to have a relative error of 11.1% on average. The spatial distribution of the ET of the different land cover types in the study area was as follows: ETwater > ETarable land > ETforest land > ETunutilized land > ETgrassland > ETurban land.

Remote sensing and GIS forest landscape assessment for sustainable forest management

2021 ◽  
Vol 17 (1) ◽  
pp. 12-26
Author(s):  
A.F. Chukwuka ◽  
A. Alo ◽  
O.J. Aigbokhan
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Forest Cover ◽  
Sustainable Forest Management ◽  
Markov Chain Model ◽  
Remote Sensing Data ◽  
Chain Model ◽  
Spatial Metrics ◽  
Forest Reserve ◽  
Land Cover Types

This study set out to assess the dynamic characteristics of the Ikere forest reserve landscape between 1985 and 2017 using remote sensing data and spatial metrics. Landscape of the study area maintained complex patterns of spatial heterogeneity over the years. Forest cover loss to other land cover types results in new large non-forest area at increasing rate. As at the year 2017, the changes in land cover types were not yet at equilibrium, thus the need to determine the future forest cover extent using a three-way markov Chain model. The decrease in number of patches of forest land (NumP) with increase in its mean patch size (MPS) shows that the forest is becoming a single unit probably due to clearing of existing patches of forest trees. The decrease in class diversity and evenness (SDI and SEI) of the general landscape over the years strengthens this assertion. The findings of this study would be very helpful to government and other stakeholders responsible for ensuring sustainable forest and general environment. Keyword: Landscape, Spatial metrics, sustainable forest and Environment

REMOTE-SENSING EXPERT CLASSIFICATION OF LAND USE/LAND COVER TYPES USING FRACTAL DIMENSIONS OVER A SUBTROPICAL HILLY REGION IN CHINA

Fractals ◽  
2011 ◽  
Vol 19 (04) ◽  
pp. 407-421
Author(s):  
JI ZHU ◽  
ZIYU LIN ◽  
XIAOZHOU LI
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Fractal Dimension ◽  
Land Cover ◽  
Fractal Dimensions ◽  
Land Use Land Cover ◽  
Surface Features ◽  
Land Cover Types ◽  
Hilly Region ◽  
Expert Classification

In the work, a simple and reliable algorithm is presented to calculate the fractal dimension of single pixel for the remote sensing images, and the fractal dimension values obtained by the algorithm proposed in this work have positive correlation with the complexity of surface features. On the basis of a scene of Landsat7 ETM+ (i.e., Enhanced Thematic Mapper Plus) data and the proposed algorithm, expert classification models and fractal technique were introduced to identify the ground objects in a Chinese subtropical hilly region, where surface features are very diverse and complex. In the work, the different land use/land cover types, especially the different vegetation categories were successfully identified using the ETM+ image, and this classification has an overall accuracy of 80.25% and a K hat of 0.7738, which are higher than those of the traditional supervised classification.

Integration of Satellite Remote Sensing Techniques and Landscape Metrics to Characterize Land Cover Change and Dynamics

2013 ◽  
pp. 228-244
Author(s):  
Carmelo Riccardo Fichera ◽  
Giuseppe Modica ◽  
Maurizio Pollino
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Landscape Metrics ◽  
Landsat Images ◽  
Aerial Photos ◽  
Multi Temporal ◽  
Urban Rural ◽  
Remote Sensing Techniques

One of the most relevant applications of Remote Sensing (RS) techniques is related to the analysis and the characterization of Land Cover (LC) and its change, very useful to efficiently undertake land planning and management policies. Here, a case study is described, conducted in the area of Avellino (Southern Italy) by means of RS in combination with GIS and landscape metrics. A multi-temporal dataset of RS imagery has been used: aerial photos (1954, 1974, 1990), Landsat images (MSS 1975, TM 1985 and 1993, ETM+ 2004), and digital orthophotos (1994 and 2006). To characterize the dynamics of changes during a fifty year period (1954-2004), the approach has integrated temporal trend analysis and landscape metrics, focusing on the urban-rural gradient. Aerial photos and satellite images have been classified to obtain maps of LC changes, for fixed intervals: 1954-1985 and 1985-2004. LC pattern and its change are linked to both natural and social processes, whose driving role has been clearly demonstrated in the case analysed. In fact, after the disastrous Irpinia earthquake (1980), the local specific zoning laws and urban plans have significantly addressed landscape changes.

scholarly journals Remote sensing and GIS application for river runoff and water quality modeling in a hilly forested watershed of Japan

2010 ◽  
Vol 13 (2) ◽  
pp. 198-216 ◽  
Author(s):  
Binaya R. Shivakoti ◽  
Shigeo Fujii ◽  
Shuhei Tanaka ◽  
Hirotaka Ihara ◽  
Masashi Moriya
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Cover ◽  
Spatial Information ◽  
Remote Sensing And Gis ◽  
Water Quality Modeling ◽  
Model Verification ◽  
Modeling Framework ◽  
Area Index ◽  
Distributed Modeling

The main objective of this study is to present a simplified distributed modeling framework based on the storage balance concept of a Tank Model and by utilizing inputs from remote sensing data and GIS analysis. The modeling process is simplified by (1) minimizing the number of parameters with unknown values and 2) retaining important characteristics (such as land cover, topography, geology) of the study area in order to account for spatial variability. Remote sensing is used as a main source of distributed data and the GIS environment is used to integrate spatial information into the model. Remote sensing is utilized mainly to derive land cover, leaf area index (Lai) and transpiration coefficient (Tc). Topographic variables such as slope, drainage direction and soil topographic index (Tindex) are derived from a digital elevation model (DEM) using GIS. The model is used to estimate evapotranspiration (Et) loss, river flow rate and selected water quality parameters (CODMn and TP). Model verification adopted a comparison of estimated results with observed data collected at different temporal scales (storm events, daily, alternate days and every 10 days). A simplified distributed modeling framework coupled with remote sensing and GIS is expected to be an alternative to complex distributed modeling processes, which required values of parameters usually unavailable at a grid scale.

scholarly journals Effects of multi-temporal high-resolution remote sensing products on simulated hydrometeorological variables in a cultivated area (southwestern France)

2017 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Aurore Brut ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Annual Precipitation ◽  
Surface Model ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes often include a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes as simulated by land surface and distributed hydrological models. Sentinel-2 mission satellite remote sensing products allow for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy flux via the ISBA-SURFEX land surface model. The study area is a 24 km by 24 km agricultural zone in southwestern France. An initial reference simulation was conducted from 2006–2010 using the ECOCLIMAP-II database. This global numerical land ecosystem database was created at a 1 km resolution and includes an ecosystem classification with a consistent set of land surface parameters required for the model, such as the Leaf Area Index (LAI) and albedo measures. The LAI of ECOCLIMAP is climatologic and derived from a 2000–2005 analysis of MODIS satellite products. This low resolution induces that several vegetation covers can be mixed in a model cell. The climatic construction of LAI dynamics also suggests that there is no interannual variability in the vegetation cycle. A second simulation was performed by forcing the same model with annual land cover maps and monthly LAI values derived from a series of 105 8 m-resolution Formosat-2 images for the same period. Both simulations were conducted at the parcel scale, i.e., a computation unit covers an area of connected pixels of the same vegetation type (a crop field, forest patch, etc.). To evaluate our simulations, we used in situ measurements of evapotranspiration and latent and sensible heat flux from two eddy covariance stations in the study area. Our results show that the use of Formosat-2 high-resolution products significantly improves simulated evapotranspiration results with respect to ECOCLIMAP-II, especially when a surface is covered with summer crops (the correlation coefficient with monthly measurements is increased by roughly 0.3 and the root mean square error is decreased by roughly 31 %). This finding is attributable to a better description of LAI evolution processes reflected by Formosat-2 data, which further modify soil water content and drainage levels of deep soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels from Formosat-2 data in comparison to reference values. In smaller proportions, runoff is also increased by roughly 1 % of annual precipitation when using Formosat-2 data. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

scholarly journals Reflectance properties of selected arctic-boreal land cover types: field measurements and their application in remote sensing

2008 ◽  
Vol 5 (2) ◽  
pp. 1069-1095 ◽  
Author(s):  
J. I. Peltoniemi ◽  
J. Suomalainen ◽  
E. Puttonen ◽  
J. Näränen ◽  
M. Rautiainen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Field Measurements ◽  
Atmospheric Effects ◽  
Optical Remote Sensing ◽  
Climatic Effects ◽  
Subarctic Region ◽  
Data Set ◽  
Climate Change Research ◽  
Land Cover Types

Abstract. We developed a mobile remote sensing measurement facility for spectral and anisotropic reflectance measurements. We measured reflection properties (BRF) of over 100 samples from most common land cover types in boreal and subarctic regions. This extensive data set serves as a unique reference opportunity for developing interpretation algorithms for remotely sensed materials as well as for modelling climatic effects in the boreal and subarctic zones. Our goniometric measurements show that the reflectances of the most common land cover types in the boreal and subarctic region can differ from each other by a factor of 100. Some types are strong forward scatterers, some backward scatterers, some reflect specularly, some have strong colours, some are bright in visual, some in infrared. We noted that spatial variations in reflectance, even among the same type of vegetation, can be well over 20%, diurnal variations of the same order and seasonal variation often over a factor of 10. This has significant consequences on the interpretation of satellite and airborne images and on the development of radiation regime models in both optical remote sensing and climate change research. We propose that the accuracy of optical remote sensing can be improved by an order of magnitude, if better physical reflectance models can be introduced. Further improvements can be reached by more optimised design of sensors and orbits/flight lines, by the effective combining of several data sources and better processing of atmospheric effects. We conclude that more extensive and systematic laboratory experiments and field measurements are needed, with more modelling effort.

scholarly journals Estimativa de Albedo e Índice de Área Foliar na Amazônia (Albedo Estimate and Leaf Area Index in Amazonia)

2011 ◽  
Vol 4 (1) ◽  
pp. 22 ◽  
Author(s):  
Ailton Marcolino Liberato
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Scientific Literature ◽  
Thematic Mapper ◽  
Amazon Forest ◽  
Geometric Correction ◽  
Landsat Images ◽  
Area Index ◽  
Landsat 5 Tm

Propôs-se, neste trabalho, estimar dados de albedo e Indice de Área Foliar (IAF) à superfície terrestre usando-se o sensor Thematic Mapper (TM) do satélite Landsat 5 e compará-lo com valores disponíveis na literatura científica. A região de estudo esta localizada no estado de Rondônia. Para a realização do estudo obtiveram-se quatro imagens orbitais do satélite Landsat 5 – TM, na órbita 231 e ponto 67, nas datas 13/07/2005, 13/05, 30/06 e 16/07 do ano de 2006, a que correspondem os dias Juliano 194, 133, 181 e 197, respectivamente. As correções geométricas para as imagens foram realizadas e geradas as cartas de albedo e IAF. O algoritmo SEBAL estimou satisfatoriamente os valores de albedo e IAF de superfícies sobre áreas de floresta (exceto para IAF) e pastagem.Palavras-chave: sensoriamento remoto, vegetacao, Floresta da Amazonia. Albedo Estimate and Leaf Area Index in Amazonia ABSTRACTThis study objectives the assessment of albedo and Leaf Area Index (LAI) data at surface using  images from Thematic Mapper (TM) sensor onboard Landsat 5 satellite, and  compare the results with values available in the scientific literature. The study area is located in the State of Rondônia. To carry out the study four orbital TM - Landsat images were obtained in the path 231 and row  67, for the dates of 07/13/2005, 06/30 and 07/16 of  2006 year, which correspond to the days 194, 181 and 197, respectively. The geometric correction for images was performed and maps of albedo and IAF were generated. The algorithm SEBAL estimated, satisfactorily, the values of albedo and IAF on the surface pasture and forest (except for LAI).Keywords: remote sensing, vegetation, Amazon Forest.

scholarly journals Monitoring Land Cover Change Using Remote Sensing and GIS Techniques: a Case Study of Al-Dalmaj Marsh, Iraq

2018 ◽  
Vol 24 (9) ◽  
pp. 96 ◽  
Author(s):  
Marwah Moojid Kadhim
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accurate Information ◽  
Past Century ◽  
Landsat Images ◽  
Gis And Remote Sensing ◽  
Vegetation Water ◽  
Set Up

Al-Dalmaj marsh and the near surrounding area is a very promising area for energy resources, tourism, agricultural and industrial activities. Over the past century, the Al-Dalmaje marsh and near surroundings area endrous from a number of changes. The current study highlights the spatial and temporal changes detection in land cover for Al-Dalmaj marsh and near surroundings area using different analyses methods the supervised maximum likelihood classification method, the Normalized  Difference Vegetation Index (NDVI), Geographic Information Systems(GIS),  and Remote Sensing (RS). Techniques spectral indices were used in this study to determine the change of wetlands and drylands area and of other land classes, through analyses Landsat images for different three years (1990, 2003, 2016). The results indicated that there was an annual increase in vegetation was from 1990 with 980.68 km2, and 1420.35km2 in 2003 to 2072.98km2 in 2016. Whereas, the annual water coverage was about 185.95km2 in 1990 then dropped to 68.27km2 in 2003, and rose to 180.23 km2 in 2016. The water coverage increasing was on the account of barren lands areas, which were significantly decreased. These collected data can be used to deliver accurate information of the values of vegetation,water, wetlands and drylands sustainability of resources which can be used to make plans to increase tourism and protected areas by using barren lands which cannot be reclaimed for agriculture, and cultivate a new renewable energy can be set up  as solar power stations.  

scholarly journals Contribution à l’étude de la vulnérabilité des terres de cultures de la région de Thiès (Sénégal) à l’aide de la télédétection et des systèmes d’information géographique (SIG)

2019 ◽  
pp. 6731-6746 ◽  
Author(s):  
Amadou SALL ◽  
Assize TOURE ◽  
Alioune KANE ◽  
Awa Niang Fall
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Temporal Dynamics ◽  
Remote Sensing And Gis ◽  
Cultivated Land ◽  
Landsat Images ◽  
Systèmes D’Information Géographique ◽  
Spatio Temporal ◽  
Systèmes D’Information

L’objectif de cette étude est d’établir à partir de la télédétection et des SIG, la dynamique spatio-temporelle des terres de cultures et d’explorer les futurs possibles de l’occupation du sol dans trois communes rurales de la région de Thiès (Fandène, Notto Diobass et Taiba Ndiaye). Une classification multidate des images landsat (1988, 2002 et 2014) a permis de quantifier les changements d’occupation des terres. Les résultats montrent que les zones de culture de Fandène sont passées entre 1988 et 2014 de 62% à 52% de la superficie totale de la commune. A l’opposée la commune de Taiba Ndiaye connait une expansion des zones de culture entre ces deux dates. Les changements enregistrés à Notto sont négligeables. Les simulations, faites sur la base des probabilités pour que la valeur d’une cellule i reste inchangée ou prenne la valeur d’une autre cellule j à l’horizon 2035, révèlent que les terres de culture de Fandène ont 69% de probabilité d’évoluer vers d’autres classes d’occupation du sol. ABSTRACT The objective of this study is to quantify from remote sensing and GIS the spatio temporal dynamics of cultivated land and explore possible futures of land use in three rural municipalities of Thies (Fandene, Notto Diobass, and Taiba Ndiaye). A multidate classification Landsat images (1988, 2002 et 2014) was used to quantify change in land cover. The results show that between 1988 and 2014 Fandene cropping areas have passed from 62% to 52% of the total area. At the opposite the commune of Taiba Ndiaye has known an expansion of cropping areas between these two dates. Minor changes are noted in Notto district. Simulations carried out on the basis of probabilities for a unit i to stay in the same cell or to be converted to another unit j in 2035, reveals that the probability for a cultivated land unit to be transformed into a another land cover category is high in Fandene (69 %).

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