It is so important to be aware of quantitative and qualitative characteristics of changes for the environment, land use planning and sustainable development. Detecting the changes in the condition of an issue is done by time difference... more
It is so important to be aware of quantitative and qualitative characteristics of changes for the environment, land use planning and sustainable development. Detecting the changes in the condition of an issue is done by time difference observations. Change detection refers to the process of detecting time changes of an object through different time observations. The detection of changes by satellite imageries has come recently to the focus of attention due to their comprehensive and integrated characteristics and their ability to monitor changes during long periods as well as their application to monitoring and controlling changes in the forest ecosystems. The vegetation maps are now used to generate required information for macro and micro planning. This study was done to monitor the changes in the forest of Golestan province in the past and also to investigate the possibility of its future forecast using the Land Change Modeler (LMC). A forest type map was first prepared to monitor changes in forests of Golestan province from 2000 to 2015. The images taken in 12 months during 2000 and 2015 were collected form MODIS satellite imageries to monitor the forest. After pre-processing and preparing the time series in two sections, the forest changes were considered using the Normalized Difference Vegetation Index and Moisture Stress Index (NDVI and MSI). The changes were then classified by indices, including the lands with excellent, very good, medium and poor coverage and compared together. The accuracy of the classification results was assessed using the field maps. The best result was found in the average time series data and the use of the NDVI index was prioritized over other indices. The forecast of changes by 2030 as 0.8629 Kappa and the model results for 2030 indicated a decline of 70000 in the land with high and very high capacities and a further increase in land with a low capacity.
Detailed analysis of Land Use/Land Cover (LULC) using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed... more
Detailed analysis of Land Use/Land Cover (LULC) using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m x 250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three different techniques: error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coefficients (K). The average overall accuracies for rabi and kharif are 82.83% and 78.21%, respectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation between cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.
Flooding is a major problem facing Southern African region. The region has been experiencing flood for the past two decades. This flood event has been exacerbated in recent years by global weather pattern known as La Niña which cools... more
Flooding is a major problem facing Southern African region. The region has been experiencing flood for the past two decades. This flood event has been exacerbated in recent years by global weather pattern known as La Niña which cools ocean waters in the equatorial Pacific and changes rainfall patterns across the world. This change in weather pattern has resulted in increased rainfall over Southern Africa causing flash floods resulting in extensive socioeconomic loses, casualties and environmental damage. This study employs remote sensing and geographical information systems (GIS) data to visualize the impact of climate change caused by flooding in the Southern African region in order to assist decision makers' plans for future occurrences. To achieve these objectives, the study used Digital Elevation Model (DEM), temporal Landsat Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites data obtained from the United States Geological Survey (USGS) and NASA's Earth Observatory web-sites in order to show the spatial dimensions of the damage and the flooded area. Results of the study revealed notable damages to social and natural environments as well as flood risk zones and watercourses in the study area. The paper concludes by outlining policy recommendations in the form of the need for building drainage ditches on the flat plains identified in this study to accommodate flood flows, the design of a comprehensive Regional Emergency Information System (REIS) with support from the governments in the study area and the neighboring countries. Building such system, the paper con
This paper presents a paddy growth stages classification using MODIS remote sensing images with support vector machines (SVMs). We collected the paddy growth stages data samples from a series of MODIS mages acquired from March to July... more
This paper presents a paddy growth stages classification using MODIS remote sensing images with support vector machines (SVMs). We collected the paddy growth stages data samples from a series of MODIS mages acquired from March to July 2012 along paddy field area only. The data are collected based on growth stages phenology of paddy using spectral profile which consists of at least 9 classes for growth stages and 2 classes for dominated soil and cloud. We apply SVMs to build a binary classifier for each class with one against all strategy of multiclass approach. One important issue needed to address is unbalanced prior probability that should be solved by each SVM. In this study, we evaluate the effectiveness of balanced branches strategy that is applied to one against all SVMs learning. Our result shows that the balanced branches strategy does improves in average around 10% classification accuracy during training and validation, and in average around 50% during testing. Index Terms-Remote sensing, MODIS images, classification, support vector machines (SVMs).
Remote sensing technology plays an important role in agriculture applications, especially for paddy growth stages classification, which is a critical process in predicting crop production. The analysis of multi bands data covering very... more
Remote sensing technology plays an important role
in agriculture applications, especially for paddy growth stages classification, which is a critical process in predicting crop production. The analysis of multi bands data covering very large swath areas using iterative methods such as neural network or SVM will certainly cost much computation time. This paper addresses this problem by taking advantage from a non-iterative tuning capability of Extreme Learning Machine (ELM) for paddy growth stages classification using MODIS (Moderate Resolution Imaging Spectroradiometer) remote sensing images. The accuracy of classification is measured by Cohen’s kappa. Seven classes are used in classification, with consist of six classes for paddy growth stages and one class for dominated cloud. The contribution of this study is a new ensemble incremental approaches based on random bootstrap resampling for basic ELM (B-ELM) and Error Minimized ELM (EM-ELM) are applied to build multi-class classifier using two types of hidden nodes function, i.e. additive and radial basis function (RBF) hidden nodes. The classification results were compared each other with these two types of hidden nodes. Our ensemble incremental approach successfully classify seven paddy growth stages and significantly improve the overall kappa coefficient to 10.2% higher with only in average 7 nodes addition overhead.
ABSTRACT The use of leaf area index (LAI) is essential in ecosystem and agronomic studies since it measures energy and gas exchanges between vegetation and atmosphere. In the last decades, LAI values have widely been estimated from... more
ABSTRACT The use of leaf area index (LAI) is essential in ecosystem and agronomic studies since it measures energy and gas exchanges between vegetation and atmosphere. In the last decades, LAI values have widely been estimated from passive remotely sensed data although estimated results were often affected by noise and measurement uncertainties. In this article, we propose a Kalman filter algorithm in order to estimate the time evolution of LAI by combining field-measured and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. The scalar equation of the dynamic LAI model (state transition model) was derived by the field-measured data while the MODIS red, near-infrared and shortwave infrared reflectance data were used to implement the observation model. The reflectance data were linked to LAI by using the reduced simple ratio. The method was tested in an experimental field located in the north-western part of the Apulia region (Italy). The results showed a good agreement between the LAI estimated through the algorithm and the LAI derived from field data, with a coefficient of determination (R2) of 0.96 and a corresponding root mean square error of 0.124.
Flooding is a major problem facing Southern African region. The region has been experiencing flood for the past two decades. This flood event has been exacerbated in recent years by global weather pattern known as La Niña which cools... more
Flooding is a major problem facing Southern African region. The region has been experiencing flood for the past two decades. This flood event has been exacerbated in recent years by global weather pattern known as La Niña which cools ocean waters in the equatorial Pacific and changes rainfall patterns across the world. This change in weather pattern has resulted in increased rainfall over Southern Africa causing flash floods resulting in extensive socioeconomic loses, casualties and environmental damage. This study employs remote sensing and geographical information systems (GIS) data to visualize the impact of climate change caused by flooding in the Southern African region in order to assist decision makers' plans for future occurrences. To achieve these objectives, the study used Digital Elevation Model (DEM), temporal Landsat Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites data obtained from the United States Geologic...
Based on 12 years (2000–2011) of Moderate Resolution Imaging Spectroradiometer (MODIS) true-colour images, statistical characteristics of internal solitary waves (ISWs) in the southern Taiwan Strait were studied. Two types of ISWs with a... more
Based on 12 years (2000–2011) of Moderate Resolution Imaging Spectroradiometer (MODIS) true-colour images, statistical characteristics of internal solitary waves (ISWs) in the southern Taiwan Strait were studied. Two types of ISWs with a distinct scale of wave crest length and geographic distributions were identified: Type-I waves have larger wave crest lengths and span a large area from the southern Taiwan Strait to the northern South China Sea, while Type-II waves have smaller wave crest lengths and appear only at the southeastern corner of the Taiwan Strait. Further analyses based on an empirical model of ISW propagation and on the calculations of the depth-integrated internal tide-generating body force suggested that Type-I waves mainly originate from the Luzon Strait, while Type-II waves are locally generated at the shelf break in the southeastern corner of the Taiwan Strait.
