Application of a Multispectral SPOT Image for Land Use Classification in Sampean Watershed

This article described the process of land use classification at Sampean Watershed. The research was conducted in Sampean watershed to calculate the land use area using a multispectral SPOT image. Two SPOT image scenes were used to identify and classify the main nomenclature of land use. The research applied level 2A of SPOT image raw data which were obtained during 2004. Research methodology consisted of geometric correction of Image; image enhancement using high sharpen filter; un-supervised classification and supervised classification. The classification algorithm used the maximum likelihood in which pixels was classified based on their spectral signature. Severaltraining areas were identified to define the region area. Supervised classification could classified 9 class of land uses, the classification of land use consist of irrigated paddy field (56.05%), rain fed paddy field (0.89%), forest (10.75%), urban area (8.69%), plantation (4.22%), barren land (11.19%), river (0.05%), cropland (7.98%), and bushes (0.19%). The overall classification accuracy was 84.21%. This work will be useful for hydrological modelling and management planning of the Watershed

Application of a Multispectral SPOT Image for Land Use Classification in Sampean Watershed

This article described the process of land use classification at Sampean Watershed. The research was conducted in Sampean watershed to calculate the land use area using a multispectral SPOT image. Two SPOT image scenes were used to identify and classify the main nomenclature of land use. The research applied level 2A of SPOT image raw data which were obtained during 2004. Research methodology consisted of geometric correction of Image; image enhancement using high sharpen filter; un-supervised classification and supervised classification. The classification algorithm used the maximum likelihood in which pixels was classified based on their spectral signature. Severaltraining areas were identified to define the region area. Supervised classification could classified 9 class of land uses, the classification of land use consist of irrigated paddy field (56.05%), rain fed paddy field (0.89%), forest (10.75%), urban area (8.69%), plantation (4.22%), barren land (11.19%), river (0.05%), cropland (7.98%), and bushes (0.19%). The overall classification accuracy was 84.21%. This work will be useful for hydrological modelling and management planning of the Watershed

A comparative assessment between object and pixel-based classification approaches for land-use/land-cover mapping using Spot 5 imagery

Land use/land cover (LULC) classification with high accuracy is necessary, especially in eco-environment research, urban planning, vegetation condition study and soil management. Over the last decade a number of classification algorithms have been developed for the analysis of remotely sensed data. The most notable algorithms are the object-oriented K-Nearest Neighbour (K-NN), Support Vector Machines (SVMs) and the Decision Trees (DTs) amongst many others. In this study, LULC types of Selangor area were analyzed on the basis of the classification results acquired using the pixel-based and object-based image analysis approaches. SPOT 5 satellite images with four spectral bands from 2003 and 2010 were used to carry out the image classification and ground truth data were collected from Google Earth and field trips. In pixel-based image analysis, a supervised classification was performed using the DT classifier. On the other hand, object-oriented (K-NN) image analysis was evaluated using standard nearest neighbour as classifier. Subsequently SVM object-based classification was performed. Five LULC categories were extracted and the results were compared between them. The overall classification accuracies for 2003 and 2010 showed that the object-oriented (K-NN) (90.5% and 91%) performed better results than the pixel-based DT (68.6% and 68.4%) and object-based SVM (80.6% and 78.15%). In general, the object-oriented (K-NN) performed better than both DTs and SVMs. The obtained LULC classification maps can be used to improve various applications such as change detection, urban design, environmental management and zooning

A Neural Network Method for Land Use Change Classification, with Application to the Nile River Delta

Detecting and monitoring changes in conditions at the Earth's surface are essential for understanding human impact on the environment and for assessing the sustainability of development. In the next decade, NASA will gather high-resolution multi-spectral and multi-temporal data, which could be used for analyzing long-term changes, provided that available methods can keep pace with the accelerating flow of information. This paper introduces an automated technique for change identification, based on the ARTMAP neural network. This system overcomes some of the limitations of traditional change detection methods, and also produces a measure of confidence in classification accuracy. Landsat thematic mapper (TM) imagery of the Nile River delta provides a testbed for land use change classification methods. This dataset consists of a sequence of ten images acquired between 1984 and 1993 at various times of year. Field observations and photo interpretations have identified 358 sites as belonging to eight classes, three of which represent changes in land use over the ten-year period. Aparticular challenge posed by this database is the unequal representation of various land use categories: three classes, urban, agriculture in delta, and other, comprise 95% of pixels in labeled sites. A two-step sampling method enables unbiased training of the neural network system across sites.National Science Foundation (SBR 95-13889); Office of Naval Research (N00014-95-1-409, N00014-95-0657); Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-042

Comparison of Supervised Image Classification Algorithms: Classifying Diverse Land Cover in California

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe research field of machine learning and supervised image classification is quickly developing. There are many studies regarding the different use cases of image classification. However, a comprehensive study on the primary algorithms in ArcGIS Pro has not been assessed for numerous classes. This study attempts to bridge that gap by evaluating the effectiveness of the three primary classification algorithms available in ArcGIS Pro, and to determine an optimal algorithm for the given study area. This scope covers 12 classes of land cover in San Joaquin County, California. Maximum Likelihood, Random Forest, and Support Vector Machine were tested based on their general usability in image classification as well as their proven characteristics through research. The training and ground truth validation data were provided by USGS, in the form of a Landsat 8 image, and crop planning map. The accuracy assessment was performed with a stratified random sampling strategy. Based on the Kappa statistic, this study determines Random Forest (Kappa = 0.68, Accuracy = 0.76) to be the most suitable algorithm for detecting a series of crop types, bodies of water, and urban spaces apart from the rest of the land cover in San Joaquin County, California, USA. In addition to determining a preferred algorithm, it is also apparent that certain parameters when tweaked, produce the optimal classifier for this dataset. In this case, this means most parameters set to default, with an increased spectral detail and a decreased spatial detail. What this indicates for crop planning is that the current algorithms used in California are already quite effective at accurately identifying unique types of land cover. This builds confidence in the field, however parameters could be similarly tweaked to produce an even better classification. This study can be useful for improving crop and water planning

Hiperspektrális távérzékeléses módszerek alkalmazása térbeli folyamatok jellemzésére = Application of hyperspectal RS methods for analyzing spatial processes

1, Az alacsony költségvetésű projektben sikerült megvalósítani két nagy területet lefedő (60-50 km2) hiperspektrális kampányt. 2, A hiperspektrális felvételek radiometriai hibái ellenére összeállítottunk egy olyan spektrumkönyvtárat, mely a jövőbeni projektekben alapadatként szolgálhat. Ennek alapja a nagy területet lefedő terepi felvételezés és adatgyűjtés volt. 3, A projekt során új eredményeket értünk el a városi felszínek vizsgálatában, különös tekintettel a subpixel alapú osztályozásra, az idősoros NDVI elemzésekre multi- és hiperspektrális felvételek alkalmazásában. 4, Hatékonyan alkalmaztunk kisformátumú CIR és hőtartományú infravörös kamerákat beépített városi környezet és mezőgazdasági – belvízzel elöntött – területek felvételezésére. 5, Térbeli statisztikai elemzéseket végeztünk a városi beépítettség és a városi hősziget-intenzitás kapcsolatrendszerének megállapítására, és olyan érvénye modelleket alkottunk, melyek alkalmasak a múltban készült multspektrális felvételekből az UHI számítására. 6, Multi- és hiperspektrális felvételek alapján, az SMA módszerre építve, olyan gyorsan futtatható háromszögmodellt alkottunk meg, mely alapján elkülöníthetők a belvízzel elöntött felszínek és az átmeneti típusok. Ezzel a módszerrel nagyobb pontosság érhető el a belvíztérképezésben, mint a terepi felvételezéssel. | 1, During a low cost research project, two (60-50 sqr km) relatively large were acquired by hyperspectral images. 2. In spite of the radiometric errors of the hyperspectral images a spectral library was composed, whose data can be basedata in a former research project. The base of this activity was a field measurement and spectral data collection. 3, During the research project new scientific results were published in the field of analysis of urban land areas, especially with the subpixel base image classification, the analysis and application of time series NDVI values of multi- and hyperspectral images. 4. Small format CIR and thermal infrared cameras were used effectively to monitor urban environment and agricultural (flooded by inland excess water) fields. 5. Spatial geostatistical analysis was carried out to prove the connection between urban land cover and intensity values of urban heat island. A model was developed, which is suitable for calculation of UHI from archive multispectral images. 6, Based on multi- and hyperspectral images, using spectral mixture analysis, fast triangle model was developed, which can used for elimination of flooded areas and other special land cover types of inland water maps

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

Analysing trade-offs and synergies between SDGs for urban development, food security and poverty alleviation in rapidly changing peri-urban areas: a tool to support inclusive urban planning

Transitional peri-urban contexts are frontiers for sustainable development where land-use change involves negotiation and contestation between diverse interest groups. Multiple, complex trade-offs between outcomes emerge which have both negative and positive impacts on progress towards achieving Sustainable Development Goals (SDGs). These trade-offs are often overlooked in policy and planning processes which depend on top-down expert perspectives and rely on course grain aggregate data which does not reflect complex peri-urban dynamics or the rapid pace of change. Tools are required to address this gap, integrate data from diverse perspectives and inform more inclusive planning processes. In this paper, we draw on a reinterpretation of empirical data concerned with land-use change and multiple dimensions of food security from the city of Wuhan in China to illustrate some of the complex trade-offs between SDG goals that tend to be overlooked with current planning approaches. We then describe the development of an interactive web-based tool that implements deep learning methods for fine-grained land-use classification of high-resolution remote sensing imagery and integrates this with a flexible method for rapid trade-off analysis of land-use change scenarios. The development and potential use of the tool are illustrated using data from the Wuhan case study example. This tool has the potential to support participatory planning processes by providing a platform for multiple stakeholders to explore the implications of planning decisions and land-use policies. Used alongside other planning, engagement and ecosystem service mapping tools it can help to reveal invisible trade-offs and foreground the perspectives of diverse stakeholders. This is vital for building approaches which recognise how trade-offs between the achievement of SDGs can be influenced by development interventions