Geostatistical integration of spectral and spatial information for land-cover mapping using remote sensing data

Park, No-Wook; Chi, Kwnaghoon; Kwon, Bo Sang

doi:10.1007/bf02919565

Cited by 15 publications

(4 citation statements)

References 20 publications

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“…For instance, Bao et al [ 47 ] not only used bike-sharing trip data and POI data but also added bicycle infrastructure data, weather data, and sociodemographic characteristics. Some even combined land data with spatial information [ 48 , 49 , 50 ]. Zhai et al [ 51 ] proposed the method of place2vec.…”

Section: Related Workmentioning

confidence: 99%

Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments

Liu

Xue

et al. 2020

Sensors

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The rapid development of urbanization has increased traffic pressure and made the identification of urban functional regions a popular research topic. Some studies have used point of interest (POI) data and smart card data (SCD) to conduct subway station classifications; however, the unity of both the model and the dataset limits the prediction results. This paper not only uses SCD and POI data, but also adds Online to Offline (OTO) e-commerce platform data, an application that provides customers with information about different businesses, like the location, the score, the comments, and so on. In this paper, these data are combined to and used to analyze each subway station, considering the diversity of data, and obtain a passenger flow feature map of different stations, the number of different types of POIs within 800 m, and the situation of surrounding OTO stores. This paper proposes a two-stage framework, to identify the functional region of subway stations. In the passenger flow stage, the SCD feature is extracted and converted to a feature map, and a ResNet model is used to get the output of stage 1. In the built environment stage, the POI and OTO features are extracted, and a deep neural network with stacked autoencoders (SAE–DNN) model is used to get the output of stage 2. Finally, the outputs of the two stages are connected and a SoftMax function is used to make the final identification of functional region. We performed experimental testing, and our experimental results show that the framework exhibits good performance and has a certain reference value in the planning of subway stations and their surroundings, contributing to the construction of smart cities.

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Section: Related Workmentioning

confidence: 99%

Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments

Liu

Xue

et al. 2020

Sensors

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“…Remote sensing is the major source of spatial information about the Earth surface’s cover and constitution [ 4 ]. Different sensors capture abundant information regarding the Earth, which can be used by scientists that are interested in monitoring spatial information in a timely manner [ 8 ]. During the past five decades, remote sensing technology has been used in many wetland research areas such as: (1) land use/cover changes or mapping in wetland regions [ 4 , 9 , 10 , 11 ]; (2) carbon cycle and climate warming in wetland environments [ 5 , 12 ]; (3) release of carbon by peatland fires [ 13 , 14 , 15 , 16 ]; and (4) hydrology processes in wetlands [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

A Review of Wetland Remote Sensing

Guo

Sheng

et al. 2017

Sensors

351

216

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Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers.

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“…During the past five decades, remote sensing techniques have emerged as an essential tool in mapping, assessing, and monitoring spatial information about natural resources, including aquatic ecosystems. Different sensors in remote sensing capture abundant information and have been used in a number of wetland researches by scientists interested in monitoring spatial information in a timely manner [50]. Data acquired from Lidar, Radar, hyperspectral images, high-resolution images, medium-resolution images, coarse-resolution images, and aerial photographs have been used to study dynamics of wetland ecosystems using different techniques and is an emerging tool for systematic study of wetlands.…”

Section: Introductionmentioning

confidence: 99%

Assessing land use land cover dynamics of wetland ecosystems using Landsat satellite data

Jamal

Ahmad

2020

SN Appl. Sci.

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In the light of growing debates over climate change, population growth, and associated activities, wetland ecosystems have gained immeasurable importance as they have potential to serve both environmentally and economically. Hence, the evaluation of wetland ecosystems dynamics and development of comprehensive management policies demand accurate assessment of land use-land cover changes over time and distinct hydrological processes taking place in such aquatic ecosystems. Assessment of such changes is imperative to deal with various perspectives of resource management, ecosystem conservation, sustainable development and land use planning, etc. The present study aims to examine the spatiotemporal dynamics of LULC in wetland ecosystems of Kashmir Valley using Landsat datasets of the period 1994-2018. Maximum likelihood classifier was used to quantify LULC changes in ERDAS IMAGINE (14). A total of seven categories were classified viz. forest, plantation, marshy land, water body, horticulture, agriculture, and built-up. From the present analysis, two patterns of LULC changes were observed in the study area (in both cases: Wular, as well as, Hokersar and Anchar): (1) Continuous increase in area of respective categories: agriculture, horticulture, and built-up (Wular Lake); forest, plantation, agriculture, and built-up (Hokersar and Anchar Lake); (2) a consistent decrease in categories like: forest, plantation, water body, and marshy land (Wular Lake); water body, marshy land, agriculture (Hokersar and Anchar Lake). Most significant change in terms of area has been observed in plantation −5378.18 ha, water body −3954.94 ha, and built-up +2556.18 (Wular Lake); horticulture +4884.3 ha, built-up +6071.96 ha, agriculture −11,605.43 ha, and water body −106.01 ha (Hokersar and Anchar Lake). The LULC transformation, mostly driven by human-induced factors, pose a severe threat to wetland ecosystems of Kashmir Valley. Anthropogenic activities threatening the wetland ecosystems may include urban development, agriculture and horticulture practices, impacts of insecticides and pesticides, fertilizers, climate change and invasion of alien species.

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Geostatistical integration of spectral and spatial information for land-cover mapping using remote sensing data

Cited by 15 publications

References 20 publications

Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments

Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments

A Review of Wetland Remote Sensing

Assessing land use land cover dynamics of wetland ecosystems using Landsat satellite data

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