Adversarial Feature Equilibrium Network for Multimodal Change Detection in Heterogeneous Remote Sensing Images

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Land use planners require up-to-date and spatially accurate time series land resources information and changing pattern for future management. As a result, assessing the status of land cover change due to population growth and arable expansion, land degradation and poor resource management, partial implementation of policy strategies, and poorly planned infrastructural development is essential. Thus, the objective of the study was to quantify the spatiotemporal dynamics of land use land cover change between 1995 and 2014 using 5 multi-temporal cloud-free Landsat Thematic Mapper images. The maximum likelihood (ML)-supervised classification technique was applied to create signature classes for significant land cover categories using means and variances of the training data to estimate the probability that a pixel is a member of a class. The final Bayesian ML classification resulted in 12 major land cover units, and the spatiotemporal change was quantified using post-classification and statistical change detection techniques. For a period of 20 years, there was a continuously increasing demand for arable areas, which can be represented by an exponential growth model. Excepting the year 2009, the built-up area has shown a steady increase due to population growth and its need for infrastructure development. There was nearly a constant trend for water bodies with a change in slope significantly less than +0.01%. The 2014 land cover change statistics revealed that the area was mainly covered by cultivated, wood, bush, shrub, grass, and forest land mapping units accounting nearly 63%, 12%, 8%, 6%, 4%, and 2% of the total, respectively. Land cover change with agro-climatic zones, soil types, and slope classes was common in most part of the area and the conversion of grazing land into plantation trees and closure area development were major changes in the past 20 years.

Land cover change is a significant issue for environmental managers for sustainable management. Remote sensing techniques have been shown to have a high probability of recognizing land cover patterns and change detection due to periodic coverage, data integrity, and provision of data in a broad range of the electromagnetic spectrum. We evaluate the applicability of remote sensing techniques for land cover pattern recognition, as well as land cover change detection of the Hatiya Island, Bangladesh, and quantify land cover changes from 1977 to 1999. A supervised classification approach was used to classify Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM), and Multispectral Scanner (MSS) images into eight major land cover categories. We detected major land cover changes over the 22-year study period. During this period, marshy land, mud, mud with small grass, and bare soil had decreased by 85%, 46%, 44%, and 24%, respectively, while agricultural land, medium forest, forest, and settlement had positive changes of 26%, 45%, 363%, and 59%, respectively. The primary drivers of such landscape change were erosion and accretion processes, human pressure, and the reforestation and land reclamation programs of the Bangladesh Government.

Land cover and its change are crucial for many environmental applications. This study focuses on the land cover classification and change detection with multitemporal and multispectral Sentinel-2 satellite data. To address the challenging land cover change detection task, we rely on two different deep learning architectures and selected pre-processing steps. For example, we define an excluded class and deal with temporal water shoreline changes in the pre-processing. We employ a fully convolutional neural network (FCN), and we combine the FCN with long short-term memory (LSTM) networks. The FCN can only handle monotemporal input data, while the FCN combined with LSTM can use sequential information (multitemporal). Besides, we provided fixed and variable sequences as training sequences for the combined FCN and LSTM approach. The former refers to using six defined satellite images, while the latter consists of image sequences from an extended training pool of ten images. Further, we propose measures for the robustness concerning the selection of Sentinel-2 image data as evaluation metrics. We can distinguish between actual land cover changes and misclassifications of the deep learning approaches with these metrics. According to the provided metrics, both multitemporal LSTM approaches outperform the monotemporal FCN approach, about 3 to 5 percentage points (p.p.). The LSTM approach trained on the variable sequences detects 3 p.p. more land cover changes than the LSTM approach trained on the fixed sequences. Besides, applying our selected pre-processing improves the water classification and avoids reducing the dataset effectively by 17.6%. The presented LSTM approaches can be modified to provide applicability for a variable number of image sequences since we published the code of the deep learning models. The Sentinel-2 data and the ground truth are also freely available.

Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information-based change detection methods have been proposed in past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. All the bi-temporal images are scanned pixel by pixel so the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by three land cover change cases with Landsat bi-temporal remote sensing images and aerial images with very high spatial resolution (0.5 m/pixel). In comparison to several widely used change detection methods, the proposed approach can produce a land cover change inventory map with a competitive accuracy.

As a result of the development of geo-technologies, in recent years, software has emerged that has provided the possibility of deriving information. The most important of these are geographic information systems (GIS), which are many sources in satellite data from multi-spectral satellites, The source via the Internet is one of the most important modern means of providing large data that varied between maps and satellite images (LANDSAT-SPOT-NASA). The study of land cover changes is one of the most important subjects of interest to the sciences and in particular the geographical research related to the environment and its effects on humans and living organisms. In the current research, the data of interactive open source maps were adopted, which provided the possibility of analysis. Using the Change Matters esri, it is one of the most efficient locations for detection of land cover changes by dealing with visuals for different periods of time. The study also reviewed some of the open source websites dealing with GIS to indicate their importance and role in the analysis and production of maps. The research aims to employ open-source digital technologies via the web to extract information about land cover changes.

As one of the last agricultural frontiers of the humid tropics, Amazonia is the largest area of the world currently undergoing frontier settlement. Although the earliest intrusions of foreign populations into Amazonia date from pre-Hispanic times, the large-scale entrance of peasant colonists into the vast region is a recent phenomenon. Much of this movement represents the spontaneous migration of peoples, but governments in the region have also become increasingly interested in opening up and integrating Amazonia to national and international economies. These actions are frequently seen as potential solutions to a number of national problems, including the need to increase agricultural production, correct spatial imbalances in the distribution of population, exploit frontier lands for reasons of national security, and defuse potentially serious political problems resulting from the existing agrarian structure, landlessness, and unemployment. The upper basin of the Amazon in Ecuador, bordering on the eastern slopes of the Andes, is one such area of frontier settlement. Recent decades have witnessed the rapid conversion of these Amazonian forests to agricultural uses through a series of schemes bearing such labels as land development and colonization. Most forest intervention in the region has come at the hands of colonist farmers attempting to establish land claims along transport routes originally constructed to aid in petroleum exploration and exploitation. These are farmers who formerly have made a living in long-established farmlands and who, for various reasons (population pressures, pervasive poverty, maldistribution of farmland, lack of inputs for intensive cultivation, lack of nonagrarian livelihood opportunities, and generally inadequate rural development) have been increasingly squeezed out of their homelands. A marginal person by virtue of his low socioeconomic and political status, the farmer often perceives

This study examines landscape changes in the context of China's national Grain for Green (GFG) policy, one of the world's largest "payment for environmental/ecosystem services" (PES) programs. We explored landscape structures and dynamics between 2000 and 2010 in Shaanxi Province, the Chinese province with the greatest amount of cropland conversion and reforestation in recent decades. We used Landsat Thematic Mapper (TM)-derived data and landscape metrics for six land cover classes to determine (1) the major land cover changes during enforcement of the policy, (2) the spatial and temporal variations in these changes, and (3) the effects of land cover changes on landscape structure and dynamics. The results suggested that provincial-level land cover changes modestly reflected the goals of the GFG. Over the 10-year study period, the forest and grassland coverages expanded from 95,737.9 to 97,017.4km(2) and from 37,235.9 to 40,613.1km(2), respectively, while the cropland coverage decreased from 59,222.8 to 54,007.6km(2). The conversion direction differed regionally: the targeted croplands in Shanbei, namely, types III and IV, were mainly transformed into grassland while those in Shannan were mainly transformed into forestland. Reforestation was associated with increased inter-landscape aggregation and connection. Despite this large-scale reforestation trend, we found notable and significant differences in the land cover changes at the subprovincial level.

Land use, land cover, and climate change impacts are current global challenges that are affecting many sectors, like agricultural production, socio-economic development, water quality, and causing land fragmentation. In developing countries like Uganda, rural areas with high populations dependent on agriculture are the most affected. The development of sustainable management measures requires proper identification of drivers and impacts on the environment and livelihoods of the affected communities. This study applied drivers, pressure, state, impact, and response model in the L. Kyoga basin to determine the drivers and impacts of land use, land cover, and climate change on livelihoods and the environment. The objective of this study was to determine the drivers and impacts of land use, land cover, and climate changes on the environment and livelihoods in the L. Kyoga basin and suggest sustainable mitigation measures. Focus group discussions, key informant interviews, field observations, and literature reviews were used to collect data. Population increase and climate change were the leading drivers, while agriculture and urbanization were the primary pressures, leading to degraded land, wetlands, and forests; loss of soil fertility, hunger, poverty, poor water quality, which are getting worse. The local communities, government, and non-government institutions had responses to impacts, including agrochemicals, restoration, and conservation approaches. Although most responses were at a small/pilot scale level, most responses had promising results. The application of policies and regulations to manage impacts was also found to be weak. Land use, land cover changes, and climate change occur in the L. Kyoga basin with major impacts on land, water, and community livelihoods. With the observed increase in climate change and population growth, drivers and impacts are potentially getting worse. Therefore, it is essential to expand interventions, provide relief, review policies and regulations, and enforce them. The findings are helpful for decisions and policy-makers to design appropriate management options.

In this paper the problem of detecting land cover changes by using multitemporal remote sensing images is addressed. An approach aimed to explicitly identify what kind of land cover transition has actually taken place in an area proposed. This approach is based on the compound classification of multitemporal images. In particular, a simple model to represent the probabilities of transition is exploited to strongly simplify the compound classification task. The effectiveness of the proposed approach is confirmed by experimental results obtained by using remote sensing images containing simulated land cover transitions.

The development of improved satellite technology generates a huge amount of remote sensing data, these data play the crucial role in natural resource management. The land use and land cover (LULC) change intensely affects local environment, as well as the global environment. Therefore, the quantifiable knowledge about LULC changes occur in global scale is important to make effective planning for conservation and precise use of natural resources, that has motivated the scientists to develop the various land cover change detection techniques. In this paper, we have proposed neural network-based approach, i.e., focused time delay neural network (FTDNN)-based approach for land cover change detection, which is a time series prediction-based approach and detect the sudden change in the enhanced vegetation index (EVI) time series. The performance of the proposed method has been addressed by using quantitative and qualitative analysis techniques. For the quantitative evaluation, the proposed algorithm is applied to the standard synthetic data set, which are analogous to EVI time series data set. The performance result of the proposed method compares with the four previously existing data mining-based benchmark techniques. The analysis was shown that the FTDNN-based method significantly outperforms than other techniques. For qualitative analysis, the San Francisco Bay Area data set has been used, which comprises real EVI time series. The proposed FTDNN-based method is applied to the San Francisco Bay Area data set and observe the interesting land cover changes. These outcomes indicate the effectiveness of data mining techniques for the land cover change detection problem.

The pace of change in land use and cover in Ethiopia depends on three main factors that cause pressure on agriculture land: resettlement programmes, population growth and increasing agricultural investments. Gambella is one of the regions of Ethiopia that attracts large-scale agricultural investments that extensively drive land use and cover changes in the region. The aim of this study is to examine the rate, extent and distribution of various land use and cover changes in Gambella Regional State, Ethiopia, from 1987 to 2017. The analysis is mainly based on Landsat 5 and Sentinel 2A satellite images and fieldwork. Two Landsat Thematic Mapper and a Sentinel 2A image were used for determining the maximum likelihood of land use/cover classification. The results show that farmland decreased by 26 km2 from 1987 to 2000; however, during the last two decades, agricultural land area increased by 599 km2, mainly at the cost of tropical grasslands and forests. We found that areas cultivated by smallholder farmers increased by 9.17% from 1987 to 2000. However, small-scale farm activities decreased by 7% from 2000 to 2017. Areas cultivated by large-scale state farms totalled 202 km2 in 1987; but by 2000, this large-scale state farming had been completely abandoned by the state, and as a result, its land use has decreased to zero. Despite this, in 2017 large-scale farming increased to 746 km2. In addition, Gambella National Park, which is the nation's largest national park and ecosystem, was also largely affected by Land Use and Land Cover changes. The conversion of savannah/tropical grasslands to agricultural farmland has caused varied and extensive environmental degradation to the park. The Land Use and Land Cover changes in the Gambella region are discussed on the basis of underlying socioeconomic factors.

Land use land cover (LULC) change detection based on remote sensing data is an important source of information for various decision support systems for important to land conservation, sustainable development and management of water resources. The land use/ land cover mapping has been utilised for identifying the current land use pattern and changes occurred during 2000 and 2011 using satellite remote sensing technology and GIS tools. LANDSAT satellite imageries for the period 2000 and 2011 were used to prepare the land use/land cover (LULC) maps. Study area was characterised into nine classes. The classified images showed that Shallow water, Agriculture field with crops, open field without crop and Forest areas which have been decreased while Deep water, Marshy area, Orchard with other vegetation, Barren land and Built up area have increased during this period. This research highlights that remote sensing and GIS techniques could be effectively used for development of land use/land cover plan.

This paper analyzed land cover changes in Gazipur Sadar - an important urban fringe of expanding Dhaka City, Bangladesh, by leveraging remotely sensed imageries between 1973 and 2017. Landsat images of1973, 1991, 2006, and 2017 were classified using widely-preferred supervised classification method. Compared against ground-truth data, the reported classification accuracy ranges from 85% to 89%. Our classified land cover maps reveal that built-up areas in Gazipur Sadar increased by 312.9%, mostly replacing vegetation cover. An overall 199.7% decrease of vegetative covers highlights on the degree of urbanization process and increasing population pressure faced by Gazipur Sadar over the past decades. The rapid decrease of vegetative cover only 57 sq. km remains out of 344 sq. km throughout the region, including the Sal (Shorea robusta) forest and other floral species - invaluable resources for biodiversity and ecosystem health, should be taken as 'alarming' situation by the local authority responsible for promoting and managing sustainable development goals. In that light, this study emphasizes on the need for a critical assessment of future development initiatives in the Gazipur Sadar area and suggests for maintaining acceptable tradeoffs between development and environmental protection.

Regional difference of environmental evolvements is one of important aspects in world change research program. Changes in land cover and in the way people use the land have become recognized as important global environmental change in many areas. Land-use and cover changing (LUCC) is one of the major studies of global changing lately. Land-use is the term which covers the condition of used-land, the method, extent, structure, regional distributing and benefits in the land-use. It is affected by the natural condition or is enslaved to the conditions in society, economy and technology, and social production mode plays a decisive role in land-use; land-cover is the state of covering which is formed on account of the earth's surface or contrived by human being, is the summary of vegetation and artificially covering on the earth's surface. So land-use and cover changing is connecting closely. Land-use and cover changing information points that information on the position, distributing, range, and size of land-use and cover changing in the certain time. Motivated by a global concern for sustainability and environmental quality in city, a considerable number of studies have utilized satellite sensor data in the analysis of urban morphological change .some studies focused on the physical and socioeconomic drivers of change in urban land cover and implications on land use practices and resource management. Other studies went beyond the characterization of change and its causes and attempted to integrate remotely sensed data with models of urban growth to project future change. GIS and RS technologies are widely applied for LUCC studies providing a powerful tool for capturing, storing, checking manipulating, merging, analyzing and displaying data. Especially RS technology are also widely used for LUCC studies such as automatic discovery changing, automatic extraction changing area, confirmation changing type, using interactive explanation accessorily to extract the changing information of land-use in the research area. RS has an important contribution to make in the actual change in LUCC on regional. This paper studies land-use and land-cover change in Fuzhou area of Fujian Province, China. It builds a series of land-use change maps using Remote Sensing automatic monitoring and GIS spatial analysis techniques. Finally the paper briefly discusses the drive of land-use and cover changes. The study provides a reference to the sustainable development for the region.