scholarly journals Mapping Inter-Annual Land Cover Variations Automatically Based on a Novel Sample Transfer Method

2018 ◽  
Vol 10 (9) ◽  
pp. 1457 ◽  
Author(s):  
Cheng Zhong ◽  
Cuizhen Wang ◽  
Hui Li ◽  
Wenlong Chen ◽  
Yong Hou
Keyword(s):  
Land Cover ◽  
Reference Data ◽  
Urban Expansion ◽  
Remotely Sensed Data ◽  
Object Based ◽  
Short Time Span ◽  
Transfer Method ◽  
The Times ◽  
Land Cover Maps ◽  
Sample Transfer

Most land cover mapping methods require the collection of ground reference data at the time when the remotely sensed data are acquired. Due to the high cost of repetitive collection of reference data, however, it limits the production of annual land cover maps to a short time span. In order to reduce the mapping cost and to improve the timeliness, an object-based sample transfer (OBST) method was presented in this study. The object-based analysis with strict constrains in area, shape and index values is expected to reduce the accident errors in selecting and transferring samples. The presented method was tested and compared with same-year mapping (SY), cross-year mapping (CY) and multi-index automatic classification (MI). For the study years of 2001–2016, both the overall accuracies (above 90%) and detailed accuracy indicators of the presented method were very close to the SY accuracy and higher than accuracies of CY and MI. With the presented method, the times-series land cover map of Guangzhou, China were derived and analyzed. The results reveal that the city has undergone rapid urban expansion and the pressure on natural resources and environment has increased. These results indicate the proposed method could save considerable cost and time for mapping the spatial-temporal changes of urban development. This suggests great potential for future applications as more satellite observations have become available all over the globe.

Incorporating the uncertainty of linguistic-scale reference data to assess accuracy of land-cover maps using fuzzy intervals

2013 ◽  
Vol 34 (11) ◽  
pp. 4008-4024 ◽  
Author(s):  
Pedro Sarmento ◽  
Cidália C. Fonte ◽  
Mário Caetano ◽  
Stephen V. Stehman
Keyword(s):  
Land Cover ◽  
Reference Data ◽  
Land Cover Maps

scholarly journals Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map

2020 ◽  
Vol 12 (9) ◽  
pp. 1418
Author(s):  
Runmin Dong ◽  
Cong Li ◽  
Haohuan Fu ◽  
Jie Wang ◽  
Weijia Li ◽  
...  
Keyword(s):  
Land Cover ◽  
Training Data ◽  
Training Dataset ◽  
Land Cover Mapping ◽  
Remotely Sensed Data ◽  
Large Area ◽  
National Scale ◽  
Substantial Progress ◽  
Efficient Learning ◽  
Land Cover Maps

Substantial progress has been made in the field of large-area land cover mapping as the spatial resolution of remotely sensed data increases. However, a significant amount of human power is still required to label images for training and testing purposes, especially in high-resolution (e.g., 3-m) land cover mapping. In this research, we propose a solution that can produce 3-m resolution land cover maps on a national scale without human efforts being involved. First, using the public 10-m resolution land cover maps as an imperfect training dataset, we propose a deep learning based approach that can effectively transfer the existing knowledge. Then, we improve the efficiency of our method through a network pruning process for national-scale land cover mapping. Our proposed method can take the state-of-the-art 10-m resolution land cover maps (with an accuracy of 81.24% for China) as the training data, enable a transferred learning process that can produce 3-m resolution land cover maps, and further improve the overall accuracy (OA) to 86.34% for China. We present detailed results obtained over three mega cities in China, to demonstrate the effectiveness of our proposed approach for 3-m resolution large-area land cover mapping.

scholarly journals Land-Use/Land-Cover Changes and Their Influence on the Ecosystem in Chengdu City, China during the Period of 1992–2018

2018 ◽  
Vol 10 (10) ◽  
pp. 3580 ◽  
Author(s):  
Xiaojuan Lin ◽  
Min Xu ◽  
Chunxiang Cao ◽  
Ramesh P. Singh ◽  
Wei Chen ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Ecosystem Service ◽  
Net Primary Productivity ◽  
Urban Expansion ◽  
Land Resource ◽  
Land Use Land Cover ◽  
Benefits Transfer ◽  
Transfer Method ◽  
Remote Sensing Interpretation

Due to urban expansion, economic development, and rapid population growth, land use/land cover (LULC) is changing in major cities around the globe. Quantitative analysis of LULC change is important for studying the corresponding impact on the ecosystem service value (ESV) that helps in decision-making and ecosystem conservation. Based on LULC data retrieved from remote-sensing interpretation, we computed the changes of ESV associated with the LULC dynamics using the benefits transfer method and geographic information system (GIS) technologies during the period of 1992–2018 following self-modified coefficients which were corrected by net primary productivity (NPP). This improved approach aimed to establish a regional value coefficients table for facilitating the reliable evaluation of ESV. The main objective of this research was to clarify the trend and spatial patterns of LULC changes and their influence on ecosystem service values and functions. Our results show a continuous reduction in total ESV from United States (US) $1476.25 million in 1992, to US $1410.17, $1335.10, and $1190.56 million in 2001, 2009, and 2018, respectively; such changes are attributed to a notable loss of farmland and forest land from 1992–2018. The elasticity of ESV in response to changes in LULC shows that 1% of land transition may have caused average changes of 0.28%, 0.34%, and 0.50% during the periods of 1992–2001, 2001–2009, and 2009–2018, respectively. This study provides important information useful for land resource management and for developing strategies to address the reduction of ESV.

scholarly journals Analyzing the Uncertainty of Degree Confluence Project for Validating Global Land-Cover Maps Using Reference Data-Based Classification Schemes

2020 ◽  
Vol 12 (16) ◽  
pp. 2589
Author(s):  
Tana Qian ◽  
Tsuguki Kinoshita ◽  
Minoru Fujii ◽  
Yuhai Bao
Keyword(s):  
Land Cover ◽  
Reference Data ◽  
Data Uncertainty ◽  
Validation Data ◽  
Classification Schemes ◽  
Reference Information ◽  
Global Land Cover ◽  
Global Land ◽  
Land Cover Maps ◽  
The Impact

Global land-cover products play an important role in assisting the understanding of climate-related changes and the assessment of progress in the implementation of international initiatives for the mitigation of, and adaption to, climate change. However, concerns over the accuracies of land-cover products remain, due to the issue of validation data uncertainty. The volunteer-based Degree Confluence Project (DCP) was created in 1996, and it has been used to provide useful ground-reference information. This study aims to investigate the impact of DCP-based validation data uncertainty and the thematic issues on map accuracies. We built a reference dataset based on the DCP-interpreted dataset and applied a comparison for three existing global land-cover maps and DCP dataset-based probability maps under different classification schemes. The results of the obtained confusion matrices indicate that the uncertainty, including the number of classes and the confusion in mosaic classes, leads to a decrease in map accuracy. This paper proposes an informative classification scheme that uses a matrix structure of unaggregated land-cover and land-use classes, and has the potential to assist in the land-cover interpretation and validation processes. The findings of this study can potentially serve as a guide to select reference data and choose/define appropriate classification schemes.

scholarly journals Refining Land-Cover Maps Based on Probabilistic Re-Classification in CCA Ordination Space

2020 ◽  
Vol 12 (18) ◽  
pp. 2954
Author(s):  
Yue Wan ◽  
Jingxiong Zhang ◽  
Wenjing Yang ◽  
Yunwei Tang
Keyword(s):  
Logistic Regression ◽  
Land Cover ◽  
Reference Data ◽  
Reference Sample ◽  
Training Sample ◽  
Small Sample ◽  
Cca Ordination ◽  
Sample Data ◽  
Map Data ◽  
Land Cover Maps

Due to spatial inhomogeneity of land-cover types and spectral confusions among them, land-cover maps suffer from misclassification errors. While much research has focused on improving image classification by re-processing source images with more advanced algorithms and/or using images of finer resolution, there is rarely any systematic work on re-processing existing maps to increase their accuracy. We propose refining existing maps to achieve accuracy gains by exploring and utilizing relationships between reference data, which are often already available or can be collected, and map data. For this, we make novel use of canonical correspondence analysis (CCA) to analyze reference-map class co-occurrences to facilitate probabilistic re-classification of map classes in CCA ordination space, a synthesized feature space constrained by map class occurrence patterns. Experiments using GlobeLand30 land-cover (2010) over Wuhan, China were carried out using reference sample data collected previously for accuracy assessment in the same area. Reference sample data were stratified by map classes and their spatial heterogeneity. To examine effects of model-training sample size on refinements, three subset samples (360, 720, and 1480 pixels) were selected from a pool of 3000 sample pixels (the full training sample). Logistic regression modeling was employed as a baseline method for comparisons. Performance evaluation was based on a test sample of 1020 pixels using a strict and relaxed definitions of agreement between reference classification and map classification, resulting in measures of types I and II, respectively. It was found that the CCA-based method is more accurate than logistic regression in general. With increasing sample sizes, refinements generally lead to greater accuracy gains. Heterogeneous sub-strata usually see greater accuracy gains than in homogeneous sub-strata. It was also revealed that accuracy gains in specific strata (map classes and sub-strata) are related to strata refinability. Regarding CCA-based refinements, a relatively small sample of 360 pixels achieved a 3% gain in both overall accuracy (OA) and F0.01 score (II). By using a selective strategy in which only refinable strata of cultivated land and forest are included in refinement, accuracy gains are further increased, with 5–11% gains in users’ accuracies (UAs) (II) and 4–10% gains in F0.01 scores (II). In conclusion, on condition of refinability, map refinement is well worth pursuing, as it increases accuracy of existing maps, extends utility of reference data, facilitates uncertainty-informed map representation, and enhances our understanding about relationships between reference data and map data and about their synthesis.

scholarly journals Classification of various land features using RISAT-1 dual polarimetric data

2014 ◽  
Vol XL-8 ◽  
pp. 833-837 ◽  
Author(s):  
V. N. Mishra ◽  
P. Kumar ◽  
D. K. Gupta ◽  
R. Prasad
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Uttar Pradesh ◽  
Land Cover Classification ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Remotely Sensed Data ◽  
Sar Data ◽  
Land Cover Maps

Land use land cover classification is one of the widely used applications in the field of remote sensing. Accurate land use land cover maps derived from remotely sensed data is a requirement for analyzing many socio-ecological concerns. The present study investigates the capabilities of dual polarimetric C-band SAR data for land use land cover classification. The MRS mode level 1 product of RISAT-1 with dual polarization (HH & HV) covering a part of Varanasi district, Uttar Pradesh, India is analyzed for classifying various land features. In order to increase the amount of information in dual-polarized SAR data, a band HH + HV is introduced to make use of the original two polarizations. Transformed Divergence (TD) procedure for class separability analysis is performed to evaluate the quality of the statistics prior to image classification. For most of the class pairs the TD values are greater than 1.9 which indicates that the classes have good separability. Non-parametric classifier Support Vector Machine (SVM) is used to classify RISAT-1 data with optimized polarization combination into five land use land cover classes like urban land, agricultural land, fallow land, vegetation and water bodies. The overall classification accuracy achieved by SVM is 95.23 % with Kappa coefficient 0.9350.

scholarly journals Pemanfaatan Data Landsat Multitemporal Untuk Pemetaan Pola Ekspansi Perkotaan Secara Spasiotemporal (Studi Kasus Pada Tiga Perkotaan Metropolitan Di Pulau Jawa)

2018 ◽  
Vol 2 (1) ◽  
pp. 99
Author(s):  
Like Indrawati ◽  
Ari Cahyono
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Urban Expansion ◽  
Rapid Expansion ◽  
City Size ◽  
Digital Data ◽  
Urban Systems ◽  
Spatiotemporal Pattern ◽  
Random Forest Algorithm ◽  
Object Based

Utilization of multitemporal remote sensing data among others can be used todetermine thepattern of changes in urban expansion. One of the most important types of cities in urban systems isthe metropolitan urban area that covers several districts and cities. This is because the regiongenerally acts as the capital of the country, the provincial capital, and the center of economicactivities that are national or strategic. Understanding urban expansion at different metropolitanurban levels is important for expanding knowledge in times of urban growth and its impact on theenvironment. Aims in this study are: (1) utilization of multitemporal Landsat data for mapping urbanexpansion patterns, (2) knowing the effectiveness of object-based classification for mapping of urbansettlements and (3) spatiotemporal urban expansion pattern analysis in three metropolitan cities onJava Island.. In this study focused on three metropolitan urban in Java, namely DKI. Jakarta,Surabaya and Semarang. This study utilizing Landsat TM, ETM + and OLI image data to map urbansettlement land cover using object-based classification with Random Forest algorithm. Next,quantifying the typology of urban expansion and compare the spatiotemporal pattern of urbanexpansion during 2005-2015 on the results of land cover mapping. This research has found that (1)object-based classification with Random Forest algorithm is quite effective in terms of time of work tomap urban settlement cover on Landsat digital data having medium spatial resolution; (2) the threeurban metropolia is experiencing rapid and massive development and has a very variedspatiotemporal pattern; (3) Size of the city affect the pattern of urban expansion, followed by rapidexpansion of the region. Larger city size with relatively rapid expansion is more likely to experiencethe edge extension model, while smaller cities tend to develop with outlying models.

scholarly journals Wetland loss in the Ñeembucú Wetlands Complex, Paraguay, using remote sensing

2022 ◽  
Author(s):  
Frances O'Leary
Keyword(s):  
Decision Making ◽  
Land Cover ◽  
Urban Expansion ◽  
River System ◽  
Open Water ◽  
Wetland Loss ◽  
Forest Conversion ◽  
Remotely Sensed Data ◽  
Goods And Services ◽  
Inspection Data

South American wetlands are of global importance, yet limited delineation and monitoring restricts informed decision-making around the drivers of wetland loss. A growing human population and increasing demand for agricultural products has driven wetland loss and degradation in the Neotropics. Understanding of wetland dynamics and land use change can be gained through wetland monitoring. The Ñeembucú Wetlands Complex is the largest wetland in Paraguay, lying within the Paraguay-Paraná-La Plata River system. This study aims to use remotely sensed data to map land cover between 2006 and 2021, quantify wetland change over the 15-year study period and thus identify land cover types vulnerable to change in the Ñeembucú Wetlands Complex. Forest, dryland vegetation, vegetated wetland and open water were identified using Random Forest supervised classifications trained on visual inspection data and field data. Annual change of -0.34, 4.95, -1.65, 0.40 was observed for forest, dryland, vegetated wetland and open water, respectively. Wetland and forest conversion is attributed to agricultural and urban expansion. With ongoing pressures on wetlands, monitoring will be a key tool for addressing change and advising decision-making around development and conservation of valuable ecosystem goods and services in the Ñeembucú Wetlands Complex.

GLOBE Observer and the GO on a Trail Data Challenge: A Citizen Science Approach to Generating a Global Land Cover Land Use Reference Dataset

2021 ◽  
Vol 3 ◽  
Author(s):  
Holli A. Kohl ◽  
Peder V. Nelson ◽  
John Pring ◽  
Kristen L. Weaver ◽  
Daniel M. Wiley ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Reference Data ◽  
Ground Cover ◽  
The United States ◽  
Large Area ◽  
Natural Processes ◽  
Land Cover Data ◽  
Data Points ◽  
Land Cover Maps

Land cover and land use are highly visible indicators of climate change and human disruption to natural processes. While land cover is frequently monitored over a large area using satellite data, ground-based reference data is valuable as a comparison point. The NASA-funded GLOBE Observer (GO) program provides volunteer-collected land cover photos tagged with location, date and time, and, in some cases, land cover type. When making a full land cover observation, volunteers take six photos of the site, one facing north, south, east, and west (N-S-E-W), respectively, one pointing straight up to capture canopy and sky, and one pointing down to document ground cover. Together, the photos document a 100-meter square of land. Volunteers may then optionally tag each N-S-E-W photo with the land cover types present. Volunteers collect the data through a smartphone app, also called GLOBE Observer, resulting in consistent data. While land cover data collected through GLOBE Observer is ongoing, this paper presents the results of a data challenge held between June 1 and October 15, 2019. Called “GO on a Trail,” the challenge resulted in more than 3,300 land cover data points from around the world with concentrated data collection in the United States and Australia. GLOBE Observer collections can serve as reference data, complementing satellite imagery for the improvement and verification of broad land cover maps. Continued collection using this protocol will build a database documenting climate-related land cover and land use change into the future.

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