scholarly journals Quality assurance and assessment framework for large area land cover maps validation in the Copernicus high-resolution hot spot monitoring activity

2021 ◽  
Vol 54 (1) ◽  
pp. 537-556
Author(s):  
Zoltan Szantoi ◽  
Gabriel Jaffrain ◽  
Heinz Gallaun ◽  
Conrad Bielski ◽  
Karl Ruf ◽  
...  
Keyword(s):  
Quality Assurance ◽  
High Resolution ◽  
Land Cover ◽  
Hot Spot ◽  
Assessment Framework ◽  
Large Area ◽  
Land Cover Maps ◽  
Monitoring Activity

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 COVER MONITORING OF LAGUNA LAKE WATERSHED USING MODIS NDVI DATA

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 85-92
Author(s):  
J. M. Medina ◽  
A. C. Blanco ◽  
C. G. Candido
Keyword(s):  
Land Cover ◽  
Hot Spots ◽  
Land Surface ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Land Reclamation ◽  
Hot Spot ◽  
Ground Truth Data ◽  
Land Cover Maps ◽  
Laguna Lake

Abstract. Land use and land cover monitoring is an important component in the management of Laguna Lake watershed due to its impacts on the lake’s water quality. Due to limitations caused by cloud cover, satellite systems with limited revisit capability fail to provide sufficient data to more effectively monitor the land surface. Normalized difference vegetation index (NDVI) derived from MODIS image data were used to generate land cover maps for the years 2001, 2005, 2009, 2013, and 2017. These were produced by classifying ISODATA classes using annual NDVI profiles, which resulted in land cover classes, namely, agricultural land, built-up, forest, rangeland, water, and wetland. The resulting maps were post-processed using multi-variate alteration detection (MAD), resulting in multi-temporal land cover maps with improved overall accuracies and kappa coefficients that indicate moderate agreement with ground truth data. Spatiotemporal hot spot analysis was also performed using NDVI data from 2001 to 2017 to identify vegetation hot spot areas, where clustering of low NDVI values were observed over the years. Results showed an increasing trend in built-up areas accompanied by decreasing trends in water and wetland areas, indicating impacts caused by land reclamation and expansion of residential subdivisions near the lakeshore. The decrease in total vegetation area from 2001 to 2017 could be attributed to conversion of land to built-up surface. Vegetated areas in identified hot spots decreased from 41% in 2001 to 19% in 2017. This suggests that vegetation cover in these hot spots was converted to non-vegetated surface during the time period studied.

scholarly journals STATISTICS FOR PATCH OBSERVATIONS

2016 ◽  
Vol XLI-B6 ◽  
pp. 235-242
Author(s):  
K. L. Hingee
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Tree Canopy ◽  
Closed Set ◽  
Closed Sets ◽  
Random Closed Sets ◽  
Contact Distribution ◽  
Land Cover Maps ◽  
Underlying Processes

In the application of remote sensing it is common to investigate processes that generate patches of material. This is especially true when using categorical land cover or land use maps. Here we view some existing tools, landscape pattern indices (LPI), as non-parametric estimators of random closed sets (RACS). This RACS framework enables LPIs to be studied rigorously. A RACS is any random process that generates a closed set, which encompasses any processes that result in binary (two-class) land cover maps. RACS theory, and methods in the underlying field of stochastic geometry, are particularly well suited to high-resolution remote sensing where objects extend across tens of pixels, and the shapes and orientations of patches are symptomatic of underlying processes. For some LPI this field already contains variance information and border correction techniques. After introducing RACS theory we discuss the core area LPI in detail. It is closely related to the spherical contact distribution leading to conditional variants, a new version of contagion, variance information and multiple border-corrected estimators. We demonstrate some of these findings on high resolution tree canopy data.

scholarly journals REVIEW OF HIGH-RESOLUTION GLOBAL LAND COVER

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 175-182
Author(s):  
G. Bratic ◽  
A. Vavassori ◽  
M. A. Brovelli
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Accuracy Assessment ◽  
General Information ◽  
Tree Canopy ◽  
Global Land Cover ◽  
Tree Canopy Cover ◽  
Global Land ◽  
Land Cover Maps

Abstract. The land cover detection on our planet at high spatial resolution has a key role in many scientific and operational applications, such as climate modeling, natural resources management, biodiversity studies, urbanization analyses and spatial demography. Thanks to the progresses in Remote Sensing, accurate and high-resolution land cover maps have been developed over the last years, aiming at detecting the spatial resolution of different types of surfaces. In this paper we propose a review of the high-resolution global land cover products developed through Earth Observation technologies. A series of general information regarding imagery and data used to produce the map, the procedures employed for the map development and for the map accuracy assessment have been provided for every dataset. The land cover maps described in this paper concern the global distribution of settlements (Global Urban Footprint, Global Human Settlement Built-Up, World Settlement Footprint), water (Global Surface Water), forests (Forest/Non-forest, Tree canopy cover), and a two land cover maps describing world in 10 generic classes (GlobeLand30 and Finer Resolution Observation and Monitoring of Global Land Cover). The advantages and shortcomings of these maps and of the methods employed to produce them are summarized and compared in the conclusions.

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.

scholarly journals Open educational resources for validation of global high-resolution land cover maps

2019 ◽  
Author(s):  
Candan E Kilsedar ◽  
Gorica Bratic ◽  
Monia E Molinari ◽  
Marco Minghini ◽  
Maria A Brovelli
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Open Access ◽  
Land Cover ◽  
Open Source ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Validation Process ◽  
Land Cover Maps

Land cover (LC) maps are crucial to analyze and understand several phenomena, including urbanization, deforestation and climate change. This elevates the importance of their accuracy, which is assessed through a validation process. However, we observed that knowledge on the importance of LC maps and their validation is limited. Hence, a set of educational resources has been created to assist in the validation of LC maps. These resources, available under an open access license, focus on validation through open source and easy-to-use software. Moreover, addressing the lack of accurate and up-to-date reference LC data, an application has been developed that provides users a means to collect LC data.

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