scholarly journals Training Data Selection for Annual Land Cover Classification for the Land Change Monitoring, Assessment, and Projection (LCMAP) Initiative

2020 ◽  
Vol 12 (4) ◽  
pp. 699 ◽  
Author(s):  
Qiang Zhou ◽  
Heather Tollerud ◽  
Christopher Barber ◽  
Kelcy Smith ◽  
Daniel Zelenak
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Classification Accuracy ◽  
Training Data ◽  
Land Change ◽  
Auxiliary Data ◽  
Surface Change ◽  
Land Cover Map ◽  
The U.S ◽  
Change Monitoring

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative involves detecting changes in land cover, use, and condition with the goal of producing land change information to improve the understanding of the Earth system and provide insights on the impacts of land surface change on society. The change detection method ingests all available high-quality data from the Landsat archive in a time series approach to identify the timing and location of land surface change. Annual thematic land cover maps are then produced by classifying time series models. In this paper, we describe the optimization of the classification method used to derive the thematic land cover product. We investigated the influences of auxiliary data, sample size, and training from different sources such as the U.S. Geological Survey’s Land Cover Trends project and National Land Cover Database (NLCD 2001 and NLCD 2011). The results were evaluated and validated based on independent data from the training dataset. We found that refining the auxiliary data effectively reduced artifacts in the thematic land cover map that are related to data availability. We improved the classification accuracy and stability considerably by using a total of 20 million training pixels with a minimum of 600,000 and a maximum of 8 million training pixels per class within geographic windows consisting of nine Analysis Ready Data tiles (450 by 450 km2). Comparisons revealed that the NLCD 2001 training data delivered the best classification accuracy. Compared to the original LCMAP classification strategy used for early evaluation (e.g., Trends training data, 20,000 samples), the optimized classification strategy improved the annual land cover map accuracy by an average of 10%.

scholarly journals Implementation of CCDC to produce the LCMAP Collection 1.0 annual land surface change product

2021 ◽  
Author(s):  
George Xian ◽  
Kelcy Smith ◽  
Danika Wellington ◽  
Josephine Horton ◽  
Qiang Zhou ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Land Cover Change ◽  
Land Surface ◽  
The United States ◽  
Land Resource ◽  
Land Change ◽  
Continuous Change ◽  
Ancillary Data ◽  
Surface Change

Abstract. The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985–2017 at 30-meter resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover and five land surface change related products. The LCMAP land cover products were validated using a collection of ~25,000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5 %. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and Shared Mesos Cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30 m annual land cover and land cover change datasets that span from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future.

scholarly journals Methods of Rapid Quality Assessment for National-Scale Land Surface Change Monitoring

2020 ◽  
Vol 12 (16) ◽  
pp. 2524
Author(s):  
Qiang Zhou ◽  
Christopher Barber ◽  
George Xian
Keyword(s):  
United States ◽  
Land Cover ◽  
Quality Assessment ◽  
Land Surface ◽  
Simulated Data ◽  
The United States ◽  
Surface Change ◽  
Tile Basis ◽  
Local Outlier ◽  
Change Monitoring

Providing rapid access to land surface change data and information is a goal of the U.S. Geological Survey. Through the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative, we have initiated a monitoring capability that involves generating a suite of 10 annual land cover and land surface change datasets across the United States at a 30-m spatial resolution. During the LCMAP automated production, on a tile-by-tile basis, erroneous data can occasionally be generated due to hardware or software failure. While crucial to assure the quality of the data, rapid evaluation of results at the pixel level during production is a substantial challenge because of the massive data volumes. Traditionally, product quality relies on the validation after production, which is inefficient to reproduce the whole product when an error occurs. This paper presents a method for automatically evaluating LCMAP results during the production phase based on 14 indices to quickly find and flag erroneous tiles in the LCMAP products. The methods involved two types of comparisons: comparing LCMAP values across the temporal record to measure internal consistency and calculating the agreement with multiple intervals of the National Land Cover Database (NLCD) data to measure the consistency with existing products. We developed indices on a tile-by-tile basis in order to quickly find and flag potential erroneous tiles by comparing with surrounding tiles using local outlier factor analysis. The analysis integrates all indices into a local outlier score (LOS) to detect erroneous tiles that are distinct from neighboring tiles. Our analysis showed that the methods were sensitive to partially erroneous tiles in the simulated data with a LOS higher than 2. The rapid quality assessment methods also successfully identified erroneous tiles during the LCMAP production, in which land surface change results were not properly saved to the products. The LOS map and indices for rapid quality assessment also point to directions for further investigations. A map of all LOS values by tile for the published LCMAP shows all LOS values are below 2. We also investigated tiles with high LOS to ensure the distinction with neighboring tiles was reasonable. An index in this study shows the overall agreement between LCMAP and NLCD on a tile basis is above 71.5% and has an average at 89.1% across the 422 tiles in the conterminous United States. The workflow is suitable for other studies with a large volume of image products.

Validation of the U.S. Geological Survey's Land Change Monitoring, Assessment and Projection (LCMAP) Collection 1.0 annual land cover products 1985–2017

2021 ◽  
Vol 265 ◽  
pp. 112646
Author(s):  
Stephen V. Stehman ◽  
Bruce W. Pengra ◽  
Josephine A. Horton ◽  
Danika F. Wellington
Keyword(s):  
Land Cover ◽  
Land Change ◽  
The U.S ◽  
Change Monitoring

Scenarios of the Tropical Dry Forest of Purulia District West Bengal

2020 ◽  
pp. 254-267
Author(s):  
Masuma Begum ◽  
Niloy Pramanick ◽  
Anirban Mukhopadhyay ◽  
Sayani Datta Majumdar
Keyword(s):  
Land Cover ◽  
Forest Cover ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Tropical Dry Forest ◽  
Dry Forest ◽  
Training Data ◽  
Change Dynamics ◽  
Land Cover Map ◽  
Purulia District

In this chapter, satellite images of the years 1995, 2005, and 2015 of LANDSAT have been used. After pre-processing (geometric correction and atmospheric correction using FLAASH, LULC change dynamics have been assessed to estimate the changes in total forest cover in Purulia district through an unsupervised K-means classification scheme. To evaluate the health status, vegetation indices, namely NDVI, SAVI, and CVI, have been used. The increase in NDVI, SAVI, and CVI values was inferred as no significant degradation of Purulia forest cover. Moreover, future scenarios have been predicted by implementing a CA-MARKOV model. Using the land cover map of 1995 as the base map, and from 1995 to 2005 as training data, a land cover map of 2015 has been generated which in turn validated by the actual land cover of 2015. After validation, prediction of land cover was possible for the years 2035 and 2050. The prediction suggested that forest area will increase by approximately 4% from 2015 to 2035 and by 3% from 2035 to 2050.

scholarly journals Characterizing Land Use/Land Cover Using Multi-Sensor Time Series from the Perspective of Land Surface Phenology

2019 ◽  
Vol 11 (14) ◽  
pp. 1677 ◽  
Author(s):  
Lan H. Nguyen ◽  
Geoffrey M. Henebry
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Land Surface ◽  
Quadratic Model ◽  
Observation Data ◽  
Land Use Land Cover ◽  
Land Surface Phenology ◽  
Land Cover Map ◽  
Phenological Metrics

Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVI2), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVI2 = f(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVI2 = f(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area.

scholarly journals Quantifying Land Cover Change Due to Petroleum Exploration and Production in the Haynesville Shale Region Using Remote Sensing

2017 ◽  
pp. 813-830
Author(s):  
Daniel Unger ◽  
I-Kuai Hung ◽  
Kenneth Farrish ◽  
Darinda Dans
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Classification Accuracy ◽  
Oil And Gas ◽  
Agricultural Land ◽  
Landscape Fragmentation ◽  
Petroleum Exploration ◽  
Landsat Thematic Mapper ◽  
Exploration And Production ◽  
The U.S

The Haynesville Shale lies under areas of Louisiana and Texas and is one of the largest gas plays in the U.S. Encompassing approximately 2.9 million ha, this area has been subject to intensive exploration for oil and gas, while over 90% of it has traditionally been used for forestry and agriculture. In order to detect the landscape change in the past few decades, Landsat Thematic Mapper (TM) imagery for six years (1984, 1989, 1994, 2000, 2006, and 2011) was acquired. Unsupervised classifications were performed to classify each image into four cover types: agriculture, forest, well pad, and other. Change detection was then conducted between two classified maps of different years for a time series analysis. Finally, landscape metrics were calculated to assess landscape fragmentation. The overall classification accuracy ranged from 84.7% to 88.3%. The total amount of land cover change from 1984 to 2011 was 24%, with 0.9% of agricultural land and 0.4% of forest land changed to well pads. The results of Patch-Per-Unit area (PPU) index indicated that the well pad class was highly fragmented, while agriculture (4.4-8.6 per sq km) consistently showed a higher magnitude of fragmentation than forest (0.8-1.4 per sq km).

Topographic normalization of satellite imagery for image classification in northeast India

2009 ◽  
Vol 33 (6) ◽  
pp. 815-836 ◽  
Author(s):  
M. Balakrishna Reddy ◽  
Baiantimon Blah
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Resource Planning ◽  
Northeast India ◽  
Training Data ◽  
Vegetation Types ◽  
Bivariate Correlation ◽  
Liss Iii

IRS-LISS-III satellite imagery covering Nongkhyllem Wildlife Sanctuary area located within the Ri-Bhoi District of Meghalaya State, northeast India, was used for analysis of the landcover pattern and vegetation types occurring there. A maximum likelihood classifier was used to generate a supervised classification into land-cover types and the vegetation types within the forested area. The preparation of training data sets used thematic maps of the area, and knowledge accruing from extensive personal field visits. Sample field plots were located at 30 different places in the Sanctuary for classification accuracy assessment. The Normalized Difference Vegetation Index (NDVI) was also computed from LISS-III satellite imagery. A digital elevation model (DEM) of the Sanctuary was generated using a GIS. The DEM was used to test the hypothesis that its joint use with the satellite data would increase classification accuracy. This proved to be the case. Bivariate correlation analysis was performed between spectral and DEM variables to cross-check the results. In the example used, that of the rugged terrain in mountainous parts of northeast India, such integration of satellite land-cover data and DEM information appears to be a necessity in improved land-cover mapping for resource planning and utilization.

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