scholarly journals Landscape Changes in the Southern Coalfields of West Virginia: Multi-Level Intensity Analysis and Surface Mining Transitions in the Headwaters of the Coal River from 1976 to 2016

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 748
Author(s):  
Vincenzo Cribari ◽  
Michael P. Strager ◽  
Aaron E. Maxwell ◽  
Charles Yuill
Keyword(s):  
Land Cover ◽  
Surface Mining ◽  
Machine Learning Algorithms ◽  
Aerial Images ◽  
Intensity Analysis ◽  
Land Change ◽  
Appalachian Region ◽  
Ancillary Data ◽  
Change Analysis ◽  
Multi Level

This study analyzes land-cover transitions in the headwaters of the Big Coal River in the Central Appalachian Region of the US, from 1976 to 2016, where surface mining was found as the major driver of landscape change. The land-change analysis combined Multi-Level Intensity Analysis for two-time intervals (1976–1996, 1996–2016) with Difference Components, to differentiate suspected misclassification errors from actual changes. Two land cover classifications were obtained with segmentation analysis and machine learning algorithms from historical high-resolution aerial images and ancillary data. Intensity Analysis allowed for the inspection of transitions across five land cover (LC) classes and measure the degree of non-stationarity of land change patterns. Results found surface mining-related classes and their transitions, including the effects of reclamation processes on areas mined before the enactment of the Surface Mining Control and Reclamation Act (SMCRA, 1977). Results included changes in settlement distribution, low vegetation, water bodies, and forest class transitions. The findings can be applied to infer similar land-change processes in the more extensive Appalachian region where Mountain Top Removal (MTR) operations are widespread. The overall method can be used to address similar problems and inform landscape managers with detailed data to support land use alternatives and conservation in regions that experienced intense changes and are characterized by anthropogenic disturbances and novel ecosystems.

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 Multi-Level Land Cover Change Analysis in the Forest-Savannah Transition Zone of the Kintampo Municipality, Ghana

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Raymond Aabeyir ◽  
◽  
Wilson Agyei Agyare ◽  
Michael J. C. Weir ◽  
Stephen Adu-Bredu ◽  
...  
Keyword(s):  
Land Cover ◽  
Transition Zone ◽  
Land Cover Change ◽  
Change Analysis ◽  
Multi Level ◽  
Land Cover Change Analysis

scholarly journals ORTHORECTIFICATION OF A LARGE DATASET OF HISTORICAL AERIAL IMAGES: PROCEDURE AND PRECISION ASSESSMENT IN AN OPEN SOURCE ENVIRONMENT

2018 ◽  
Vol XLII-4/W8 ◽  
pp. 53-59 ◽  
Author(s):  
S. Gobbi ◽  
G. Maimeri ◽  
C. Tattoni ◽  
M. G. Cantiani ◽  
D. Rocchini ◽  
...  
Keyword(s):  
Land Cover ◽  
Digital Terrain Model ◽  
Image Resolution ◽  
Aerial Images ◽  
Change Analysis ◽  
Terrain Model ◽  
Digital Terrain ◽  
Ground Control Points ◽  
Minimum Number ◽  
Gis Environment

<p><strong>Abstract.</strong> The availability of data time series spanning a long period is crucial for landscape change analysis. A suitable dataset, both in terms of time span and information content, must be available for the use with a GIS.</p><p>In Italy, one of the most important historical source of land cover analysis is the GAI (Gruppo Aereo Italiano) photogrammetric survey (“Volo GAI”) commissioned in 1954 by the Italian national mapping agency, Istituto Geografico Militare Italiano (IGMI).</p><p>The survey covers the whole Italy, but so far only some Regions, namely Lombardia and Veneto, have carried out the image rectification and the successive analyses to map land cover and use.</p><p>This work describes the process of image orthorectification of the Volo GAI images for the Province of Trento (Provincia Autonoma di Trento).</p><p>Image orthorectification must be performed to transform the images in maps available for analysis. This procedure corrects the geometry according to the terrain surface described by a Digital Terrain Model (DTM) to create an image compatible with the cartographic projection in use.</p><p>To this end, the orthorectification modules available in GRASS GIS have been used, with the advantage of using the same GIS environment which will be used for the landscape analysis.</p><p> The dataset covering the whole Province contains almost 100 images, this paper presents the preliminary results of the orthorectification of a quarter of the images. A reduced dataset has been used to test the results obtained using different settings with respect to: digital image resolution, DTM resolution and number of Ground Control Points (GCPs) used for the external orientation.</p><p>These preliminary tests show that for the average quality of the Volo GAI images scan resolution beyond 600<span class="thinspace"></span>DPI and DTM resolution above 10<span class="thinspace"></span>m do not provide significant improvements for orthorectification images. The minimum number of GCPs to guarantee the requested accuracy can vary from image to image, depending on the image quality and recognizable features position, but it is usually in the 15&amp;ndash;20 points range.

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

scholarly journals Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

2021 ◽  
Vol 13 (3) ◽  
pp. 364
Author(s):  
Han Gao ◽  
Jinhui Guo ◽  
Peng Guo ◽  
Xiuwan Chen
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Training Data ◽  
Aerial Images ◽  
Rural Landscapes ◽  
Feature Representations ◽  
Object Based

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

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