A generalized regression-based unmixing model for mapping forest cover fractions throughout three decades of Landsat data

2020 ◽  
Vol 240 ◽  
pp. 111691 ◽  
Author(s):  
Cornelius Senf ◽  
Josef Laštovička ◽  
Akpona Okujeni ◽  
Marco Heurich ◽  
Sebastian van der Linden
2022 ◽  
Vol 14 (2) ◽  
pp. 322
Author(s):  
Dmitry V. Ershov ◽  
Egor A. Gavrilyuk ◽  
Natalia V. Koroleva ◽  
Elena I. Belova ◽  
Elena V. Tikhonova ◽  
...  

Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels.


2014 ◽  
Vol 151 ◽  
pp. 138-148 ◽  
Author(s):  
Christophe Sannier ◽  
Ronald E. McRoberts ◽  
Louis-Vincent Fichet ◽  
Etienne Massard K. Makaga

2008 ◽  
Vol 112 (5) ◽  
pp. 2495-2513 ◽  
Author(s):  
Matthew C. Hansen ◽  
David P. Roy ◽  
Erik Lindquist ◽  
Bernard Adusei ◽  
Christopher O. Justice ◽  
...  

Author(s):  
S. Turubanova ◽  
P. Potapov ◽  
A. Krylov ◽  
A. Tyukavina ◽  
J. L. McCarty ◽  
...  

Dramatic political and economic changes in Eastern European countries following the dissolution of the “Eastern Bloc” and the collapse of the Soviet Union greatly affected land-cover and land-use trends. In particular, changes in forest cover dynamics may be attributed to the collapse of the planned economy, agricultural land abandonment, economy liberalization, and market conditions. However, changes in forest cover are hard to quantify given inconsistent forest statistics collected by different countries over the last 30 years. The objective of our research was to consistently quantify forest cover change across Eastern Europe from 1985 until 2012 using the complete Landsat data archive. We developed an algorithm for processing imagery from different Landsat platforms and sensors (TM and ETM+), aggregating these images into a common set of multi-temporal metrics, and mapping annual gross forest cover loss and decadal gross forest cover gain. Our results show that forest cover area increased from 1985 to 2012 by 4.7% across the region. Average annual gross forest cover loss was 0.41% of total forest cover area, with a statistically significant increase from 1985 to 2012. Most forest disturbance recovered fast, with only 12% of the areas of forest loss prior to 1995 not being recovered by 2012. Timber harvesting was the main cause of forest loss. Logging area declined after the collapse of socialism in the late 1980s, increased in the early 2000s, and decreased in most countries after 2007 due to the global economic crisis. By 2012, Central and Baltic Eastern European countries showed higher logging rates compared to their Western neighbours. Comparing our results with official forest cover and change estimates showed agreement in total forest area for year 2010, but with substantial disagreement between Landsat-based and official net forest cover area change. Landsat-based logging areas exhibit strong relationship with reported roundwood production at national scale. Our results allow national and sub-national level analysis of forest cover extent, change, and logging intensity and are available on-line as a baseline for further analyses of forest dynamics and its drivers.


Forests ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 23 ◽  
Author(s):  
Fernando Aguilar ◽  
Abderrahim Nemmaoui ◽  
Manuel Aguilar ◽  
Mimoun Chourak ◽  
Yassine Zarhloule ◽  
...  

2008 ◽  
Vol 32 (1) ◽  
pp. 12-20 ◽  
Author(s):  
Dan Unger ◽  
James Kroll ◽  
I-Kuai Hung ◽  
Jeffrey Williams ◽  
Dean Coble ◽  
...  

Abstract A standardized remote sensing methodology was evaluated for its use in quantifying the forested resources of the state of Texas in a timely and cost-effective manner. Landsat data from 2002 were used to create a land cover base map encompassing a four-county study area in East Texas. Site-specific and non-site-specific accuracy assessments of the classified map indicate that overall the 2002 base map accuracy of 72.78% was within acceptable remote sensing standards for Landsat data and that forest cover types derived from 2002, 1987, and 1980 Landsat data were within 4.4, 0.5, and 7.4% agreement with Forest Inventory and Analysis Program data collected in 1988, 1988, and 1980 respectively. A classified image representing five age class distributions for all forest cover types, derived through a Boolean manipulation of forest cover type maps from 2002, 1997, 1992, 1987, 1984, 1980, and 1974, indicates that overall map accuracy for age class distributions based on 30-m Landsat data from 1974 through 2002 was 58.69%. Overall, results indicate that remote sensing in conjunction with ground truthing can accurately quantify forest composition and age distributions using standardized and readily available data.


Sign in / Sign up

Export Citation Format

Share Document