TY  - JOUR
A1  - Thonfeld, Frank
A1  - Gessner, Ursula
A1  - Holzwarth, Stefanie
A1  - Kriese, Jennifer
A1  - da Ponte, Emmanuel
A1  - Huth, Juliane
A1  - Kuenzer, Claudia
T1  - A first assessment of canopy cover loss in Germany's forests after the 2018–2020 drought years
JF  - Remote Sensing
N2  - Central Europe was hit by several unusually strong periods of drought and heat between 2018 and 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast forest areas in Germany. We present the first assessment of canopy cover loss in Germany for the period of January 2018–April 2021. Our approach makes use of dense Sentinel-2 and Landsat-8 time-series data. We computed the disturbance index (DI) from the tasseled cap components brightness, greenness, and wetness. Using quantiles, we generated monthly DI composites and calculated anomalies in a reference period (2017). From the resulting map, we calculated the canopy cover loss statistics for administrative entities. Our results show a canopy cover loss of 501,000 ha for Germany, with large regional differences. The losses were largest in central Germany and reached up to two-thirds of coniferous forest loss in some districts. Our map has high spatial (10 m) and temporal (monthly) resolution and can be updated at any time.
KW  - forest
KW  - canopy cover loss
KW  - drought
KW  - Sentinel-2
KW  - Landsat-8
KW  - disturbance index
KW  - time series
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-255306
SN  - 2072-4292
VL  - 14
IS  - 3
ER  - 
TY  - JOUR
A1  - Halbgewachs, Magdalena
A1  - Wegmann, Martin
A1  - da Ponte, Emmanuel
T1  - A spectral mixture analysis and landscape metrics based framework for monitoring spatiotemporal forest cover changes: a case study in Mato Grosso, Brazil
JF  - Remote Sensing
N2  - An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3% of the number of patches and a decrease of the mean patch area of 86.1% for the selected time period, resulting in altered habitats for flora and fauna.
KW  - Landsat
KW  - Google Earth Engine
KW  - spectral mixture analysis
KW  - deforestation
KW  - forest degradation
KW  - landscape metrics
KW  - forest fragmentaion
KW  - Mato Grosso
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-270644
SN  - 2072-4292
VL  - 14
IS  - 8
ER  -