Supplementary material to "Drought effects on leaf fall, leaf flushing and stem growth in Neotropical forest; reconciling remote sensing data and field observations"

Abstract. Large amounts of carbon flow through tropical ecosystems every year, from which a part is sequestered in biomass through tree growth. However, the effects of ongoing warming and drying on tree growth and carbon sequestration in tropical forest is still highly uncertain. Field observations are sparse and limited to a few sites while remote sensing analysis shows diverging growth responses to past droughts that cannot be interpreted with confidence. To reconcile data from field observations and remote sensing, we collated in situ measurements of stem growth and leaf litterfall from inventory plots across the Neotropics. This data was used to train two machine learning models and to evaluate model performance on reproducing stem growth and litterfall rates. The models utilized multiple climatological variables and other geospatial datasets as explanatory variables. The output consisted of monthly estimates of leaf litterfall (R2 = 0.67, NRMSE = 9.5 %) and stem growth (R2 = 0.51, NRMSE = 11.2 %) across the neotropics from 1982 to 2019 at a high spatial resolution (0.1°). Modelled time series allowed to assess the impacts of the 2005 and 2015 droughts in the Amazon basin on regional scales. Both droughts were estimated to have caused widespread declines in stem growth (−0.6σ ~ −1.8σ), coinciding with enhanced leaf fall (+0.7σ ~ +0.9σ). Regions in the Amazon basin that flushed leaves at the onset of both droughts (+1.1σ ~ +1.9σ), showed positive anomalies in remotely sensed enhanced vegetation index, while sun-induced fluorescence and vegetation optical depth were reduced. The previously observed counterintuitive response of canopy green-up during drought in the Amazon basin detected by many remote sensing analyses can therefore be explained by enhanced leaf flushing at the onset of a drought. The long-term estimates of leaf litterfall and stem growth point to a decline of stem growth and a simultaneous but weaker increase in leaf litterfall in the Amazon basin since 1982 that is not observed in long-term inventory plots. These trends are associated with increased warming and drying of the Amazonian climate.

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Supplementary material to "Spatial variation and linkages of soil and vegetation in the Siberian Arctic tundra – coupling field observations with remote sensing data"

10.5194/bg-2017-569-supplement ◽

2018 ◽

Author(s):

Juha Mikola ◽

Tarmo Virtanen ◽

Maiju Linkosalmi ◽

Emmi Vähä ◽

Johanna Nyman ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Variation ◽

Remote Sensing Data ◽

Arctic Tundra ◽

Field Observations ◽

Coupling Field ◽

Sensing Data ◽

Siberian Arctic ◽

Supplementary Material

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Drought effects on leaf fall, leaf flushing and stem growth in the Amazon forest: reconciling remote sensing data and field observations

Biogeosciences ◽

10.5194/bg-18-4445-2021 ◽

2021 ◽

Vol 18 (14) ◽

pp. 4445-4472

Author(s):

Thomas Janssen ◽

Ype van der Velde ◽

Florian Hofhansl ◽

Sebastiaan Luyssaert ◽

Kim Naudts ◽

...

Keyword(s):

Remote Sensing ◽

Tree Growth ◽

Amazon Basin ◽

Stem Growth ◽

Simultaneous Increase ◽

Amazon Forest ◽

Field Observations ◽

Leaf Fall ◽

Leaf Litterfall ◽

Leaf Flushing

Abstract. Large amounts of carbon flow through tropical ecosystems every year, from which a part is sequestered in biomass through tree growth. However, the effects of ongoing warming and drying on tree growth and carbon sequestration in tropical forest is still highly uncertain. Field observations are sparse and limited to a few sites, while remote sensing analysis shows diverging growth responses to past droughts that cannot be interpreted with confidence. To reconcile data from field observations and remote sensing, we collated in situ measurements of stem growth and leaf litterfall from inventory plots across the Amazon region and other neotropical ecosystems. These data were used to train two machine-learning models and to evaluate model performance on reproducing stem growth and litterfall rates. The models utilized multiple climatological variables and other geospatial datasets (terrain, soil and vegetation properties) as explanatory variables. The output consisted of monthly estimates of leaf litterfall (R2= 0.71, NRMSE = 9.4 %) and stem growth (R2= 0.54, NRMSE = 10.6 %) across the neotropics from 1982 to 2019 at a high spatial resolution (0.1∘). Modelled time series allow us to assess the impacts of the 2005 and 2015 droughts in the Amazon basin on regional scales. The more severe 2015 drought was estimated to have caused widespread declines in stem growth (−1.8σ), coinciding with enhanced leaf fall (+1.4σ), which were only locally apparent in 2005. Regions in the Amazon basin that flushed leaves at the onset of both droughts (+0.9σ∼+2.0σ) showed positive anomalies in remotely sensed enhanced vegetation index, while sun-induced fluorescence and vegetation optical depth were reduced. The previously observed counterintuitive response of canopy green-up during drought in the Amazon basin detected by many remote sensing analyses can therefore be a result of enhanced leaf flushing at the onset of a drought. The long-term estimates of leaf litterfall and stem growth point to a decline in stem growth and a simultaneous increase in leaf litterfall in the Amazon basin since 1982. These trends are associated with increased warming and drying of the Amazonian climate and could point to a further decline in the Amazon carbon sink strength.

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Supplementary material to "The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets"

10.5194/hess-2016-671-supplement ◽

2017 ◽

Author(s):

Clara Linés ◽

Micha Werner ◽

Wim Bastiaanssen

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Data Sets ◽

Ebro Basin ◽

Sensing Data ◽

Supplementary Material

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Methods of calculating landslide volume using remote sensing data

E3S Web of Conferences ◽

10.1051/e3sconf/202014902009 ◽

2020 ◽

Vol 149 ◽

pp. 02009

Author(s):

Maira Razakova ◽

Alexandr Kuzmin ◽

Igor Fedorov ◽

Rustam Yergaliev ◽

Zharas Ainakulov

Keyword(s):

Remote Sensing ◽

Sea Level ◽

Remote Sensing Data ◽

Field Studies ◽

Aerial Photographs ◽

Field Observations ◽

Sensing Data ◽

Quantitative Estimates ◽

Landslide Volume ◽

Engineering Protection

The paper considers the issues of calculating the volume of the landslide from remote sensing data. The main methods of obtaining information during research are field observations. The most important results of field studies are quantitative estimates, such as the volume of the embankment resulting from a landslide, morphometric indicators, etc. The study of a remote and remote object was carried out by remote methods using aerial photographs in the Ile Alatau foothills at 1,600 meters above sea level. The obtained materials from the mudflow survey will be useful in developing solutions to mitigate the effects of disasters and in the design of measures for engineering protection from landslides.

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Visual characteristics of landscapes and methods for their assessment on GIS (Belogradchik rocks (Bulgaria) as an example)

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/10.35595/2414-9179-2021-2-27-191-204 ◽

2021 ◽

Vol 27 (2) ◽

pp. 191-204

Author(s):

Elina Sheremet ◽

Natalia Kalutskova ◽

Vladimir Dekhnich

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Visual Quality ◽

Aerial Photographs ◽

Main Task ◽

Field Observations ◽

North West ◽

Sensing Data ◽

Visual Characteristics

Visual characteristics of landscapes are important factors for the assessment of tourist and recreational potential of territories. At present, a number of methodological approaches are applied to assess the visual characteristics of landscapes. They can be divided into traditional, associated exclusively with field research, and innovative, which is based on remote sensing data (RSD) of high spatial resolution and GIS technologies. Field assessment of the visual quality of landscapes utilizes a system of numerous elementary indicators to minimize subjectivity of assessment. They are conducted within separate areas or touristic routes. In its turn, modern GIS and high quality of remote sensing data allow assessing of most indicators of the visual quality of landscapes for any observation point on the entire territory. The main task of our research is to verify the results of automated processing of ultra-high resolution aerial photographs obtained from unmanned aerial vehicles (UAV) by field observations on a touristic route. The research was carried out on the territory of the “Belogradchik Rocks” Geopark (North-West Bulgaria). In our study, we estimated 4 out of 28 aesthetic indicators—the amount of mountain peaks visible from a site, the amount of mountain peaks on the skyline, the percentage of the forest-covered area, and the amount of open spaces in the wooded landscape. The obtained results confirmed that our approach allows calculating these aesthetic indicators at an accuracy level comparable to field observations.

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Visual characteristics of landscapes and methods for their assessment on GIS (Belogradchik rocks (Bulgaria) as an example)

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/2414-9179-2021-2-27-191-204 ◽

2021 ◽

Vol 27 (2) ◽

pp. 191-204

Author(s):

Elina Sheremet ◽

Natalia Kalutskova ◽

Vladimir Dekhnich

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Visual Quality ◽

Aerial Photographs ◽

Main Task ◽

Field Observations ◽

North West ◽

Sensing Data ◽

Visual Characteristics

Visual characteristics of landscapes are important factors for the assessment of tourist and recreational potential of territories. At present, a number of methodological approaches are applied to assess the visual characteristics of landscapes. They can be divided into traditional, associated exclusively with field research, and innovative, which is based on remote sensing data (RSD) of high spatial resolution and GIS technologies. Field assessment of the visual quality of landscapes utilizes a system of numerous elementary indicators to minimize subjectivity of assessment. They are conducted within separate areas or touristic routes. In its turn, modern GIS and high quality of remote sensing data allow assessing of most indicators of the visual quality of landscapes for any observation point on the entire territory. The main task of our research is to verify the results of automated processing of ultra-high resolution aerial photographs obtained from unmanned aerial vehicles (UAV) by field observations on a touristic route. The research was carried out on the territory of the “Belogradchik Rocks” Geopark (North-West Bulgaria). In our study, we estimated 4 out of 28 aesthetic indicators—the amount of mountain peaks visible from a site, the amount of mountain peaks on the skyline, the percentage of the forest-covered area, and the amount of open spaces in the wooded landscape. The obtained results confirmed that our approach allows calculating these aesthetic indicators at an accuracy level comparable to field observations.

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