Syed Adnan
https://orcid.org/0000-0003-3973-4776
Supervisors: Matti Maltamo and Ruben Valbuena
Address: Joensuu, Finland
Supervisors: Matti Maltamo and Ruben Valbuena
Address: Joensuu, Finland
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structure across the landscape, which can support sustainable forest management. A challenge arises in determining the
optimal spatial resolution that maximizes the stability and precision of GC estimates, which in turn depends on stand density
or ALS scan density. By subsampling different plot sizes within large field plots, we evaluated the optimal spatial resolution by
observing changes in GC estimation and in its correlation with ALS metrics. We found that plot size had greater effects than
either stand density or ALS scan density on the relationship between GC and ALS metrics. Uncertainty in GC estimates fell as plot
size increased. Correlation with ALS metrics showed convex curves with maxima at 250–450m2, which thus was considered the
optimal plot size and, consequently, the optimal spatial resolution. By thinning the density of the ALS point cloud, we deduced
that at least 3 points·m−2 were needed for reliable GC estimates. Many nationwide ALS scan densities are sparser than this, so may
be unreliable for GC estimation. Ours is a simple approach for evaluating the optimal spatial resolution in remote sensing
estimation of any forest attribute.
countries. The current study focused on farmer’s perception about climate change vulnerabilities, their adaptation
measures and relationship with the changing climatic records in the district Swat (Pakistan). The data
collected through household interviews and focus group discussions. Total 177 questionnaires administered
to sample households and 9 FGDs (Focus Group Discussions) in selected villages with the aim to understand
local perceptions about changing climate, its vulnerabilities and farmers adaptive measures toward climate
change in district Swat of Khyber Pakhtunkhwa, Pakistan. Additionally, vulnerability matrix was used to
identify livelihood resources that are vulnerable to climate change induced hazards. Results showed that primarily
deforestation and pollution contributed more to the perceived causes of climate change which resulted
frequent and sever floods or droughts and reduction in agricultural productivity and poor farm householders
with low farm holdings are more exposed to such extreme weather events. FGDs and interviews also showed
various indicators of causes, impacts and observations of climate change in the study area. Vulnerability assessment
revealed that cereals, vegetables and fruit orchards are vulnerable to both climatic and non-climatic
factors resulting reduction in crop production. Climatic record in the study area such as increase in mean
annual maximum (0.032 °C/year) and minimum temperatures (0.024 °C) and decrease in the mean annual
precipitation (-0.73 mm per year) favors the local community perceptions about climate changes. To offset
such worsening situation, future planning is vital to keep the natural resources intact while minimizing the
risks in the years ahead.
structure across the landscape, which can support sustainable forest management. A challenge arises in determining the
optimal spatial resolution that maximizes the stability and precision of GC estimates, which in turn depends on stand density
or ALS scan density. By subsampling different plot sizes within large field plots, we evaluated the optimal spatial resolution by
observing changes in GC estimation and in its correlation with ALS metrics. We found that plot size had greater effects than
either stand density or ALS scan density on the relationship between GC and ALS metrics. Uncertainty in GC estimates fell as plot
size increased. Correlation with ALS metrics showed convex curves with maxima at 250–450m2, which thus was considered the
optimal plot size and, consequently, the optimal spatial resolution. By thinning the density of the ALS point cloud, we deduced
that at least 3 points·m−2 were needed for reliable GC estimates. Many nationwide ALS scan densities are sparser than this, so may
be unreliable for GC estimation. Ours is a simple approach for evaluating the optimal spatial resolution in remote sensing
estimation of any forest attribute.
countries. The current study focused on farmer’s perception about climate change vulnerabilities, their adaptation
measures and relationship with the changing climatic records in the district Swat (Pakistan). The data
collected through household interviews and focus group discussions. Total 177 questionnaires administered
to sample households and 9 FGDs (Focus Group Discussions) in selected villages with the aim to understand
local perceptions about changing climate, its vulnerabilities and farmers adaptive measures toward climate
change in district Swat of Khyber Pakhtunkhwa, Pakistan. Additionally, vulnerability matrix was used to
identify livelihood resources that are vulnerable to climate change induced hazards. Results showed that primarily
deforestation and pollution contributed more to the perceived causes of climate change which resulted
frequent and sever floods or droughts and reduction in agricultural productivity and poor farm householders
with low farm holdings are more exposed to such extreme weather events. FGDs and interviews also showed
various indicators of causes, impacts and observations of climate change in the study area. Vulnerability assessment
revealed that cereals, vegetables and fruit orchards are vulnerable to both climatic and non-climatic
factors resulting reduction in crop production. Climatic record in the study area such as increase in mean
annual maximum (0.032 °C/year) and minimum temperatures (0.024 °C) and decrease in the mean annual
precipitation (-0.73 mm per year) favors the local community perceptions about climate changes. To offset
such worsening situation, future planning is vital to keep the natural resources intact while minimizing the
risks in the years ahead.