New publication: Remote sensing solutions for monitoring species diversity as affected by invasive plants

New publication: Remote sensing solutions for monitoring species diversity as affected by invasive plants

A new published work featuring Hooman Latifi from Dept. of Remote Sensing and Siddhartha Khare from Indian Institute of Technology Roorkee presents a full remote sensing-based approach to assess the vegetation diversity across the areas affected and invaded by Lantana camara, an invasive plant species. The study comprises two main steps  utilizing  multi-source satellite earth observation data. The process starts with a supervised classification applied on ery high spatial resolution Pléiades 1A data, and continues with comparing Pléiades 1A, RapidEye and Landsat-8 OLI – assessed plant species diversities.

Schematic representation of plant diversity estimaiton by remote sensing approach

With detailed mathematical formulation combined with an straightforward methodology solely based on optical remote sensing data, the study is expected to add a new baseline to the existing studies on solutions for remote and rapid estimation of biodiversity attributes in mountaineuous forest areas. Further informaiton on the published paper can be retrieved here.

Bibliography:

Khare, S., Latifi, H., Ghosh, S.K., 2017. Multi-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data. Geocarto International . DOI: 10.1080/10106049.2017.1289562

 

Invited talk @ ForBioSensing conference

Invited talk @ ForBioSensing conference

Dr. Hooman Latifi from the Dept. of Remote Sensing held an invited speech on “Remote sensing-assisted mapping of bark beetle-induced tree mortality” at the first conference on comprehensive monitoring of stand dynamics in Białowieża forest supported by remote sensing (ForBioSensing) in Białowieża, Poland, from November 30th to December 2nd, 2016. The conference also hosted different generations and forestry-related disciplines from Polish and international research institutions.

y9djssfaThe ForBioSensing is a project funded by the LIFE program of the European Union, and its activities are focused on providing a comprehensive illustration of changes in forest stands and their dynamics (by using of several different time series of remote sensing data) and moving from the point scale monitoring (field measurements on sample plots) to the large scale area monitoring.

 

MSc thesis handed in on Analysis of Airborne LiDAR Data for Deriving Terrain and Surface Models

MSc thesis handed in on Analysis of Airborne LiDAR Data for Deriving Terrain and Surface Models

A M.Sc thesis by Raja Ram Aryal  at the University of Applied Sciences Stuttgart was recently written  under the supervision of Dr. Hooman Latifi and Prof. Michael Hahn. The thesis focused on a comparative study on the variations of an adaptive TIN ground filtering algorithm  to extract DTM from discrete LiDAR point cloud captured in leaf-off and full wave LiDAR point cloud collected in leaf-on conditions. In addition Analysis of Variance (ANOVA) type II was used to assess the influential factors that are related to DTM random error.  The Accuracy assessment of extracted DTMs was done  at local and landscape levels in heterogeneous forest stands of Bavarian Forest National Park. The DTM generated using mirror points in leaf-off returned less RMSE (0.844 m) than in leaf-on (0.988 m) conditions. Furthermore RMSE values of 0.916 m (leaf-off) and 1.078 m (leaf-on) were observed the local level analysis when no mirror points were used. However, RMSE value of ca. 0.5 m was observed at the landscape level, with leaf-off DTM showing slightly higher error than leaf-on DTM. The DTM error increased with increasing slope. Deciduous habitat was found to significantly influence DTM error in both leaf-off and leaf-on conditions. Interaction effects were mainly observed between slope and forest habitat type.

DTMs Extracted using denser point cloud LiDAR data (leaf-on condition) using mirror points  (left side) and without using mirror (right side)

DTMs Extracted using denser point cloud LiDAR data (leaf-on condition) using mirror points (left side) and without using mirror (right side)

MSc by Andrea Hess on deforestation drivers in Myanmar handed in

MSc by Andrea Hess on deforestation drivers in Myanmar handed in

Andrea_hess_landcover_MyanmarThe MSc by Andrea Hess “Deforestation in Myanmar – what can we say about causes?” has been handed in. It has been supervised by Peter Leimgruber (Smithsonian Conservation Biology Institute) and Martin Wegmann. Deforestation in the tropics is a global issue. Tropical forests are important not only for local livelihoods, but also for global climate regulation and biodiversity. Causes of forest loss have been extensively assessed on larger scales, but information on national and sub-
national level is often limited. In Myanmar, spatially explicit information on drivers of deforestation is almost non-existent. In this study I analyzed deforestation and factors affecting it in Myanmar, with focus on deforestation hotspots. I calculated change statistics from a Myanmar wide satellite imagery based forest cover change classification for deforestation on country level, the three states/regions Kachin, Sagaing and Tanintharyi and Homalin township. Using GAM, GLM and Random Forest models, I assessed the importance of eight factors in driving deforestation in Homalin township, namely elevation, slope, and distance to roads, waterways, populated places, protected areas, and reserved forests. To delineate areas under future deforestation risk, I also performed spatially explicit deforestation predictions. The results show a regionally distinct influence of mining in Kachin and Sagaing and plantation expansion in Tanintharyi on deforestation. In Homalin township, deforestation was strongly influenced by elevation, slope, distance to streams and populated places. The risk for  deforestation was predicted highest in low areas in proximity to waterways and towns and villages.

New RapidEye datasets granted from RESA platform of DLR/Black Bridge

New RapidEye datasets granted from RESA platform of DLR/Black Bridge

The Dept. of Remote Sensing recently applied for a time series of 12 RapidEye scenes to support a PhD thesis by Siddhartha Khare (Indian Institute of Technology Roorkee, India). The proposal is now accepted, and th PhD work will be supported by a dense time series of RapidEye scenes acquired in 2013 over dry forests in Lesser Himalayan region of India.

The PhD thesis which will use these processed datasets is entitled  “Object Based approaches for remote sensing-assisted assessment of forest biodiversity focusing on invasive species”, supervised and advised by Prof. S.K. Ghosh (IIT Roorkee) and Dr. Hooman Latifi (University of Würzburg). Additional multisource datasets to be used within the work include time series of Landsat 8 data and Cartosat 2-derived terrain model.

RESA already published this project in RESA project map 2015 which can be found online HERE. The project area is flagged under No. 120 (project number 184) in the map.

resa_india