Using an OBCD approach and Landsat TM data to detect harvesting on nonindustrial private property in Upper Michigan

Forest dynamics influence climate, biodiversity, and livelihoods at multiple scales, yet current resource policy addressing these dynamics is ineffective without reliable land use land cover change data. The collective impact of harvest decisions by many small forest owners can be substantial at the landscape scale, yet monitoring harvests and regrowth in these forests is challenging. Remote sensing is an obvious route to detect and monitor small-scale land use dynamics over large areas. Using an annual series of Landsat-5 Thematic Mapper (TM) images and a GIS shapefile of property boundaries, we identified units where harvests occurred from 2005 to 2011 using an Object-Based Change Detection (OBCD) approach. Percent of basal area harvested was verified using stand-level harvest data. Our method detected all harvests above 20% basal area removal in all forest types (northern hardwoods, mixed deciduous/coniferous, coniferous), on properties as small as 10 acres (0.4 ha; approximately four Landsat pixels). Our results had a resolution of about 10% basal area (that is, a selective harvest removal of 30% could be distinguished from one of 40%). Our method can be automated and used to measure annual harvest rates and intensities for large areas of the United States, providing critical information on land use transition

Evaluation of MODIS data for mapping oil slicks - the deepwater horizon oil spill case

Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral imagery is used for oil spills mapping as an integration to radar data. MODIS images of the northern Gulf of Mexico (USA) are analyzed to study the sea anomalies from visible to thermal infrared in order to detect a reported oil slick. A simple Fluorescence/Emissivity Index and RGB false color bands combination are applied to detect fluorescence and emissivity anomalies due to oil spills in particular sun glint conditions. A monitoring system of sea surface may be built using high temporal resolution imagery as MODIS data. Applying the proposed index and RGB bands combination, also suitable on night-time overpasses, it’s possible to further increase the availability of clouds free images using optical sensors

Supporting hydrocarbon exploration in new venture areas with optical remote sensing

In past time, exploration geologists mainly used Earth Observation systems for basin-wide analysis of gravimetry, magnetomerty, structural faults, lithology and land-cover. After two decades of research, nowadays multispectral and hyperspectral remote sensing represent a cutting-edge technology in the oil and gas industry. The application fields of optical remote sensing not only range from the monitoring of the oilfields to the evaluation of pollution, but also to hydrocarbon exploration. With reference to exploration activities, the observation of the territory from above into several different wavelengths is able to supply inestimable geophysical information related to the microseepage effect, different and complementary to tradition geophysical methods. It is almost accepted that many of the oil and gas fields leak light hydrocarbon gases along nearly vertical pathways and, thus, their detection with multi/hyperspectral imaging can support the detection of active petroleum systems. Indeed, several independent oil companies are using satellite and airborne observations for reducing exploration risks in new venture areas and for optimizing their seismic surveys. This study shows some examples of microseepage-related geochemical and geobotanical alterations detected in several different environments, from sandy desert to vegetated savannah, both using airborne hyperspectral data and multispectral satellite time series. All the examples analyze real onshore concession blocks in Africa and Asia and results clearly show a correlation between the spectral signals recorded form remote with in situ measures, well logs, the knowledge of the subsurface and the position of known oilfields

Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Land-cover/land-use thematic maps are a major need in urban and country planning. This paper demonstrates the capabilities of Object Based Image Analysis in multi-scale thematic classification of a complex sub-urban landscape with simultaneous presence of agricultural, residential and industrial areas using pan-sharpened very high resolution satellite imagery. The classification process was carried out step by step through the creation of different hierarchical segmentation levels and exploiting spectral, geometric and relational features. The framework returned a detailed land-cover/land-use map with a Cohen’s kappa coefficient of 0.84 and an overall accuracy of 85%

Advance EFFIS report on forest fires in Europe, Middle East and North Africa 2019

This report contains an anticipated annual summary of the fire season of 2019 with an analysis of fire danger and areas mapped in the European Forest Fire Information System (EFFIS). This report precedes that to be published in August/September 2020, which will include detailed reports prepared by countries in the Expert Group on Forest Fires.JRC.E.1-Disaster Risk Managemen

Advance EFFIS report on Forest Fires in Europe, Middle East and North Africa 2017

This report contains the annual summary of the fire season of 2017 with an analysis of fire danger and areas mapped in the European Forest Fire Information System (EFFIS).JRC.E.1-Disaster Risk Managemen

Advance EFFIS Report on Forest Fires in Europe, Middle East and North Africa 2018

This report contains an anticipated annual summary of the fire season of 2018 with an analysis of fire danger and areas mapped in the European Forest Fire Information System (EFFIS). This report precedes that to be published in August/September 2019, which will include detailed reports prepared by countries in the Expert Group on Forest Fires.JRC.E.1-Disaster Risk Managemen

Forest Fires in Europe, Middle East and North Africa 2018

This is the 19th issue of the EFFIS annual report on forest fires for the year 2018. This report is consolidated as highly appreciated documentation of the previous year's forest fires in Europe, Middle East and North Africa. In its different sections, the report includes information on the evolution of fire danger in the European and Mediterranean regions, the damage caused by fires and detailed description of the fire conditions during the 2017 fire campaign in the majority of countries in the EFFIS network. The chapter on national reporting gives an overview of the efforts undertaken at national and regional levels, and provides inspiration for countries exposed to forest fire risk.JRC.E.1-Disaster Risk Managemen

Forest Fires in Europe, Middle East and North Africa 2017

This report contains the annual summary of the fire season of 2017 with official figures provided by 31 contributing countries for the number of fires, burnt areas and fire prevention efforts, and the analysis of fire danger and areas mapped in the European Forest Fire Information System (EFFIS).JRC.E.1-Disaster Risk Managemen

Interoperability of Landsat and DMC imagery for continuous detection and quantification of nonindustrial forest harvests in the Western Upper Peninsula of Michigan, USA

The relationship between human land use and land cover change is critical to sustainable forest management. Land use decisions by small land managers aggregate into substantial land cover changes at landscape and regional scales. Land ownership across large portions of the Upper Great Lakes region is in considerable flux, as large timber industry tracts are split into many smaller non-industrial ownerships, and new owners prioritize amenity and non-timber forest values. Nonindustrial Private Forest (NIPF) owners also transfer their properties to younger generations or other NIPF owners with different management approaches and goals. Survey data on intended harvests and sales are available through the National Woodland Owner Survey (NWOS), run by the USDA Forest Service. However, the disparity between NIPF owner-stated plans to harvest, and what actually occurs, can be substantially different, especially if annual fluctuations in timber prices or general economic fluctuations cause NIPF owners to deviate from their stated management and ownership intentions. This reduces the NWOS\u27 utility. Remote sensing data have considerable value for identifying small scale harvests and, paired with ownership data at the parcel scale, can measure NIPF harvest rates as related to ownership change at a regional scale. Here we focus on the Western Upper Peninsula of Michigan (WUP) and the most recent decade to develop our methodology, using primarily Landsat images from 2003-2013. However, Landsat data series are characterized by gaps in coverage over long temporal and large spatial scales, and so a methodology to combine multiple remote sensing data sources is necessary for regional-scale land use/land cover change research. We filled these gaps by integrating the available Landsat time series with DMC imagery. We then combined these data with GIS overlays of the parcels and stand-level data on removed basal area (BA) during known harvesting events to develop a classification of harvest intensity for the WUP. Images taken during peak growing season were preferred to calculate NDVI and ΔNDVI, and in general for enhancing possible spectral changes. We classified the harvests as clear cut, selective harvesting or thinning using an object-based image analysis. In particular, we defined a clear cut a harvesting event in which ~90-100% BA is removed, commercial harvesting if ~50-80% BA is removed and thinning if ~20-40% BA removal. This work demonstrates that DMC images can effectively fill the Landsat data gap for the detection and quantification of harvesting events. Preliminary results show that the method is capable of identifying harvests down to ~20% BA removal. These results can then be used to monitor the accuracy of the NWOS, and to develop a probability estimate of harvest given either ownership change or changes in market conditions