Heinz Gallaun

Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa

Detecting and monitoring forest degradation in the tropics has implications for various fields of interest (biodiversity, emission calculations, self-sustenance of indigenous communities, timber exploitation). However, remote-sensing-based detection of forest degradation is difficult, as these subtle degradation signals are not easy to detect in the first place and quickly lost over time due to fast re-vegetation. To overcome these shortcomings, a time series analysis has been developed to map and monitor forest degradation over a longer period of time, with frequent updates based on Landsat data. This time series approach helps to reduce both the commission and the omission errors compared to, e.g., bi- or tri-temporal assessments. The approach involves a series of pre-processing steps, such as geometric and radiometric adjustments, followed by spectral mixture analysis and classification of spectral curves. The resulting pixel-based classification is then aggregated to degradation areas. The method was developed on a study site in Cameroon and applied to a second site in Central African Republic. For both areas, the results were finally evaluated against visual interpretation of very high-resolution optical imagery. Results show overall accuracies in both study sites above 85% for mapping degradation areas with the presented methods.

Remote Sensing Based Two-Stage Sampling for Accuracy Assessment and Area Estimation of Land Cover Changes

Land cover change processes are accelerating at the regional to global level. The remote sensing community has developed reliable and robust methods for wall-to-wall mapping of land cover changes; however, land cover changes often occur at rates below the mapping errors. In the current publication, we propose a cost-effective approach to complement wall-to-wall land cover change maps with a sampling approach, which is used for accuracy assessment and accurate estimation of areas undergoing land cover changes, including provision of confidence intervals. We propose a two-stage sampling approach in order to keep accuracy, efficiency, and effort of the estimations in balance. Stratification is applied in both stages in order to gain control over the sample size allocated to rare land cover change classes on the one hand and the cost constraints for very high resolution reference imagery on the other. Bootstrapping is used to complement the accuracy measures and the area estimates with confidence intervals. The area estimates and verification estimations rely on a high quality visual interpretation of the sampling units based on time series of satellite imagery. To demonstrate the cost-effective operational applicability of the approach we applied it for assessment of deforestation in an area characterized by frequent cloud cover and very low change rate in the Republic of Congo, which makes accurate deforestation monitoring particularly challenging.

Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review

Purpose of ReviewThis paper presents a review of the current state of the art in remote sensing-based monitoring of forest disturbances and forest degradation from optical Earth Observation data. Part one comprises an overview and tabular description of currently available optical remote sensing sensors, which can be used for forest disturbance and degradation mapping. Part two reviews the two main categories of existing mapping approaches: first, classical image-to-image change detection and second, time series analysis.Recent FindingsWith the launch of the Sentinel-2a satellite and available Landsat imagery, time series analysis has become the most promising but also most demanding category of degradation mapping approaches. Four time series classification methods are distinguished. The methods are explained and their benefits and drawbacks are discussed. A separate chapter presents a number of recent forest degradation mapping studies for two different ecosystems: temperate forests with a geographical focus on Europe and tropical forests with a geographical focus on Africa.SummaryThe review revealed that a wide variety of methods for the detection of forest degradation is already available. Today, the main challenge is to transfer these approaches to high-resolution time series data from multiple sensors. Future research should also focus on the classification of disturbance types and the development of robust up-scalable methods to enable near real-time disturbance mapping in support of operational reactive measures.

Satellite-based forest monitoring: spatial and temporal forecast of growing index and short-wave infrared band

For detecting anomalies or interventions in the field of forest monitoring we propose an approach based on the spatial and temporal forecast of satellite time series data. For each pixel of the satellite image three different types of forecasts are provided, namely spatial, temporal and combined spatio-temporal forecast. Spatial forecast means that a clustering algorithm is used to group the time series data based on the features normalised difference vegetation index (NDVI) and the short-wave infrared band (SWIR). For estimation of the typical temporal trajectory of the NDVI and SWIR during the vegetation period of each spatial cluster, we apply several methods of functional data analysis including functional principal component analysis, and a novel form of random regression forests with online learning (streaming) capability. The temporal forecast is carried out by means of functional time series analysis and an autoregressive integrated moving average model. The combination of the temporal forecasts, which is based on the past of the considered pixel, and spatial forecasts, which is based on highly correlated pixels within one cluster and their past, is performed by functional data analysis, and a variant of random regression forests adapted to online learning capabilities. For evaluation of the methods, the approaches are applied to a study area in Germany for monitoring forest damages caused by wind-storm, and to a study area in Spain for monitoring forest fires.

Land Information System Austria (LISA)

The project Land Information System Austria (LISA) demonstrates the feasibility of an Austrian-wide homogeneous land cover and land use monitoring solution. State of the art technologies in remote sensing and GIS combined with the effective integration of national spatial data infrastructure (orthophotos, airborne laser scanning data and other countrywide available geodata) are used in combination with European GMES/Copernicus data infrastructure to meet the requirements of a modern land monitoring system, specified by governmental institutions on state, provincial and municipality level. The conceptual basis consists of an object oriented data model that covers the two main land monitoring issues separately: land cover and land use. Whereas land cover is derived solely based on remote sensing data, the land use data are assessed as synoptic view from various thematic databases (spatial zoning plans, IACS, nature conservation, road network etc.) and land cover classifications. The applied technical approach is the result of various research activities. Currently, the countrywide realization of LISA in form of an administrative national collaboration across various hierarchical administration levels (regional and national) as well as across various thematic contributions (cadastral mapping, spatial planning, agriculture, etc.) is discussed.

The assessment of forest parameters by combined LiDAR and satellite data over Alpine regions – EUFODOS Implementation in Austria / Hodnotenie parametrov lesa kombináciou LIDAR-u a satelitných údajov v alpských regiónoch – implementácia systému EUFODOS v Rakúsku

Abstract Regional authorities require detailed and georeferenced information on the status of forests to ensure a sustainable forest management. One of the objectives in the FP7 project EUFODOS was the development of an operational service based on airborne laser scanning and satellite data in order to derive forest parameters relevant for the management of protective forests in the Alps. The estimated parameters are forest type, stem number, height of upper layer, mean height and timber volume. RapidEye imagery was used to derive coniferous and broadleaf forest classes using a logistic regression-based method. After the generation of a normalised Digital Surface Model and a forest mask, the forest area was segmented into homogeneous polygons, tree tops were detected, and various forest parameters are calculated. The accuracy of such an assessment was comparable with some previous studies, and the R-square between the estimated and measured values was 0.69 for tree top detection, 0.82 for upper height and 0.84 for mean height. For the calculation of timber volume, the R² for modelling is 0.82, for validation with an independent set of field plots, the R² is 0.71. The results have been successfully integrated into the regional forestry GIS and are used in forest management. Abstrakt Regionálne plánovanie zabezpečujúce trvale udržateľný manažment lesa vyžaduje detailné a georeferencované informácie o stave lesov. Jedným z cieľov projektu EUFODOS (projekt 7. RP EÚ) bolo vyvinúť operatívnu službu využívajúcu údaje leteckého laserového skenovania v kombinácii so satelitnými údajmi, pomocou ktorých sú odvodené informácie potrebné pre obhospodarovanie ochranných lesov v Alpách. Zisťované parametre sú lesný typ, počet stromov, výška hornej korunovej vrstvy, priemerná výška a kmeňová zásoba. Použilo sa snímkovanie systémom RapidEye pre odvodenie tried ihličnanov a listnáčov s použitím logistického regresného modelu. Po vygenerovaní normalizovaného digitálneho modelu povrchu a masky lesa sa plocha lesa segmentovala do homogénnych polygónov, identifikovali sa vrcholce stromov a vypočítali sa požadované porastové charakteristiky. Presnosť uvedených odhadov bola porovnateľná s predošlými štúdiami - R2 medzi odhadovanými a meranými hodnotami pozícií vrcholcov stromov bol 0,69, pre hornú výšku 0,82 a pre priemernú výšku porastu 0,84. Pri výpočte objemu dreva bol R2 príslušného modelu 0,82. Pri validácii s nezávislým súborom plôch bola dosiahnutá hodnota R2 0,71. Prezentované výsledky sa úspešne integrovali do regionálnych lesníckych GIS sú využívané pri manažmente lesa.