Johannes Reiche

Wageningen University & Research | WUR · Laboratory of Geo-Information Science and Remote Sensing

Fire use for land management is widespread in natural tropical and plantation forests, causing major environmental and economic damage. Recent studies combining active fire alerts with annual forest-cover loss information identified fire-related forest-cover loss areas well, but do not provide detailed understanding on how fires and forest-cover lo...

Combining observations from multiple optical and synthetic aperture radar (SAR) satellites can provide temporally dense and regular information at medium resolution scale, independently of weather, season, and location. This has the potential to improve near real-time deforestation monitoring in dry tropical regions, where traditional optical only...

Implementation of policies to reduce forest loss challenges the Earth observation community to improve forest monitoring. An important avenue for progress is the use of new satellite missions and the combining of optical and synthetic aperture radar sensor data.

The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use as...

During disaster response, clouds or darkness can prevent the use of optical images for detecting the consequences of natural disasters, including landslides. In these situations, radar images can be used to detect changes more rapidly. However, Synthetic Aperture Radar (SAR) backscatter intensity images are underutilized for landslide detection. Un...

More than half a decade after the launch of the Sentinel-1A C-band SAR satellite, several near real-time forest disturbances detection systems based on backscattering time series analysis have been developed and made operational. Every system has its own particular approach to change detection. Here, we have compared the performance of the main SAR...

Handling multiple scales efficiently is one avenue for processing big remote sensing imagery data. Unfortunately, imagery is also affected by the infamous modifiable areal unit problem, which creates unpredictable errors at different scales. We developed a downsampling method that attempts to keep the data distribution in a downsampled image consta...

Community-based monitoring (CBM) is one of the- most sustainable ways of establishing a national forest monitoring system for successful Reduce Emissions from Deforestation and Forest Degradation (REDD+) implementation. In this research, we present the details of the National Forest Conservation Program (PNCB—Programa Nacional de Conservación de Bo...

An increase in the frequency and severity of disturbances (such as forest fires) is putting pressure on the resilience of the Amazon tropical forest; potentially leading to reduced ability to recover and to maintain a functioning forest ecosystem. Dense and long-term satellite time series approaches provide a largely untapped data source for charac...

Analysis of forest disturbance patterns in relation to precipitation seasonality is important for understanding African tropical forest dynamics under changing climate conditions and different levels of human activities. Newly available radar-based forest disturbance information now enables an investigation of the intra-annual relationship between...

Monitoring progress in the Glasgow ‘Declaration on Forests’ remains impossible without open sharing of data. Three actions are required if this declaration is to succeed.

Two novel satellite LiDAR missions —GEDI and ICESat-2— are currently operational and combined provide near-global measurements of forest height and structure. Such data underpin a new era of large-area approaches for measuring forest height in regrowing forests of different ages and assessing associated regrowth rates. Two LiDAR missions further al...

Spatially explicit monitoring of tropical forest aboveground carbon is an important prerequisite for better targeting and assessing forest conservation efforts and more transparent reporting of carbon losses. Here, we combine near-real-time forest disturbance alerts based on all-weather radar data with aboveground carbon stocks to provide carbon lo...

Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model (WCM) to account for backscattering from soil mois...

Remote Sensing-based global Forest/Non-Forest (FNF) masks have shown large inaccuracies in tropical wetland areas. This limits their applications for deforestation monitoring and alerting in which they are used as a baseline for mapping new deforestation. In radar-based deforestation monitoring, for example, moisture dynamics in unmasked non-forest...

Like many other tropical countries, the Philippines has suffered from decades of deforestation and forest degradation during and even after the logging era. Several open access Earth Observation (EO) products are increasingly being used for deforestation analysis in support of national and international initiatives and policymaking on forest conser...

Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Senti...

Tropical forest disturbances linked to fire usage cause large amounts of greenhouse gas (GHG) emissions and environmental damages. Supporting precise GHG estimations and counteracting illegal fire usages in the tropics require timely and thematically detailed large-scale information on fire-related forest disturbances. Multi-sensor optical and rada...

A polarimetric synthetic aperture radar (PolSAR) sensor is able to collect images in different polarization states, making it a rich source of information for target characterization. PolSAR images are inherently affected by speckle. Therefore, before deriving ad hoc products from the data, the polarimetric covariance matrix needs to be estimated...

At present, accessing and processing Earth Observation (EO) data on different cloud platforms requires users to exercise distinct communication strategies as each backend platform is designed differently. The openEO API (Application Programming Interface) standardises EO-related contracts between local clients (R, Python, and JavaScript) and cloud...

A Polarimetric Synthetic Aperture Radar (PolSAR) sensor is able to collect images in different polarization states, making it a rich source of information for target characterization. PolSAR images are inherently affected by speckle. Therefore, before deriving ad- hoc products from the data, the polarimetric covariance matrix needs to be estimated...

A humid tropical forest disturbance alert using Sentinel-1 radar data is presented for the Congo Basin. Radar satellite signals can penetrate through clouds, allowing Sentinel-1 to provide gap-free observations for the tropics consistently every 6-12 days at 10 m spatial scale. In the densely cloud covered Congo Basin, this represents a major advan...

Deep learning (DL) has proven to be a suitable approach for despeckling synthetic aperture radar (SAR) images. So far, most DL models are trained to reduce speckle that follows a particular distribution, either using simulated noise or a specific set of real SAR images, limiting the applicability of these methods for real SAR images with unknown no...

Deep learning (DL) has proven to be a suitable approach for despeckling synthetic aperture radar (SAR) images. So far, most DL models are trained to reduce speckle that follows a particular distribution, either using simulated noise or a specific set of real SAR images, limiting the applicability of these methods for real SAR images with unknown no...

This article provides an angular-based radiometric slope correction routine for Sentinel-1 SAR imagery on the Google Earth Engine platform. Two established physical reference models are implemented. The first model is optimised for vegetation applications by assuming volume scattering on the ground. The second model is optimised for surface scatter...

Climate change is a threat to many high-latitude regions. Changing patterns in precipitation intensity and increasing glacial ablation during spring and summer have major influence on river dynamics and the risk of widespread flooding. To monitor these rapid events, more frequent discharge observations are necessary. Having access to near-daily sat...

Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and-2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a u...

The achievement of international goals and national commitments related to forest conservation and management, climate change, and sustainable development requires credible, accurate, and reliable monitoring of stocks and changes in forest biomass and carbon. Most prominently, the Paris Agreement on Climate Change and the United Nations’ Sustainabl...

Obtaining information on vertical forest structure requires detailed data acquisition and analysis which is often performed at a plot level. With the growing availability of multi-modal satellite remote sensing (SRS) datasets, their usability towards forest structure estimation is increasing. We assessed the relationship of PlanetScope-, Sentinel-2...

Current research on forest change monitoring using medium spatial resolution Landsat satellite data aims for accurate and timely detection of forest disturbances. However, producing forest disturbance maps that have both high spatial and temporal accuracy is still challenging because of the trade-off between spatial and temporal accuracy. Timely de...

The chapter discusses the potential use of passive optical sensors, RADAR and LiDAR technology for measuring vegetation height to support the monitoring of the EBV "ecosystem structure."

The Essential Biodiversity Variables (EBV) concept proposed by GEO BON, Space Agencies, and the Earth Observation research community at large aims to support efforts for biodiversity monitoring. GOFC-GOLD and GEO BON propose a new sourcebook to promote the best operational monitoring practices for the relevant EBVs based on scientific literature, a...

Authors In addition to the core editors, a number of international experts in remote sensing, and biodiversity field measurement have contributed to the development of the Sourcebook and are thankfully acknowledged for their support. This Sourcebook is the result of a joint voluntary effort from more than 70 contributing authors from different inst...

Package to apply the Multi-sensor Time series Fusion (MulTiFuse) approach for fusing univariate time series. The MulTiFuse approach has been published in Reiche et al. 2015, where it was used to fuse univariate Landsat NDVI and ALOS PALSAR L-band SAR backscatter time series. The fused time series were used to detect deforestation. Reiche, J., Verb...

To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian...

Many tropical countries suffer from persistent cloud cover inhibiting spatially consistent reporting of deforestation and forest degradation for REDD+. Data gaps remain even when compositing Landsat-like optical satellite imagery over one or two years. Instead, medium resolution SAR is capable of providing reliable deforestation information but sho...

Envisat Advanced Synthetic Aperture Radar (ASAR) dual-polarization data are shown to be effective for regional forest monitoring. To this scope, an automatic SAR image preprocessing procedure was developed using SRTM DEM and Landsat TM image for geocoding in rugged terrain and smooth terrain areas, respectively. An object-oriented forest and non-fo...

The present study proposes a concept and a joint processing chain for integrating the ESA's upcoming Sentinel 1-3 satellites to address the needs from a series of users. The framework considers the advantages of the single Sentinel systems in terms of the spatial, temporal and thematic detail for the generation of a global land cover dynamics and c...

Intensive grazing leads to land degradation and desertification of grassland ecosystems followed by serious environmental and social problems. The Xilingol steppe grassland in Inner Mongolia, China, which has been a sink area for dust for centuries, is strongly affected by the negative effects of overgrazing and wind erosion. The aim of this study...

Envisat Advanced Synthetic Aperture Radar (ASAR) dual-polarization data are shown to be effective for regional forest monitoring. To this scope, an automatic SAR image preprocessing procedure was developed using SRTM DEM and Land-sat TM image for geocoding in rugged terrain and smooth terrain areas, respectively. An object-oriented forest and non-f...

One of the objectives of Forest Dragon 2 project is the generation of a forest cover change map between 1990s and 2000s for Northeast China based on ERS-1/2 tandem data and ENVISAT ASAR data. For the 1990s' map, an automatic and seasonal-adaptive retrieval of forest biomass method was used to produce a forest biomass map based on the ERS tandem int...

The use of spaceborne Synthetic Aperture Radar (SAR) is fundamental for forest mapping and monitoring for the northern hemisphere because of non-adequate solar illumination for most of the year around and the sensitivity of the radar observables to forest structure. Past and current spaceborne SAR missions have not been designed specifically for fo...

In terms of climate change analysis, information on large area forest cover distribution becomes increasingly important for studying terrestrial carbon cycle changes and its human impacts. Within the FOREST DRAGON 1 project large-area forest growing stock volume maps of Northeast and Southeast China based on ERS-1/2 tandem coherence data have been...

The creation of large-area forest growing stock volume (GSV) maps of Northeast and Southeast China, based on ERS-1/2 tandem coherence data from the mid 1990s, was aim of the Forest DRAGON 1 project. The evaluation of the map products reveals a reasonable agreement above 70% in terms of forest/ non-forest when compared against freely available Earth...

One of the main objectives of the Forest DRAGON 2 project is the evaluation of the Forest DRAGON 1 large area forest growing stock volume (GSV) maps generated for Northeast and Southeast China, based on ERS-1/2 tandem coherence data from the mid 1990s. A special cross-comparison design mainly based on freely available Earth Observation products has...

The creation of large-area forest stem volume maps of Northeast (~1,5 Million km2) and Southeast China (~3 Million km2), based on ERS-1/2 tandem coherence data, was the aim of the FOREST DRAGON 1 project. The accuracy assessment of the map products is one of the objectives of the ongoing FOREST DRAGON 2 project. The ERS-1/2 tandem datasets consiste...

Studies identified desertification and land degradation as a possible cause for persistent drought in the Sahel. The specific vegetation dynamics in the Sahel are good indicators for this kind of environmental change. Due to the scattered availability of ground truth data in huge parts of the Sahel, EO data might provide the only reliable means for...

This paper presents an object-oriented and knowledge-based approach for the classification of terrestrial oil spills, using high-resolution Quickbird data. A crude oil contaminated 6 sqkm sub area of the Khanty-Mansiysk oil-production district in West Siberia was defined as the study area. The problem of mixed spectral signatures of vegetation, soi...

Um großflächige Walduntersuchungen durchzuführen, hat sich in den letzten Jahren der Einsatz von Fernerkundungsdaten, speziell von Radardaten stark bewährt, um aktuelle Waldbestände und Waldflächenveränderungen (Abholzung, Waldbrände, Sturmschäden, Insektenbefall etc.) zu erfassen und zu überwachen. In dieser Arbeit konnte gezeigt werden, dass eine...

This paper presents results achieved within the AQUIFER project from applying a remote sensing approach for regional scale vegetation monitoring in the Sahel. The present study is focussing on vegetation monitoring over parts of Niger, Nigeria and Mali, three countries sharing the common Iullemeden Aquifer System. This Aquifer system is affected by...

This paper presents results achieved within the AQUIFER project and from applying a remote sensing approach for regional scale water and vegetation monitoring in the Sahel. This area is characterized by important interaction between climate variability and socio-economic key factors like agriculture and water resources. The present study is focussi...

This paper provides an initial assessment of the qualification of polarimetric parameters for forest cover mapping in Siberian boreal forest. This investigation is carried out in the framework of JAXA's Kyoto and Carbon Initiative. The impact of the additional polarimetric information will be related to the backscattering intensities derived with t...

For the Russian Service Case of GSE FM ENVISAT ASAR APP data (HH/HV, swath 7) were used during the first two years. For optimal SAR based forest cover mapping a well suited SAR data set is required. Thus emphasis is put on the analysis of ASAR time series throughout the seasons. The investigation comprises for each image of the time series the anal...