A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection

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 updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat- and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series

Use of Sentinel-2 satellite imagery for forest site evaluation and forest harvesting detection

The access to Earth Observation data leaded researcher to a different point of view in the forest sector. Immediately tropical forest deforestation drawn the majority of interests (Perbet et al., 2019; Tang et al., 2019; Shimizu et al., 2017; Asner et al., 2009), heading to the development of many different tools for tropical forest monitoring. This study was focused on the application of satellite remote sensing data (derived from Sentinel-2) to two cardinal aspect for Italian forest. Since wood production plays a key role in developing a rural economy and stimulating the use of sustainable raw material, an increment of Douglas-fir plantation is desirable because of his great growth potential. Therefore, it was necessary to investigate good indices in order to assess the Douglas-fir land suitability and fertility indices. Empirical models were developed and validated using different sets of variables derived from remote sensing data and field survey. Models validation reached good results for Site Index ranging from 0.63 to 0.97 R2 and Current Annual Increment ranging from 0.50 to 0.98 R2. Furthermore, remote sensing data were applied to calibrate and validate different approaches for forest change detection. Knowing where and when forest harvests are done is crucial for correctly applying sustainable forest management and for controlling illegal logging. In this study was demonstrated that there are already tools developed in tropical forest that they could be applied to Italian forest. The best method was the basic one, which uses only summer images avoiding the seasonal noise problem in the time series but losing near-real time ability. If the temporal accuracy is essential the best method for removing time series seasonality resulted the harmonic model fitting, but further analyses are needed expanding the validation area in order to corroborate these results

Sentinel-1 data to support monitoring deforestation in tropical humid forests

In recent years, methodologies for deforestation detection that use satellite data have been developed, primarily using optical data, which cannot detect deforestation in the presence of clouds. In this paper, we discuss a methodology developed to detect deforestation using Sentinel-1 data and that aims to complement typical early warning system based on optical satellite images such as one the Peruvian Government employs. The methodology was applied in three pilot areas in the tropical humid forest of Peru. Sentinel-1 data were acquired in Interferometric Wide Swath (IW) mode and VH polarization. We use a Gamma-Map filter to reduce the speckle noise, and the average of 3 chrono-sequentially continuous images to reduce the multi-temporal variation of the forest backscattering. This produced 6 time series for each pilot area. For the detection of deforestation, we used an algorithm based on the difference and ratio between the images before and after deforestation. The accuracy assessment revealed a user’s accuracy greater than 95%. We also made a multitemporal comparison between our results and the early warning tropical forest loss alerts that use only Landsat data, which showed that until the end of the study period 33.26% of the deforestation we detected was not detected by the early warning alerts that use Landsat data

Detecting tropical selective logging with C-band SAR data may require a time series approach

Selective logging is the primary driver of forest degradation in the tropics and reduces the capacity of forests to harbour biodiversity, maintain key ecosystem processes, sequester carbon, and support human livelihoods. While the preceding decade has seen a tremendous improvement in the ability to monitor forest disturbances from space, large-scale (spatial and temporal) forest monitoring systems have almost universally relied on optical satellite data from the Landsat program, whose effectiveness is limited in tropical regions with frequent cloud cover. Synthetic aperture radar (SAR) data can penetrate clouds and have been utilized in forest mapping applications since the early 1990s, but only recently has SAR data been widely available on a scale sufficient to facilitate pan-tropical selective logging detection systems. Here, a detailed selective logging dataset from three lowland tropical forest regions in the Brazilian Amazon was used to assess the effectiveness of SAR data from Sentinel-1, RADARSAT-2, and Advanced Land Observing Satellite-2 Phased Arrayed L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) for monitoring tropical selective logging. We built Random Forests models aimed at classifying pixel-based differences between logged and unlogged areas. In addition, we used the Breaks For Additive Season and Trend (BFAST) algorithm to assess if a dense time series of Sentinel-1 imagery displayed recognizable shifts in pixel values after selective logging. In general, Random Forests classification with SAR data (Sentinel-1, RADARSAT-2, and ALOS-2 PALSAR-2) performed poorly, having high commission and omission errors for logged observations. This suggests little to no difference in pixel-based metrics between logged and unlogged areas for these sensors, particularly at lower logging intensities. In contrast, the Sentinel-1 time series analyses indicated that areas under higher intensity selective logging (> 20 m3 ha−1) show a distinct spike in the number of pixels that included a breakpoint during the logging season. BFAST detected breakpoints in 50% of logged pixels and exhibited a false alarm rate of approximately 20 m3 ha−1) within the Amazon

Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts

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 loss are temporally related. Here, we combine Sentinel-1-based, near real-time forest cover information with Visible Infrared Imaging Radiometer Suite (VIIRS) active fire alerts, and for the first time, characterize the temporal relationship between fires and tropical forest-cover loss at high temporal detail and medium spatial scale. We quantify fire-related forest-cover loss and separate fires that predate, coincide with, and postdate forest-cover loss. For the Province of Riau, Indonesia, dense Sentinel-1 C-band Synthetic Aperture Radar data with guaranteed observations of at least every 12 days allowed for confident and timely forest-cover-loss detection in natural and plantation forest with user’s and producer’s accuracy above 95%. Forest-cover loss was detected and confirmed within 22 days in natural forest and within 15 days in plantation forest. This difference can primarily be related to different change processes and dynamics in natural and plantation forest. For the period between 1 January 2016 and 30 June 2017, fire-related forest-cover loss accounted for about one third of the natural forest-cover loss, while in plantation forest, less than ten percent of the forest-cover loss was fire-related. We found clear spatial patterns of fires predating, coinciding with, or postdating forest-cover loss. Only the minority of fires in natural and plantation forest temporally coincided with forest-cover loss (13% and 16%) and can thus be confidently attributed as direct cause of forest-cover loss. The majority of the fires predated (64% and 58%) or postdated forest-cover loss (23% and 26%), and should be attributed to other key land management practices. Detailed and timely information on how fires and forest cover loss are temporally related can support tropical forest management, policy development, and law enforcement to reduce unsustainable and illegal fire use in the tropics

A landslide dating framework using a combination of Sentinel-1 SAR and -2 optical imagery

Landslides are mass movements of rock or soil down a slope, which may cause economic loss, damage to natural resources and frequent fatalities. To support risk management, landslide dating methods can provide useful knowledge about the date of the landslide and the frequency of occurrences, and thus potential triggers. Remote sensing techniques provide opportunities for landslide dating and are especially valuable in remote areas. However, the use of optical remote sensing is frequently hampered by cloud cover, decreasing the success rate and accuracy of dating. Here, we propose a landslide dating framework that combines the advantages of optical and SAR remote sensing satellites, because optical monitoring provides spectral changes on the ground and microwave observations provide information on surface changes due to loss of coherence. Our method combines Sentinel-1 and -2 satellite data, and is designed for cases wherein the landslide causes vegetation decrease and terrain deformation resulting in changing Normalized Difference Vegetation Index (NDVI) and SAR backscatter values. This landslide dating framework was tested and evaluated against 60 published landslides across the world. We show that the mean accuracy of landslide dating reaches 23 days when using combined Sentinel-1 and -2 imagery, which is a pronounced improvement compared to using only optical Sentinel-2 images resulting in an accuracy of 51 days. This study highlights that a combination of optical and SAR remote sensing monitoring is a promising technique for dating landslides, especially in remote areas where monitoring equipment is limited or which are frequently covered by clouds. Our method contributes to identifying failure mechanism by providing reliable date ranges of landslide occurrence, assessing landslide hazard and constructing landslide early warning systems

A hierarchical clustering method for land cover change detection and identification

A method to detect abrupt land cover changes using hierarchical clustering of multi-temporal satellite imagery was developed. The Autochange method outputs the pre-change land cover class, the change magnitude, and the change type. Pre-change land cover information is transferred to post-change imagery based on classes derived by unsupervised clustering, enabling using data from different instruments for pre- and post-change. The change magnitude and change types are computed by unsupervised clustering of the post-change image within each cluster, and by comparing the mean intensity values of the lower level clusters with their parent cluster means. A computational approach to determine the change magnitude threshold for the abrupt change was developed. The method was demonstrated with three summer image pairs Sentinel-2/Sentinel-2, Landsat 8/Sentinel-2, and Sentinel-2/ALOS 2 PALSAR in a study area of 12,372 km2 in southern Finland for the detection of forest clear cuts and tested with independent data. The Sentinel-2 classification produced an omission error of 5.6% for the cut class and 0.4% for the uncut class. Commission errors were 4.9% for the cut class and 0.4% for the uncut class. For the Landsat 8/Sentinel-2 classifications the equivalent figures were 20.8%, 0.2%, 3.4%, and 1.6% and for the Sentinel-2/ALOS PALSAR classification 16.7%, 1.4%, 17.8%, and 1.3%, respectively. The Autochange algorithm and its software implementation was considered applicable for the mapping of abrupt land cover changes using multi-temporal satellite data. It allowed mixing of images even from the optical and synthetic aperture radar (SAR) sensors in the same change analysis

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

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 updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat- and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series

A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection

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 updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat- and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series