‘Looting marks’ in space-borne SAR imagery: measuring rates of archaeological looting in Apamea (Syria) with TerraSAR-X Staring Spotlight

In archaeological remote sensing, space-borne Synthetic Aperture Radar (SAR) has not been used so far to monitor ‘looting’ (i.e. illegal excavations in heritage sites) mainly because of the spatial resolution of SAR images, typically not comparable to the ground dimensions of looting features. This paper explores the potential of the new TerraSAR-X beam mode Staring Spotlight (ST) to investigate looting within a workflow of radar backscattering change detection. A bespoke time series of five single polarisation, ascending mode, ST scenes with an unprecedented azimuth resolution of 0.24 m was acquired over the archaeological site of Apamea in western Syria, from October 2014 to June 2015 with a regular sampling of one image every two months. Formerly included in the Tentative List of UNESCO, the site has been heavily looted from at least early 2012 to May 2014, as confirmed by Google Earth Very High Resolution (VHR) optical imagery. Building upon the theory of SAR imaging, we develop a novel conceptual model of ‘looting marks’, identify marks due to occurrence of new looting and discriminate them from alteration (e.g. filling) of pre-existing looting holes. ‘Looting marks’ appear as distinctive patterns of shadow and layover which are visible in the ground-range reprojected ST image and generated by the morphology of the holes. The recognition of looting marks within ratio maps of radar backscatter (σ0) between consecutive ST scenes allows quantification of the magnitude, spatial distribution and rates of looting activities. In agreement with the estimates based on Google Earth imagery, the ST acquired in October 2014 shows that ~ 45% of the site was looted. In the following eight months new looting happened locally, with holes mainly dug along the margins of the already looted areas. Texture values of ~ 0.31 clearly distinguish these holes from the unaltered, bare ground nearby. Hot spots of change are identified based on the temporal variability of σ0, and colour composites indicate where repeated looting and alteration of existing holes occurred. Most looting marks are observed north of the two main Roman decumani. Looting intensified almost steadily from December 2014, with over 1500 new marks in February–April 2015. The estimated rates of looting increased from 214 looting marks/month in October–December 2014 to over 780 marks/month in April–June 2015, and numerically express the dynamic nature of the phenomenon to which Apamea is still exposed. The method of identifying looting marks in VHR radar images therefore proves a reliable opportunity for archaeologists and image analysts to measure remotely the scale of looting and monitor its temporal evolution

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence

Remote sensing for monitoring tropical dryland forests: A review of current research, knowledge gaps and future directions for Southern Africa

Climate change, manifest via rising temperatures, extreme drought, and associated anthropogenic activities, has a negative impact on the health and development of tropical dryland forests. Southern Africa encompasses significant areas of dryland forests that are important to local communities but are facing rapid deforestation and are highly vulnerable to biome degradation from land uses and extreme climate events. Appropriate integration of remote sensing technologies helps to assess and monitor forest ecosystems and provide spatially explicit, operational, and long-term data to assist the sustainable use of tropical environment landscapes. The period from 2010 onwards has seen the rapid development of remote sensing research on tropical forests, which has led to a significant increase in the number of scientific publications. This review aims to analyse and synthesise the evidence published in peer review studies with a focus on optical and radar remote sensing of dryland forests in Southern Africa from 1997-2020. For this study, 137 citation indexed research publications have been analysed with respect to publication timing, study location, spatial and temporal scale of applied remote sensing data, satellite sensors or platforms employed, research topics considered, and overall outcomes of the studies. This enabled us to provide a comprehensive overview of past achievements, current efforts, major research topics studies, EO product gaps/challenges, and to propose ways in which challenges may be overcome. It is hoped that this review will motivate discussion and encourage uptake of new remote sensing tools (e.g., Google Earth Engine (GEE)), data (e.g., the Sentinel satellites), improved vegetation parameters (e.g., red-edge related indices, vegetation optical depth (VOD)) and methodologies (e.g., data fusion or deep learning, etc.), where these have potential applications in monitoring dryland forests

Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery

Having the ability to make accurate assessments of above ground biomass (AGB) at high spatial resolution is invaluable for the management of dryland forest resources in areas at risk from deforestation, forest degradation pressure and climate change impacts. This study reports on the use of satellite-based synthetic-aperture radar (SAR) and multispectral imagery for estimating AGB by correlating satellite observations with ground truth data collected on forest plots from dryland forests in the Chobe National Park, Botswana. We undertook nineteen quantitative experiments with Sentinel-1 (S1), and Sentinel-2 (S2) and tested simple and multivariate regression including parametric (linear) and non-parametric (random forests) algorithms, to explore the optimal approaches for AGB estimation. The largest AGB value of 145 Mg/ha was found in northern Chobe while a large part of the study area (85%) is characterized by low AGB values (80 Mg/ha AGB, whereas the inclusion of SAR backscatter and optical red edge bands (B5) significantly reduces saturation effects in areas of high biomass. GNDVI and red edge (B5) derived vegetation indices have more potential for estimating AGB in dryland forests than NDVI. Our results demonstrate that dryland AGB can be estimated with a reasonable level of precision from open access Earth observation data using multivariate random forest regression

Integrating remote sensing and demography for more efficient and effective assessment of changing mountain forest distribution

Species range shifts have been well studied in light of rising global temperatures and the role climate plays in restricting species distribution. In mountain regions, global trends show upward elevational shifts of altitudinal treelines. However, there is significant variation in response between geographic locations driven by climatic and habitat heterogeneity and biotic interactions. Accurate estimation of treeline shifts requires fine-scale patterns of forest structure to be discriminated across mountain ranges. Satellite remote sensing allows detailed information on forest structure to be extrapolated across mountain ranges, however, variation in methodology combined with a lack of information on accuracy and repeatability has led to high uncertainty in the utility of remotely sensed data in studies of mountain treelines. We unite three themes; suitability of remote sensing products, ecological relevance of classifications and effectiveness of the training and validation process in relation to the study of mountain treeline ecotones. We identify needs for further research comparing the utility of different remotely sensed data sets, better characterisation of treeline structure and incorporation of accuracy assessment. Collectively, the improvements we describe will significantly improve the utility of remote sensing by facilitating a more consistent approach to defining geographic variation in treeline structure, improving our ability to link processes from stand to regional scale and the accuracy of range shift assessments. Ultimately, this advance will enable better monitoring of mountain treeline shifts and estimation of the associated to biodiversity and ecosystem function

Quantifying structural diversity to better estimate change at mountain forest margins

Global environmental changes are driving shifts in forest distribution across the globe with significant implications for biodiversity and ecosystem function. At the upper elevational limit of forest distribution, patterns of forest advance and stasis can be highly spatially variable. Reliable estimations of forest distribution shifts require assessments of forest change to account for variation in treeline advance across entire mountain ranges. Multispectral satellite remote sensing is well suited to this purpose and is particularly valuable in regions where the scope of field campaigns is restricted. However, there is little understanding of how much information about forest structure at the mountain treeline can be derived from multispectral remote sensing data. Here we combine field data from a structurally diverse treeline ecotone in the Central Mountain Range, Taiwan, with data from four multispectral satellite sensors (GeoEye, SPOT-7, Sentinel-2 and Landsat-8) to identify spectral features that best explain variation in vegetation structure at the mountain treeline and the effect of sensor spatial resolution on the characterisation of structural variation. The green, red and short-wave infrared spectral bands and vegetation indices based on green and short-wave infrared bands offer the best characterisation of forest structure with R2 values reported up to 0.723. There is very little quantitative difference in the ability of the sensors tested here to discriminate between discrete descriptors of vegetation structure (difference of R2MF within 0.09). While Landsat-8 is less well suited to defining above-ground woody biomass (R2 0.12–0.29 lower than the alternative sensors), there is little difference between the relationships defined for GeoEye, SPOT-7 and Sentinel-2 data (difference in R2 < 0.03). Discrete classifications are best suited to the identification of forest structures indicative of treeline advance or stasis, using a simplified class designation to separate areas of old growth forest, forest advance and grassland habitats. Consequently, our results present a major opportunity to improve quantification of forest range shifts across mountain systems and to estimate the impacts of forest advance on biodiversity and ecosystem function

An Assessment of Global Forest Change Datasets for National Forest Monitoring and Reporting

Global Forest Change datasets have the potential to assist countries with national forest measuring, reporting and verification (MRV) requirements. This paper assesses the accuracy of the Global Forest Change data against nationally derived forest change data by comparing the forest loss estimates from the global data with the equivalent data from Guyana for the period 2001&ndash;2017. To perform a meaningful comparison between these two datasets, the initial year 2000 forest state needs first to be matched to the definition of forest land cover appropriate to a local national setting. In Guyana, the default definition of 30% tree cover overestimates forest area is by 483,000 ha (18.15%). However, by using a tree canopy cover (i.e., density of tree canopy coverage metric) threshold of 94%, a close match between the Guyana-MRV non-forest area and the Global Forest Change dataset is achieved with a difference of only 24,210 ha (0.91%) between the two maps. A complimentary analysis using a two-stage stratified random sampling design showed the 94% tree canopy cover threshold gave a close correspondence (R2 = 0.98) with the Guyana-MRV data, while the Global Forest Change default setting of 30% tree canopy cover threshold gave a poorer fit (R2 = 0.91). Having aligned the definitions of forest for the Global Forest Change and the Guyana-MRV products for the year 2000, we show that over the period 2001&ndash;2017 the Global Forest Change data yielded a 99.34% overall Correspondence with the reference data and a 94.35% Producer&rsquo;s Accuracy. The Guyana-MRV data yielded a 99.36% overall Correspondence with the reference data and a 95.94% Producer&rsquo;s Accuracy. A year-by-year analysis of change from 2001&ndash;2017 shows that in some years, the Global Forest Change dataset underestimates change, and in other years, such as 2016 and 2017, change is detected that is not forest loss or gain, hence the apparent overestimation. The conclusion is that, when suitably calibrated for percentage tree cover, the Global Forest Change datasets give a good first approximation of forest loss (and, probably, gains). However, in countries with large areas of forest cover and low levels of deforestation, these data should not be relied upon to provide a precise annual loss/gain or rate of change estimate for audit purposes without using independent high-quality reference data

Accounting for Greenhouse Gas Emissions from Forest Edge Degradation: Gold Mining in Guyana as a Case Study

Background and Methods: Degradation of forests in developing countries results from multiple activities and is perceived to be a key source of greenhouse gas emissions, yet there are not reliable methodologies to measure and monitor emissions from all degrading activities. Therefore, there is limited knowledge of the actual extent of emissions from forest degradation. Degradation can be either in the forest interior, with a repeatable defined pattern within areas of forest, as with timber harvest, or on the forest edge and immediately bounding areas of deforestation. Forest edge degradation is especially challenging to capture with remote sensing or to predict from proxy factors. This paper addresses forest edge degradation and: (1) proposes a low cost methodology for assessing forest edge degradation surrounding deforestation; (2) using the method, provides estimates of gross carbon emissions from forest degradation surrounding and caused by alluvial mining in Guyana, and (3) compares emissions from mining degradation with other sources of forest greenhouse gas emissions. To estimate carbon emissions from forest degradation associated with mining in Guyana, 100 m buffers were located around polygons pre-mapped as mining deforestation, and within these buffers rectangular transects were established. Researchers collected ground data to produce estimates of the biomass damaged as a result of mining activities to apply to the buffer area around the mining deforestation. Results: The proposed method to estimate emissions from forest edge degradation was successfully piloted in Guyana, where 61% of the transects lost 10 Mg C ha−1 or less in trees from mining damage and 46% of these transects lost 1 Mg C ha−1 or less. Seventy percent of the damaged stems and 60% of carbon loss occurred in the first 50 m of the transects. The median loss in carbon stock from mining damage was 2.2 Mg C ha−1 (95% confidence interval: 0.0–10.2 Mg C ha−1). The carbon loss from mining degradation represented 1.0% of mean total aboveground carbon stocks, with emissions from mining degradation equivalent to ~2% of all emissions from forest change in Guyana. Conclusions: Gross carbon emissions from forest degradation around mining sites are of little significance regardless of persistence and potential forest recovery. The development of cost- and time-effective buffers around deforestation provides a sound approach to estimating carbon emissions from forest degradation adjacent to deforestation including surrounding mining. This simple approach provides a low-cost method that can be replicated anywhere to derive forest degradation estimates