Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey

Long-term monitoring of the extent and intensity of irrigation systems is needed to track crop water consumption and to adapt land use to a changing climate. We mapped the expansion and changes in the intensity of irrigated dry season cropping in Turkey´s Southeastern Anatolia Project annually from 1990 to 2018 using Landsat time series. Irrigated dry season cropping covered 5,779 km² (± 479 km²) in 2018, which represents an increase of 617% over the study period. Dry season cropping was practiced on average every second year, but spatial variability was pronounced. Increases in dry season cropping frequency were observed on 40% of the studied croplands. The presented maps enable the identification of land use intensity hotspots at 30 m spatial resolution, and can thus aid in assessments of water consumption and environmental degradation. All maps are openly available for further use at https://doi.org/10.5281/zenodo.4287661.Peer Reviewe

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series:Does Forest Type Matter?

Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found in tropical dry regions. In this paper, we develop a methodology for mapping forest clearing in Southeast Asia using a study region characterised by heterogeneous forest types. Moderate Resolution Imaging Spectroradiometer (MODIS) time series are decomposed using Breaks For Additive Season and Trend (BFAST) and breakpoints, trend, and seasonal components are combined in a binomial probability model to distinguish between cleared and stable forest. We found that the addition of seasonality and trend information improves the change model performance compared to using breakpoints alone. We also demonstrate the value of considering forest type in disturbance mapping in comparison to the more common approach that combines all forest types into a single generalised forest class. By taking a generalised forest approach, there is less control over the error distribution in each forest type. Dry-deciduous and evergreen forests are especially sensitive to error imbalances using a generalised forest model i.e., clearances were underestimated in evergreen forest, and overestimated in dry-deciduous forest. This suggests that forest type needs to be considered in time series change mapping, especially in heterogeneous forest regions. Our approach builds towards improving large-area monitoring of forest-diverse regions such as Southeast Asia. The findings of this study should also be transferable across optical sensors and are therefore relevant for the future availability of dense time series for the tropics at higher spatial resolutions

The effects of forest cover and disturbance on torrential hazards

Global human population growth, limited space for settlements and a booming tourism industry have led to a strong increase of human infrastructure in mountain regions. As this infrastructure is highly exposed to natural hazards, a main role of mountain forests is to regulate the environment and reduce hazard probability. However, canopy disturbances are increasing in many parts of the world, potentially threatening the protection function of forests. Yet, large-scale quantitative evidence on the influence of forest cover and disturbance on natural hazards remains scarce to date. Here we quantified the effects of forest cover and disturbance on the probability and frequency of torrential hazards for 10 885 watersheds in the Eastern Alps. Torrential hazard occurrences were derived from a comprehensive database documenting 3768 individual debris flow and flood events between 1986 and 2018. Forest disturbances were mapped from Landsat satellite time series analysis. We found evidence that forests reduce the probability of natural hazards, with a 25 percentage point increase in forest cover decreasing the probability of torrential hazards by 8.7%± 1.2%. Canopy disturbances generally increased the probability of torrential hazard events, with the regular occurrence of large disturbance events being the most detrimental disturbance regime for natural hazards. Disturbances had a bigger effect on debris flows than on flood events, and press disturbances were more detrimental than pulse disturbances. We here present the first large scale quantification of forest cover and disturbance effects on torrential hazards. Our findings highlight that forests constitute important green infrastructure in mountain landscapes, efficiently reducing the probability of natural hazards, but that increasing forest disturbances can weaken the protective function of forests.Austrian Climate Research ProgramAustrian Science Fund https://doi.org/10.13039/501100002428Peer Reviewe

Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps

Background: Estimation of forest biomass on the regional and global scale is of great importance. Many studies have demonstrated that lidar is an accurate tool for estimating forest aboveground biomass. However, results vary with forest types, terrain conditions and the quality of the lidar data. Methods: In this study, we investigated the utility of low density lidar data (<2 points∙m−2) for estimating forest aboveground biomass in the mountainous forests of northern Italy. As a study site we selected a 4 km2 area in the Valsassina mountains in Lombardy Region. The site is characterized by mixed and broad-leaved forests with variable stand densities and tree species compositions, being representative for the entire Pre-Alps region in terms of type of forest and geomorphology. We measured and determined tree height, DBH and tree species for 27 randomly located circular plots (radius =10 m) in May 2008. We used allometric equations to calculate total aboveground tree biomass and subsequently plot-level aboveground biomass (mg∙ha−1). Lidar data were collected in June 2004. Results: Our results indicate that low density lidar data can be used to estimate forest aboveground biomass with acceptable accuracies. The best height results show a R2 = 0.87 from final model and the root mean square error (RMSE) 1.02 m (8.3% of the mean). The best biomass model explained 59% of the variance in the field biomass. Leave-one-out cross validation yielded an RMSE of 30.6 mg∙ha−1 (20.9% of the mean). Conclusions: Low-density lidar data can be used to develop a forest aboveground biomass model from plot-level lidar height measurements with acceptable accuracies. In order to monitoring the National Forest Inventory, and respond to Kyoto protocol requirements, this analysis might be applied to a larger area. Keywords: LiDAR; Allometric equations; Plant height; Mixed fores

MODIS land cover and LAI Collection 4 product quality across nine sites in the western hemisphere

Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS products. We present an updated BigFoot project protocol for developing 25-m validation data layers over 49-km2 study areas. Results from comparisons of MODIS and BigFoot land cover and LAI products at nine contrasting sites are reported. In terms of proportional coverage, MODIS and BigFoot land cover were in close agreement at six sites. The largest differences were at low tree cover evergreen needleleaf sites and at an Arctic tundra site where the MODIS product overestimated woody cover proportions. At low leaf biomass sites there was reasonable agreement between MODIS and BigFoot LAI products, but there was not a particular MODIS LAI algorithm pathway that consistently compared most favorably. At high leaf biomass sites, MODIS LAI was generally overpredicted by a significant amount. For evergreen needleleaf sites, LAI seasonality was exaggerated by MODIS. Our results suggest incremental improvement from Collection 3 to Collection 4 MODIS products, with some remaining problems that need to be addresse

Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series

Vegetation phenology has a great impact on land-atmosphere interactions like carbon cycling, albedo, and water and energy exchanges. To understand and predict these critical land-atmosphere feedbacks, it is crucial to measure and quantify phenological responses to climate variability, and ultimately climate change. Coarse-resolution sensors such as MODIS and AVHRR have been useful to study vegetation phenology from regional to global scales. These sensors are, however, not capable of discerning phenological variation at moderate spatial scales. By offering increased observation density and higher spatial resolution, the combination of Landsat and Sentinel-2 time series might provide the opportunity to overcome this limitation. In this study, we analyzed the potential of combined Sentinel-2 and Landsat time series for estimating start of season (SOS) of broadleaf forests across Germany for the year 2018. We tested two common statistical modeling approaches (logistic and generalized additive models using thin plate splines) and the two most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). We found strong agreement between SOS estimates from logistic and spline models (rEVI = 0.86; rNDVI = 0.65), whereas agreement was higher for EVI than for NDVI (RMSDEVI = 3.07, RMSDNDVI = 5.26 days). The choice of vegetation index thus had a higher impact on the results than the fitting method. The EVI-based SOS also showed higher correlation with ground observations compared to NDVI (rEVI = 0.51, rNDVI = 0.42). Data density played an important role in estimating land surface phenology. Models combining Sentinel-2A/B, with an average cloud-free observation frequency of 12 days, were largely consistent with the combined Landsat and Sentinel-2 models, suggesting that Sentinel-2A/B may be sufficient to capture SOS for most areas in Germany in 2018. However, in non-overlapping swath areas and mountain areas, observation frequency was significantly lower, underlining the need to combine Landsat and Sentinel-2 for consistent SOS estimates over large areas. Our study demonstrates that estimating SOS of temperate broadleaf forests at medium spatial resolution has become feasible with combined Landsat and Sentinel-2 time series.Peer Reviewe

Satellite‐based habitat monitoring reveals long‐term dynamics of deer habitat in response to forest disturbances

Disturbances play a key role in driving forest ecosystem dynamics, but how disturbances shape wildlife habitat across space and time often remains unclear. A major reason for this is a lack of information about changes in habitat suitability across large areas and longer time periods. Here, we use a novel approach based on Landsat satellite image time series to map seasonal habitat suitability annually from 1986 to 2017. Our approach involves characterizing forest disturbance dynamics using Landsat‐based metrics, harmonizing these metrics through a temporal segmentation algorithm, and then using them together with GPS telemetry data in habitat models. We apply this framework to assess how natural forest disturbances and post‐disturbance salvage logging affect habitat suitability for two ungulates, roe deer (Capreolus capreolus) and red deer (Cervus elaphus), over 32 yr in a Central European forest landscape. We found that red and roe deer differed in their response to forest disturbances. Habitat suitability for red deer consistently improved after disturbances, whereas the suitability of disturbed sites was more variable for roe deer depending on season (lower during winter than summer) and disturbance agent (lower in windthrow vs. bark‐beetle‐affected stands). Salvage logging altered the suitability of bark beetle‐affected stands for deer, having negative effects on red deer and mixed effects on roe deer, but generally did not have clear effects on habitat suitability in windthrows. Our results highlight long‐lasting legacy effects of forest disturbances on deer habitat. For example, bark beetle disturbances improved red deer habitat suitability for at least 25 yr. The duration of disturbance impacts generally increased with elevation. Methodologically, our approach proved effective for improving the robustness of habitat reconstructions from Landsat time series: integrating multiyear telemetry data into single, multi‐temporal habitat models improved model transferability in time. Likewise, temporally segmenting the Landsat‐based metrics increased the temporal consistency of our habitat suitability maps. As the frequency of natural forest disturbances is increasing across the globe, their impacts on wildlife habitat should be considered in wildlife and forest management. Our approach offers a widely applicable method for monitoring habitat suitability changes caused by landscape dynamics such as forest disturbance.Federal State of BerlinEuropean Commission http://dx.doi.org/10.13039/501100000780Peer Reviewe

Comparison of Landsat-8 and Sentinel-2 Data for Estimation of Leaf Area Index in Temperate Forests

With the launch of the Sentinel-2 satellites, a European capacity has been created to ensure continuity of Landsat and SPOT observations. In contrast to previous sensors, Sentinel-2′s multispectral imager (MSI) incorporates three additional spectral bands in the red-edge (RE) region, which are expected to improve the mapping of vegetation traits. The objective of this study was to compare Sentinel-2 MSI and Landsat-8 OLI data for the estimation of leaf area index (LAI) in temperate, deciduous broadleaf forests. We used hemispherical photography to estimate effective LAI at 36 field plots. We then built and compared simple and multiple linear regression models between field-based LAI and spectral bands and vegetation indices derived from Landsat-8 and Sentinel-2, respectively. Our main findings are that Sentinel-2 predicts LAI with comparable accuracy to Landsat-8. The best Landsat-8 models predicted LAI with a root-mean-square error (RMSE) of 0.877, and the best Sentinel-2 model achieved an RMSE of 0.879. In addition, Sentinel-2′s RE bands and RE-based indices did not improve LAI prediction. Thirdly, LAI models showed a high sensitivity to understory vegetation when tree cover was sparse. According to our findings, Sentinel-2 is capable of delivering data continuity at high temporal resolution.Peer Reviewe

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Up‐to‐date and fine‐scale habitat information is essential for managing and conserving wildlife. Studies assessing wildlife habitat commonly rely on categorical land‐cover maps as predictors in habitat models. However, broad land‐cover categories often do not adequately capture key habitat features and generating robust land‐cover maps is challenging and laborious. Continuous variables derived directly from satellite imagery provide an alternative for capturing land‐cover characteristics in habitat models. Improved data availability and processing capacities now allow integrating all available images from medium‐resolution sensors in compositing approaches that derive spectral‐temporal metrics at the pixel level, summarizing spectral responses over time. In this study, we assessed the usefulness of such metrics derived from Landsat imagery for mapping wildlife habitat. We categorize spectral‐temporal metrics into habitat metrics characterizing different aspects of wildlife habitat. Comparing the performance of these metrics against categorical land‐cover maps in habitat models for lynx, red deer and roe deer, we found that models using habitat metrics consistently outperformed models based on categorical land‐cover maps, with average improvements of 13.7% in model AUC and 9.7% in the Continuous Boyce Index. Performance increases were larger for seasonal habitat models, indicating that the habitat metrics capture intra‐annual variability in habitat conditions better than land‐cover maps. Comparing suitability maps to ancillary data further revealed that our habitat metrics were sensitive to fine‐scale heterogeneity in habitat associated with forest structure. Overall, our study highlights the considerable potential of Landsat‐based spectral temporal metrics for assessing wildlife habitat. Given these metrics can be derived directly and in an automatized fashion from globally and freely available Landsat imagery, they open up new possibilities for monitoring habitat dynamics in space and time.Peer Reviewe