Deforestation Events Research Articles

Mitochondrial Introgression, Color Pattern Variation, and Severe Demographic Bottlenecks in Three Species of Malagasy Poison Frogs, Genus Mantella.

Madagascar is a biodiversity hotspot particularly rich in amphibian diversity and only a few charismatic Malagasy amphibians have been investigated for their population-level differentiation. The Mantella madagascariensis group is composed of two rainforest and three swamp forest species of poison frogs. We first confirm the monophyly of this clade using DNA sequences of three nuclear and four mitochondrial genes, and subsequently investigate the population genetic differentiation and demography of the swamp forest species using one mitochondrial, two nuclear and a set of nine microsatellite markers. Our results confirm the occurrence of two main mitochondrial lineages, one dominated by Mantella aurantiaca (a grouping supported also by our microsatellite-based tree) and the other by Mantella crocea + Mantella milotympanum. These two main lineages probably reflect an older divergence in swamp Mantella. Widespread mitochondrial introgression suggests a fairly common occurrence of inter-lineage gene flow. However, nuclear admixture seems to play only a limited role in this group, and the analyses of the RAG-1 marker points to a predominant incomplete lineage sorting scenario between all five species of the group, which probably diverged relatively recently. Our demographic analyses show a common, severe and recent demographic contraction, inferred to be in temporal coincidence with the massive deforestation events that took place in the past 1000 years. Current data do not allow to conclusively delimit independent evolutionary units in these frogs, and we therefore refrain to suggest any taxonomic changes.

Long-Tailed Macaque Response to Deforestation in a Plasmodium knowlesi-Endemic Area

Land-use changes can impact infectious disease transmission by increasing spatial overlap between people and wildlife disease reservoirs. In Malaysian Borneo, increases in human infections by the zoonotic malaria Plasmodium knowlesi are hypothesised to be due to increasing contact between people and macaques due to deforestation. To explore how macaque responses to environmental change impact disease risks, we analysed movement of a GPS-collared long-tailed macaque in a knowlesi-endemic area in Sabah, Malaysia, during a deforestation event. Land-cover maps were derived from satellite-based and aerial remote sensing data and models of macaque occurrence were developed to evaluate how macaque habitat use was influenced by land-use change. During deforestation, changes were observed in macaque troop home range size, movement speeds and use of different habitat types. Results of models were consistent with the hypothesis that macaque ranging behaviour is disturbed by deforestation events but begins to equilibrate after seeking and occupying a new habitat, potentially impacting human disease risks. Further research is required to explore how these changes in macaque movement affect knowlesi epidemiology on a wider spatial scale.

Profile, Level of Vulnerability and Spatial Pattern of Deforestation in Sulawesi Period of 1990 to 2018

Deforestation is an event of loss of forest cover to another cover. Sulawesi forests have the potential to be deforested as with Sumatra and Kalimantan. This study aims to provide information on deforestation events in Sulawesi from 1990 to 2018. The data used in this study are (1) land cover in 1990, 2000, 2010; (2) Landsat 8 imagery in 2018; (3) administrative map of BIG in 2018. The methods used are (1) image classification with on-screen digitation techniques following the PPIK land cover classification guidelines, Forestry Planning Agency (2008) using ArcGIS Desktop 10.6 from ESRI; (2) overlapping maps; (3) analysis of deforestation; (4) analysis of deforestation profiles, (5) vulnerability analysis; and (6) analysis of distribution patterns of deforestation. The results showed that the profile of deforestation occurring on Sulawesi Island in the 1990–2018 observation period was dominated by profile 3-1-1 (the proportion of large forest area, the highest incidence of deforestation early stage at the beginning, at a low rate) in 13 districts. The level of vulnerability to deforestation is a non-vulnerable category (37 districts) which is directed to become a priority in handling deforestation in Sulawesi. Spatial patterns of the deforestation that occurred randomly and were scattered are dominated by shrubs, dryland agricultural activities, and small-scale plantations.

Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors

ABSTRACTMalaysia and Indonesia have been affected by deforestation caused in great part by the proliferation of oil palm plantations. To survey this loss of forest, several studies have monitored these southeast Asian nations with satellite remote sensing alert systems. The methods used have shown potential for this approach, but they are limited by imagery with coarse spatial resolution, low revisit times, and cloud cover. The objective of this research is to improve near real-time operational deforestation detection by combining three sensors: Sentinel-1, Sentinel-2 and Landsat-8. We used Change Vector Analysis to detect changes between non-affected forest and images under analysis. The results were validated using 166 plots of undisturbed forest and confirmed deforestation events throughout Sabah Malaysian State, and from 70 points from drone pictures in Sumatra, Indonesia. Sentinel-2 and Landsat-8 yielded sufficient results in terms of accuracy (less than 11% of commission and omission error). Sentinel-1 had lower accuracy (14% of commission error and 28% of omission error), probably resulting from geometric distortions and speckle noise. During the high cloud-cover season optical sensors took about twice the time to detect deforestation compared to Sentinel-1 which was not affected by cloud cover. By combining the three sensors, we detected deforestations about 8 days after forest clearing events. Deforestations were only detectable during approximately the first 100 days, before bare soils were often coved by legume crop. Our results indicate that near real-time deforestation detection can reveal most events, but the number of false detections could be improved using a multiple event detection process.

What causes deforestation in Indonesia?

We investigate the causes of deforestation in Indonesia, a country with one of the highest rates of primary natural forest loss in the tropics, annually between 2001 and 2016. We use high spatial resolution imagery made available on Google Earth to characterize the land cover types following a random selection of deforestation events, drawn from the Global Forest Change dataset. Notorious in the region, large-scale oil palm and timber plantations together contributed more than two-fifths of nationwide deforestation over our study period, with a peak in late aughts followed by a notable decline up to 2016. Conversion of forests to grasslands, which comprised an average of one-fifth of national deforestation, rose sharply in dominance in years following periods of considerable fire activity, particularly in 2016. Small-scale agriculture and small-scale plantations also contributed one-fifth of nationwide forest loss and were the dominant drivers of loss outside the major islands of Indonesia. Although relatively small contributors to total deforestation, logging roads were responsible for a declining share of deforestation, and mining activities were responsible for an increasing share, over the study period. Direct drivers of deforestation in Indonesia are thus spatially and temporally dynamic, suggesting the need for forest conservation policy responses tailored at the subnational level, and new methods for monitoring the causes of deforestation over time.

Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon

Secondary forests (SF) are important carbon sinks, removing CO2 from the atmosphere through the photosynthesis process and storing photosynthates in their aboveground live biomass (AGB). This process occurring at large-scales partially counteracts C emissions from land-use change, playing, hence, an important role in the global carbon cycle. The absorption rates of carbon in these forests depend on forest physiology, controlled by environmental and climatic conditions, as well as on the past land use, which is rarely considered for retrieving AGB from remotely sensed data. In this context, the main goal of this study is to evaluate the potential of polarimetric (quad-pol) ALOS-2 PALSAR-2 data for estimating AGB in a SF area. Land-use was assessed through Landsat time-series to extract the SF age, period of active land-use (PALU), and frequency of clear cuts (FC) to randomly select the SF plots. A chronosequence of 42 SF plots ranging 3–28 years (20 ha) near the Tapajós National Forest in Pará state was surveyed to quantifying AGB growth. The quad-pol data was explored by testing two regression methods, including non-linear (NL) and multiple linear regression models (MLR). We also evaluated the influence of the past land-use in the retrieving AGB through correlation analysis. The results showed that the biophysical variables were positively correlated with the volumetric scattering, meaning that SF areas presented greater volumetric scattering contribution with increasing forest age. Mean diameter, mean tree height, basal area, species density, and AGB were significant and had the highest Pearson coefficients with the Cloude decomposition (λ3), which in turn, refers to the volumetric contribution backscattering from cross-polarization (HV) (ρ = 0.57–0.66, p-value < 0.001). On the other hand, the historical use (PALU and FC) showed the highest correlation with angular decompositions, being the Touzi target phase angle the highest correlation (Φs) (ρ = 0.37 and ρ = 0.38, respectively). The combination of multiple prediction variables with MLR improved the AGB estimation by 70% comparing to the NL model (R2 adj. = 0.51; RMSE = 38.7 Mg ha−1) bias = 2.1 ± 37.9 Mg ha−1 by incorporate the angular decompositions, related to historical use, and the contribution volumetric scattering, related to forest structure, in the model. The MLR uses six variables, whose selected polarimetric attributes were strongly related with different structural parameters such as the mean forest diameter, basal area, and the mean forest tree height, and not with the AGB as was expected. The uncertainty was estimated to be 18.6% considered all methodological steps of the MLR model. This approach helped us to better understand the relationship between parameters derived from SAR data and the forest structure and its relation to the growth of the secondary forest after deforestation events.

Assessing the accuracy of detected breaks in Landsat time series as predictors of small scale deforestation in tropical dry forests of Mexico and Costa Rica

Tracking the occurrence of deforestation events is an essential task in tropical dry forest (TDF) conservation efforts. Ideally, deforestation monitoring systems would identify a TDF clearing with near real time precision and high spatial detail, and alert park managers and environmental practitioners of illegal forest clearings occurring anywhere in a region of interest. Over the past several years there have been significant advances in the design and application of continuous land cover change mapping algorithms with these capabilities, but no studies have implemented such methods over human dominated TDF environments where small–scale deforestation (<5 ha) is widespread and hard to detect with moderate resolution sensors. The general objective for this research was to evaluate the overall accuracy of the BFASTSpatial R Package for detecting and monitoring small-scale deforestation in four sites located in tropical dry forest landscapes of Mexico and Costa Rica using greenness and moisture spectral indices derived from Landsat time series. Results show a high degree of spatial agreement (90%–94%) between the distribution of TDF clearings occurred during the 2013–2016 period (as indicated by VHR imagery interpretation) and BFASTSpatial outputs. NDMI and NBR2 had the best performance than other indices and this is evidenced by the combined overall, user's and producer's accuracies. In particular, NBR2 were the most accurate predictor of deforestation with an overall accuracy of 94.5%. Our results also imply that monitoring sites at an annual basis is feasible using BFASTSpatial and LTS, but that lower confidence should be given to sub-annual products given significant systematic temporal differences between the BFASTSpatial monthly product and reference data. The possibility of including more clear observations at the spatial resolution of Landsat (30-m) or higher will greatly increase the spatial and temporal accuracies of the method. Given its performance, BFASTSpatial can help monitor hotspots of small-scale TDF loss across Central and North America at little or no cost. Users of the method should have a strong knowledge of the local land use and land cover dynamics and the ecophysiology of vegetation types present in the landscape. This local expertise is necessary for interpreting and validating results as well as communicating its output to decision-makers and stakeholders.

Pre-industrial composition of woodlands and modern deforestation events in the southern part of the Western Carpathians

The southern part of the Western Carpathians is relatively poorly investigated from the perspective of vegetation changes in the Late Holocene. We analysed two fen profiles (Kláťa and Pálenica) in the Slovenské rudohorie Mts (Slovakia), aiming to reconstruct the regional vegetation composition before humans started to have a strong influence to determine the major developmental stages of the woodland vegetation and to date significant deforestation events. According to the results of radiocarbon dating, the Kláťa fen initiated around 1230 AD whereas the Pálenica fen initiated around 570 CE. Both profiles exhibited small differences in their tree composition and its development. Pollen data indicate a prevalence of Fagus, Abies and Picea, with an admixture of Quercus, Ulmus, Fraxinus, Acer and Tilia in the oldest layers of both profiles. The abundance of spruce pollen was higher in the Pálenica fen, where fossilised stomata of spruce provide evidence of its local occurrence. The Kláťa fen, however, exhibited a lower abundance of spruce pollen and was locally more encroached by fir, as evidenced by its macrocopic remains. Young layers of both profiles showed signs of strong deforestation between ca 1530 and 1750 CE, which was indicated by a decrease in pollen of climax trees, an increase in pollen of grassland species and an increase in anthropogenic indicators. Such changes in the composition of pollen spectra clearly coincided with increased human pressure during the colonisation waves since the 16th century and with the development of industry. Human pressure has had a decisive influence on compositional changes in woodlands and their transformation into grasslands and arable fields.

Wildfires as a major challenge for natural regeneration in Atlantic Forest

The natural regeneration management is a good strategy of ecological restoration of the Atlantic Forest, one of the most devastated biomes on the planet. However, the frequent occurrence of wildfires is one of the challenges to the success of this method. The objective of this study was to evaluate the effects of wildfires on forest dynamics in Atlantic Forest. The studied area was explored during the coffee cycle when plantations replaced primary forests. We used remote sensing data to analyze the forest dynamics over a period of 50 years (1966–2016). We used the INPE burn database to find the occurrence of hot spots from 1998 to 2016. During this period, we selected the years most affected by the fires for the identification of fire scars using the Normalized Burn Ratio spectral index. From this set of information, we used the methodology of weights of evidence to relate forest dynamics and wildfire events with biophysical and anthropic variables. The results showed that in 1966 the forest area accounted for 8.01% of the land cover, and in 2016 this number rose to 18.55% due to the spontaneous natural regeneration process. The regenerating areas were mainly related to the proximity of the remaining fragments and the portions of the landscape receiving the least amount of global solar radiation. The proximity to urban areas, roads and highways, damaged regeneration and favored both deforestation and wildfire events. Fire scars preferentially occur where there is greater sun exposure. It is possible to observe a negative correlation between the natural regeneration process and the fire scars. We concluded that fire severity is one of the factors that shape the landscape of the region while slowing the regeneration process in preferential areas.

Change and persistence: exploring the driving forces of long-term forest cover dynamics in the Swiss lowlands

By the end of the twentieth century, the forest cover over most of Europe had stabilized or was increasing after many decades of decline. Persistence and change in forest cover are driven by complex human–environmental interactions and feedback loops operating on different temporal and spatial scales. A promising method to detect these complex interactions between driving forces is a causal analysis based on historical documents. In the first step of this study, forest cover was reconstructed based on historical and contemporary maps of the Canton of Zurich at seven different points in time between 1664 and 2000. Secondly, causal chains of drivers were constructed based on historical document analysis, in order to investigate whether forest cover was stable or whether any compensating mechanisms in place were reducing the net changes. While the overall net forest cover remained considerably persistent in the Canton of Zurich throughout the 336-year study period, major gross forest cover losses and gains were detected during certain periods. Major deforestation events occurred during times of crisis, e.g., at times of economic or political crisis. In contrast, the strong persistence of net forest cover was mainly a consequence of the Forest Police Law, but can also be attributed to intensive land use. The law prohibits deforestation in general, and intensive land use is preventing the kind of natural reforestation occurring in other regions, e.g., the Swiss Alps. These empirical findings shed light on the relevance of a high degree of political and economic stability in terms of maintaining landscape persistence. These insights into the driving forces of forest cover change and persistence can contribute to protecting and managing valuable landscapes in a rapidly changing world.

Improving tropical deforestation detection through using photosynthetic vegetation time series – (PVts-β)

This paper proposes a new approach of change detection that reduces seasonality in time series by using Photosynthetic Vegetation Time Series (PVTS) from satellite images. With this approach, each pixel value represents at the subpixel level a fraction of the photosynthetic forest’s activity. Our hypothesis is based on an assumption that photosynthetic vegetation fractions will remain constant until a disturbing agent (natural or anthropic) occurs. Using Landsat data, we compared our approach with the Carnegie Landsat Systems Analysis-Lite (CLASlite) approach and with the national reports of the Ministry of the Environment of Perú (MINAM). After reducing seasonal variations in Landsat data, we detected deforestation events with a new detection method. Our approach (which was called PVts-β) of detection is a simple method that does not model the seasonality and it only requires as inputs: i) the average and standard deviation of the time series of a pixel and ii) a threshold magnitude (β) that was calibrated to detect deforestation events in tropical forests. For the PVts-β approach, the results of calibration show that deforestation was optimally detected for β = (5,6), higher or lower than this range, the biases favor to false detections and favor the omission of deforestation too. On the other hand, the overall accuracy for the PVts-β approach was 91.1%, with an omission and commission of 8.3% and 0.5% respectively, while for CLASlite the overall accuracy was 79.2%, with an omission and commission of 20.8% and 0.0% respectively. The differences in the overall accuracy between the PVts-β and CLASlite approach were significant, being atmospheric noise a main problem which CLASlite usually does not work optimally. The strength of our PVts-β approach is the early detection at the subpixel level of deforestation events that, added to our new method of change detection explain the little omission obtained in the results. Therefore, the PVts-β approach -that we propose here- provides the opportunity to monitoring deforestation events in tropical forests at sub-annual scales using Landsat data, and it can be used for near-real-time change detection monitoring without a doubt.

Monitoring Deforestation in Rainforests Using Satellite Data: A Pilot Study from Kalimantan, Indonesia

Monitoring large forest areas is presently feasible with satellite remote sensing as opposed to time-consuming and expensive ground surveys as alternative. This study evaluated, for the first time, the potential of using freely available medium resolution (30 m) Landsat time series data for deforestation monitoring in tropical rainforests of Kalimantan, Indonesia, at sub-annual time scales. A simple, generic, data-driven algorithm for deforestation detection based on a consecutive anomalies criterion was proposed. An accuracy assessment in the spatial and the temporal domain was carried out using high-confidence reference sample pixels interpreted with the aid of multi-temporal very high spatial resolution image series. Results showed a promising spatial accuracy, when three consecutive anomalies were required to confirm a deforestation event. Recommendations in tuning the algorithm for different operational use cases were provided within the context of satisfying REDD+ requirements, depending on whether spatial accuracy or temporal accuracy need to be optimized.

Rainfall–vegetation interaction regulates temperature anomalies during extreme dry events in the Horn of Africa

Abstract Climate–vegetation interaction can be perturbed by human activities through deforestation and natural extreme climatic events. These perturbations can affect the energy and water balance, exacerbating heat stress associated with droughts. Such phenomena are particularly relevant in the Horn of Africa, given its economic and social vulnerability to environmental changes. In this paper, we used 16-year time series (2001–2016) of remotely sensed environmental data with the objective of 1) clarifying how rainfall–vegetation interaction affects land surface temperature (LST) seasonality across the Horn of Africa, and 2) evaluating how this interaction affects LST anomalies during forest loss and drought events. Our results showed that vegetation seasonality follows rainfall modality patterns in 81% of the region. On the other hand, seasonality of daytime LST was negatively related to vegetation greenness patterns across ecoregions, and rainfall modality. LST varied more strongly in grasslands and shrublands than over other vegetation classes. Comparison of LST before and after forest loss in three selected areas (two in Ethiopia and one in Kenya) revealed an annual average increase in LST of 0.7 °C, 1.8 °C, and 0.2 °C after climate variability correction, respectively. The average increase in LST was relatively high and consistent during dry months (1.5 °C, 3 °C, and 0.6 °C). As expected, the rainfall anomalies during droughts (2010/2011, 2015, and 2016) were positively correlated with vegetation greenness anomalies. Nonetheless, the degree with which vegetation cover is affected by extreme rainfall events has a strong influence in regulating the impact of droughts on temperature anomalies. This highlights the importance of vegetation resilience and land cover management in regulating the impact of extreme events.

Linking forests, deforestation, and nutritional outcomes: an observational study in nine African countries

BackgroundAgricultural expansion is a leading driver of deforestation, yet malnutrition remains a significant cause of morbidity. Understanding trade-offs and synergies between forest protection and agricultural expansion, and how they vary between groups across time, could help guide socially equitable sustainable development. Forests have important roles in supporting dietary diversity and micronutrient intake, but limited empirical evidence links forests and broad nutritional outcomes. Our research explores the associations between key indicators of malnutrition and forest cover and deforestation, the mediating role of socioeconomic factors, and underlying temporal trends. MethodsIn this observational study, we explored associations between nutritional outcomes (underweightness, stunting, wasting, and overweightness) in children younger than 5 years and forest cover and loss gradients (in 10 km buffers around communities) in 61 032 African households. These households were surveyed by the Demographic and Health Surveys (DHS) Program from nine countries (Burkina Faso, Cameroon, Democratic Republic of the Congo, Ethiopia, Ghana, Guinea, Kenya, Lesotho, and Liberia) between 2003 and 2014. DHS data were combined with spatially explicit data on forest cover and loss, climate, infrastructure, economy, and demography in a series of generalised linear mixed-effects models. FindingsIn urban areas, children in communities surrounded by 1 SD higher forest cover had 10·8% (95% CI 3·0–18·0; p=0·0073) lower incidence of underweightness and 11·7% (1·9–20·6; p=0·0212) lower incidence of wasting than did children in areas with the mean amount of forest cover. In rural areas, the relationship was reversed, with 6·0% (1·6–10·6; p=0·0074) higher incidence of underweightness among children in areas of 1 SD higher forest cover than among children in areas with the mean amount of forest cover. These relationships became marginally non-significant when analysing the urban and rural datasets together. Additionally, children in areas of 1 SD higher deforestation had 6·7% (1·4–12·3; p=0·0124) and 2·6% (0·04–5·2; p=0·0460) higher incidence of underweightness in urban and rural areas, respectively, than did children in areas with the mean amount of deforestation. This positive relationship between deforestation and underweightness appeared to diminish over time. InterpretationOur results provide evidence that forest cover is associated with worse nutritional outcomes in rural areas, but better outcomes in urban areas of Africa. We did not explicitly measure the contribution of forests to dietary diversity. Yet, the nutritional trade-offs between rural African forests and agriculture should be assessed alongside potential micronutrient benefits from forest foods. Furthermore, forest loss appears to be associated with worse nutrition in the short term, although unmeasured frontier migration might have a role. These results might indicate a farm–forest frontier effect, with short-term declines in nutrition during deforestation events, potentially followed by long-term improvements, although further research is required to substantiate this hypothesis. These nutritional effects should be recognised and accounted for in efforts to meet sustainable development objectives relating to health and forest protection. FundingMinistry of Education of Singapore.

A review of land-based greenhouse gas flux estimates in Indonesia

This study examines underlying reasons for differences among land-based greenhouse gas flux estimates in Indonesia, where six national inventories reported average emissions of between 0.4 and 1.1 Gt CO2e yr−1 over the 2000–2012 period. The large range among estimates is only somewhat smaller than Indonesia’s GHG mitigation commitment. To determine the reasons for these differences, we compared input data and estimation methods, including the definitions and assumptions used for setting accounting boundaries, including emitting activities, incorporating fluxes from various carbon pools, and handling legacy fluxes. We also tested the sensitivity of methodological differences by generating our own reference emissions estimate and iteratively modifying individual components of the inventory. We found that the largest changes stem from the inclusion of legacy GHG emissions due to peat drainage (which increased emissions by at least +94% compared to the reference), methane emissions due to peat fires (+35%), and GHG emissions from belowground biomass and necromass carbon pools (+61%), modifications to assumptions of the mass of fuel burnt in peat fire events (+88%), and accounting for regrowth following a deforestation event (−31%). These differences cumulatively explain more than half of the observed difference among inventory estimates. Understanding the various approaches to emissions estimation, and how these influence the magnitude of component GHG fluxes, is an important first step towards reconciling GHG inventories. The Indonesian government’s success in achieving its mitigation goal will depend on its ability to measure progress and evaluate the effectiveness of abatement actions, for which reliable harmonized greenhouse gas inventories are an essential foundation.

Are Cedrus atlantica forests in the Rif Mountains of Morocco heading towards local extinction?

Cedrus atlantica (Atlas cedar) is a relict and endemic endangered species from northwestern African mountains, whose distribution range has undergone a dramatic reduction over recent decades. Long-term studies are needed for a better understanding of the development of its range as well as for assisting in the implementation of sustainable conservation measures. The multi-proxy analysis of a high-resolution fossil record of 180 cm depth allowed us to depict the final demise of an Atlas cedar population from the western Rif Mountains (Jbel Khesana), despite its high resilience during the last ~4000 years. Currently, Atlas cedar trees are not observed in Jbel Khesana but they still occur in the nearby area as scattered populations on a few mountain tops at altitudes higher than 1400 m a.s.l. Our data show an initial relatively stable period (~4000–2400 cal. yr BP) followed by a phase where both climatic and human-induced disturbances cause an alternate dominance of oaks and Atlas cedars (2400~1550 cal. yr BP). Then, the increasing aridity and human activities favoured the depletion of Atlas cedar forests (~1550–800 cal. yr BP). Our record shows that Atlas cedar forests have recovered after each deforestation event, which reveals a high resilience of the species until the mid-20th century, when they became extinct in the study area. The main driver of their local extinction may be attributed to the strong human pressure. Management measures of Atlas cedar in the Rif Mountains should aim at limiting intensive loggings and protecting the existing populations for their local regeneration.

Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2

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 monitoring systems typically suffer from limited data availability due to persistent cloud cover. In this context, the recently launched Sentinel-1 satellites promise unprecedented potential, because for the first time dense and regular SAR observations are free and openly available. We demonstrate multi-sensor near real-time deforestation detection in tropical dry forests, through the combination of Sentinel-1 C-band SAR time series with ALSO-2 PALSAR-2 L-band SAR, and Landsat-7/ETM+ and 8/OLI. We used spatial normalisation to reduce the dry forest seasonality in the optical and SAR time series, and combined them within a probabilistic approach to detect deforestation in near real-time. Our results for a dry tropical forest site in Bolivia, showed that, as a result of high observation availability of Sentinel-1, deforestation events were detected more timely with Sentinel-1 than compared to Landsat and ALOS-2 PALSAR-2. The spatial and temporal accuracies of the multi-sensor approach were higher than the single-sensor results. We improved the precision of the reference data derived from the multi-sensor satellite time series, which enabled a more robust estimation of the temporal accuracy. We quantified how the near real-time deforestation detection is associated with a trade-off between the confidence in detection and the temporal accuracy. We showed that the trade-off affects the choice on how to use the near-real time data for different applications such as fast alerting with high temporal accuracy but lower confidence versus accurate detection at lower temporal detail. When aiming for a high confidence in change area estimates for example, deforestation was detected with a user's accuracy of 88%, a producer's accuracy of 89% (low area bias), and a mean time lag of 31days using all sensors. This is on average 7days earlier than when using only Sentinel-1 observations, and six weeks earlier than when relying only on Landsat observations. We showed that confident near real-time deforestation alerts can be provided with a mean time lag of 22days, but these are associated with a higher commission error. With more dense time series data expected from the Sentinel-1 and -2 sensors for the upcoming decade, spatial and temporal detection accuracy of multi-sensor deforestation monitoring in the tropics will improve further.

Recent loss of closed forests is associated with Ebola virus disease outbreaks

Ebola virus disease (EVD) is a contagious, severe and often lethal form of hemorrhagic fever in humans. The association of EVD outbreaks with forest clearance has been suggested previously but many aspects remained uncharacterized. We used remote sensing techniques to investigate the association between deforestation in time and space, with EVD outbreaks in Central and West Africa. Favorability modeling, centered on 27 EVD outbreak sites and 280 comparable control sites, revealed that outbreaks located along the limits of the rainforest biome were significantly associated with forest losses within the previous 2 years. This association was strongest for closed forests (>83%), both intact and disturbed, of a range of tree heights (5–>19 m). Our results suggest that the increased probability of an EVD outbreak occurring in a site is linked to recent deforestation events, and that preventing the loss of forests could reduce the likelihood of future outbreaks.

Effects of Changing Weather, Oceanographic Conditions, and Land Uses on Spatio-Temporal Variation of Sedimentation Dynamics along Near-Shore Coral Reefs

Sedimentation is a critical threat to coral reefs worldwide. Major land use alteration at steep, highly erodible semi-arid islands accelerates the potential of soil erosion, runoff, and sedimentation stress to nearshore coral reefs during extreme rainfall events. The goal of this study was to assess spatio-temporal variation of sedimentation dynamics across nearshore coral reefs as a function of land use patterns, weather and oceanographic dynamics, to identify marine ecosystem conservation strategies. Sediment was collected at a distance gradient from shore at Bahia Tamarindo (BTA) and Punta Soldado (PSO) coral reefs at Culebra Island, Puerto Rico. Sediment texture and composition were analyzed by dry sieving and loss-on-ignition techniques, and were contrasted with environmental variables for the research period (February 2014 to April 2015). Rainfall and oceanographic data were analyzed to address their potential role on affecting sediment distribution with BEST BIO-ENV, RELATE correlation, and linear regression analysis. A significant difference in sedimentation rate was observed by time and distance from shore (PERMANOVA, p<0.0100), mostly attributed to higher sediment exposure at reef zones closer to shore due to strong relationships with coastal runoff. Sedimentation rate positively correlated with strong rainfall events (Rho= 0.301, p=0.0400) associated with storms and rainfall intensity exceeding 15 mm/hr. At BTA, sediment deposited were mostly composed of sand, suggesting a potential influence of resuspension produced by waves and swells. In contrast, PSO sediments were mostly composed of silt-clay and terrigenous material, mainly attributed to a deforestation event that occurred at adjacent steep sub-watershed during the study period. Spatial and temporal variation of sedimentation pulses and terrigenous sediment input implies that coral reefs exposure to sediment stress is determined by local land use patterns, weather, and oceanographic dynamics. Comprehensive understanding of sediment dynamics and coastal ecosystem interconnectivity is fundamental to implement integrated and adaptive management strategies aimed to promote sustainable development at watershed and island wide-scale to fully mitigate terrigenous sediment impact to marine ecosystems. Furthermore, decision-making processes and policy needs to address sedimentation stress in the context of future climate to reduce land-based threats and strengthen coral reef resilience.

Impact of deforestation on subsurface temperature profiles: implications for the borehole paleoclimate record

Subsurface temperature profiles measured in boreholes are one of the important archives of paleoclimate data for reconstructing the climate of the past 2000 years. Subsurface temperatures are a function of past ground surface temperatures (GST), however GSTs are influenced both by changes in land-use and changes in regional climate. Thus the history of deforestation at borehole sampling locations represents a potential uncertainty in the reconstructed temperature history at the site. Here a fully coupled Earth system model is used estimate the magnitude of the subsurface temperature anomaly from deforestation events from a global perspective. The model simulations suggest that warming of the ground surface is the dominant response to deforestation, consistent with the limited field data that exist. The magnitude of the temperature anomaly varies by environment with a global average anomaly of 0.85 °C with a range of −0.48 °C to 1.78 °C. The warming originates from a reduction in the efficiency of turbulent energy flux to the atmosphere overcompensating an increase in albedo. Overall our simulations suggest that deforestation has a large impact on subsurface temperatures for centuries following deforestation and thus GST reconstructions should take into account previous deforestation events.