Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon

Tropical forests support two-thirds of the world's biodiversity, contribute to global climate regulation, and support the culture and livelihoods of forest-dependent people. Much of extant tropical forest is subject to selective logging and hunting - extractive activities that potentially alter ecosystem function and species diversity. However, the collective impact of these threats, especially in the context of protected vs unprotected areas, is not fully understood. Here we assess how vocalizing biodiversity responds to logging and hunting, across the diel cycle, seasonally, and between protected and unprotected landscapes in Gabon. We compared soundscape saturation across 109 sites in national parks, Forest Stewardship Council (FSC) certified, and non-certified logging concessions. We estimated hunting pressure by quantifying gunshots and relative accessibility per site. Overall, we found that the soundscapes of FSC-certified concessions resembled national parks (selectively logged 20+ years ago) more so than non-certified concessions. We also found that never logged sites, part of a proposed community conserved area, had different soundscapes than all other categories, including national parks. Unlogged sites had higher saturation than logging concessions at dusk and dawn. Soundscapes and hunting pressure were highly variable across different concessions. We found that higher gunshot rates and recent logging were associated with lower soundscape saturation overall. Based on our findings, we recommend that (i) the very few never logged forests that remain (and are not yet protected) should be urgently withdrawn from selective logging, and (ii) FSC or other certification schemes should be promoted in Gabon, with an emphasis on sustainable hunting

The Role of Remote Sensing for Understanding Large-Scale Rubber Concession Expansion in Southern Laos

Increasing global demand for natural rubber began in the mid-2000s and led to large-scale expansion of plantations in Laos until rubber latex prices declined greatly beginning in 2011. The expansion of rubber did not, however, occur uniformly across the country. While the north and central Laos experienced mostly local and smallholder plantations, rubber expansion in the south was dominated by transnational companies from Vietnam, China and Thailand through large-scale land concessions, often causing conflicts with local communities. In this study we use satellite remote sensing to identify and map the expansion of large-scale rubber plantations in Champasak Province—the first area in southern Laos to host large-scale rubber development—and document the biophysical impacts on the local landscape, which of course is linked to social impacts on local people. Our study demonstrates that the expansion of rubber in the province was rapid and did not always conform to approved concession area locations. The mono-culture nature of rubber plantations also had the effect of homogenizing the landscape, eclipsing the changes caused by local populations. We argue that by providing a relatively inexpensive way to track the expansion of rubber plantations over space and time, remote sensing has the potential to provide advocates and other civil society groups with data that might otherwise remain limited to the restricted domains of state regulation and private sector reporting. However, we also caution that while remote sensing has the potential to provide strong public evidence about plantation expansion, access to and control of this information ultimately determines its value

The Role of Remote Sensing for Understanding Large-Scale Rubber Concession Expansion in Southern Laos

Increasing global demand for natural rubber began in the mid-2000s and led to large-scale expansion of plantations in Laos until rubber latex prices declined greatly beginning in 2011. The expansion of rubber did not, however, occur uniformly across the country. While the north and central Laos experienced mostly local and smallholder plantations, rubber expansion in the south was dominated by transnational companies from Vietnam, China and Thailand through large-scale land concessions, often causing conflicts with local communities. In this study we use satellite remote sensing to identify and map the expansion of large-scale rubber plantations in Champasak Province—the first area in southern Laos to host large-scale rubber development—and document the biophysical impacts on the local landscape, which of course is linked to social impacts on local people. Our study demonstrates that the expansion of rubber in the province was rapid and did not always conform to approved concession area locations. The mono-culture nature of rubber plantations also had the effect of homogenizing the landscape, eclipsing the changes caused by local populations. We argue that by providing a relatively inexpensive way to track the expansion of rubber plantations over space and time, remote sensing has the potential to provide advocates and other civil society groups with data that might otherwise remain limited to the restricted domains of state regulation and private sector reporting. However, we also caution that while remote sensing has the potential to provide strong public evidence about plantation expansion, access to and control of this information ultimately determines its value

Weather and Welfare in Ethiopia

Long term increases in rural incomes and productivity in Ethiopia are threatened by weather fluctuations. Changes in weather variability and the number of extreme weather events (specifically droughts) has the capacity to undermine development efforts if it translates into decreased food availability and incomes. This study integrates downscaled daily weather data with household surveys to study the impact of weather and temperature on rural household welfare in Ethiopia. Our panel data econometric approach is one of the first to measure the impacts of weather on household consumption directly. Generally, we find that food and non-food consumption are a function of weather in Ethiopia, and that this link is lessening over time but more pronounced for poor households. Evidence from these survey villages suggests that being in a vulnerable area may not actually result in being worse off relative to being poor in a non vulnerable area. These findings have implications for focusing climate mitigation strategies on the poor regardless of location rather than just the poorest regions

A Landsat-based national-scale map of cropping practices for Turkey (2015)

Cropping practices underlie substantial spatial and temporal variability, which can be captured through the analysis of image time series. Temporal binning helps to overcome limitations concerning operability and repeatability for mapping large areas and can improve the thematic detail and consistency of maps in agricultural systems. We used eight-day temporal features for mapping five cropping practices on annual croplands at 30 m spatial resolution across Turkey. A total of 2,403 atmospherically corrected and topographically normalized Landsat Collection 1 L1TP images of 2015 were used to compute gap-filled eight-day time series of Tasseled Cap components and annual descriptions thereof. We used these features for binary cropland mapping, and subsequent discrimination of five cropping practices: Spring and winter cropping, summer cropping, semi-aquatic cropping, double cropping, and greenhouse cultivation. The map has an overall accuracy of 90%. Class accuracies of winter and spring, summer, and double cropping were robust, while omission errors for semi-aquatic cropping and greenhouse cultivation were high. Note that the map contains information on cropping practices for areas, which were identified as croplands with high certainty. The file is of GeoTiff format and contains the following classes: 1: Winter/spring cropping 2: Summer cropping 3: Semi-aquatic cropping 4: Double-cropping 6: Greenhouse cultivation For details, please see the publication or contact Philippe Rufin mailto:[email protected]

Mapping Cropping Practices on a National Scale Using Intra-Annual Landsat Time Series Binning

Spatially explicit information on cropland use intensity is vital for monitoring land and water resource demands in agricultural systems. Cropping practices underlie substantial spatial and temporal variability, which can be captured through the analysis of image time series. Temporal binning helps to overcome limitations concerning operability and repeatability for mapping large areas and can improve the thematic detail and consistency of maps in agricultural systems. We here assessed the use of annual, quarterly, and eight-day temporal features for mapping five cropping practices on annual croplands across Turkey. We used 2,403 atmospherically corrected and topographically normalized Landsat Collection 1 L1TP images of 2015 to compute quarterly best-pixel composites, quarterly and annual spectral-temporal metrics, as well as gap-filled eight-day time series of Tasseled Cap components. We tested 22 feature sets for binary cropland mapping, and subsequent discrimination of five cropping practices: Spring and winter cropping, summer cropping, semi-aquatic cropping, double cropping, and greenhouse cultivation. We evaluated area-adjusted accuracies and compared cropland area estimates at the province-level with official statistics. We achieved overall accuracies above 90%, when using either all quarterly features or the eight-day Tasseled Cap time series, indicating that temporal binning of intra-annual image time-series into multiple temporal features improves representations of cropping practices. Class accuracies of winter and spring, summer, and double cropping were robust, while omission errors for semi-aquatic cropping and greenhouse cultivation were high. Our mapped cropland extent was in good agreement with province-level statistics (r² = 0.85, RMSE = 7.2%). Our results indicate that 71.3% (± 2.3%) of Turkey´s annual croplands were cultivated during winter and spring, 15.8% (± 2.2%) during summer, while 8.5% (± 1.6%) were double-cropped, 4% (± 1.9%) were cultivated under semi-aquatic conditions, and 0.32% (± 0.2%) was greenhouse cultivation. Our study presents an open and readily available framework for detailed cropland mapping over large areas, which bears the potential to inform assessments of land use intensity, as well as land and water resource demands

Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine

Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area – defined as continental to global – cropland mapping is challenging due to differential manifestation of croplands, wide range of cultivation practices and limited reference data availability. This study presents the results of a cropland extent mapping of 64 countries covering large parts of Europe, Middle East, Russia and Central Asia. To cover such a vast area, roughly 160,000 Landsat scenes from 3351 footprints between 2014 and 2016 were processed within the Google Earth Engine (GEE) platform. We used a pixel-based Random Forest (RF) machine learning algorithm with a set of satellite data inputs capturing diverse spectral, temporal and topographical characteristics across twelve agroecological zones (AEZs). The reference data to train the classification model were collected from very high spatial resolution imagery (VHRI) and ancillary datasets. The result is a binary map showing cultivated/non-cultivated areas ca. 2015. The map produced an overall accuracy of 93.8% with roughly 14% omission and commission errors for the cropland class based on a large set of independent validation samples. The map suggests the entire study area has a total 546 million hectares (Mha) of net croplands (nearly 30% of global net cropland areas) occupying 18% of the study land area. Comparison between national cropland area estimates from United Nations Food and Agricultural Organizations (FAO) and those derived from this work also showed an R-square value of 0.95. This Landsat-derived 30-m cropland product (GFSAD30) provided 10–30% greater cropland areas compared to UN FAO in the 64 Countries. Finally, the map-to-map comparison between GFSAD30 with several other cropland products revealed that the best similarity matrix was with the 30 m global land cover (GLC30) product providing an overall similarity of 88.8% (Kappa 0.7) with producer’s cropland similarity of 89.2% (errors of omissions = 10.8%) and user’s cropland similarity of 81.8% (errors of commissions = 8.1%). GFSAD30 captured the missing croplands in GLC30 product around significantly irrigated agricultural areas in Germany and Belgium and rainfed agriculture in Italy. This study also established that the real strengths of GFSAD30 product, compared to other products, were: 1. identifying precise location of croplands, and 2. capturing fragmented croplands. The cropland extent map dataset is available through NASA’s Land Processes Distributed Active Archive Center (LP DAAC) at https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001, while the training and reference data as well as visualization are available at the Global Croplands website, GEE code is accessible at: https://code.earthengine.google.com/1666e8bed34e0ce2b2aaf1235ad8c6bd

Reference Ranges of Serum Blood Urea Nitrogen, Creatinine Concentration and Ultrasonographic Measurement of the Kidneys in Term Healthy Newborns in the Neonatal Period

Objective: Acute kidney injury is an important problem in neonates. We conducted a cross-sectional prospective study to determine normal serum blood urea nitrogen, creatinine reference ranges and ranges of ultrasonographic measurement of kidneys in healthy term newborns. Study Design: Blood samples were collected from total 357 healthy newborns at birth (n=45), 1st (n=30), 3rd (n=61), 7th (n=34), 10th (n=132), 14th (n=36), and 28th (n=19) days of life. Renal ultrasonographic was performed by the same two radiologists on 81 newborns aged 10 days. Results: Serum blood urea nitrogen and creatinine concentrations have reached to the highest level at the first day of life and have returned to cord level at the third day of life. There were gradually decrease in serum blood urea nitrogen and creatinine levels after the first day of life. There were significant difference in both right and left renal length, width, and volumes in terms of gender and these parameters were statistically higher in boys than girls (p<0.05). Birth weight of the boys (3548±539g) was statistically higher than girls (3307±405 g) (p=0.028). There was a positive correlation between birth weight and right (r=0.38, p=0.000) and left kidney volumes (r=0.44, p=0.000). Conclusion: Our findings showed that measured blood urea nitrogen and creatinine levels changed in accordance with postnatal days and there was a positive correlation between kidney volume and birth weight of newborns. We concluded that these findings are important for evaluation of acute kidney injury and for screening of for urinary tract anomalies in neonate

How universal is the relationship between remotely sensed vegetation indices and crop leaf area index? A global assessment

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CIGreen). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 &gt; 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research