Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI.

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA's Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky-Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.

Addressing indirect sourcing in zero deforestation commodity supply chains.

The trade in agricultural commodities is a backbone of the global economy but is a major cause of negative social and environmental impacts, not least deforestation. Commodity traders are key actors in efforts to eliminate deforestation-they are active in the regions where commodities are produced and represent a "pinch point" in global trade that provides a powerful lever for change. However, the procurement strategies of traders remain opaque. Here, we catalog traders' sourcing across four sectors with high rates of commodity-driven deforestation: South American soy, cocoa from Côte d'Ivoire, Indonesian palm oil, and Brazilian live cattle exports. We show that traders often source more than 40% of commodities "indirectly" via local intermediaries and that indirect sourcing is a major blind spot for sustainable sourcing initiatives. To eliminate deforestation, indirect sourcing must be included in sectoral initiatives, and landscape or jurisdictional approaches, which internalize indirect sourcing, must be scaled up.

Detecting cocoa plantations in Côte d'Ivoire and Ghana and their implications on protected areas.

Côte d'Ivoire and Ghana are the largest producers of cocoa in the world. In recent decades the cultivation of this crop has led to the loss of vast tracts of forest areas in both countries. Efficient and accurate methods for remotely identifying cocoa plantations are essential to the implementation of sustainable cocoa practices and for the periodic and effective monitoring of forests. In this study, a method for cocoa plantation identification was developed based on a multi-temporal stack of Sentinel-1 and Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. The Normalized Difference Vegetation Index (NDVI) and second-order texture features were assessed for their importance in an RF classification, and their optimal combination was used as input variables for the RF model to identify cocoa plantations in both countries. The RF model-based cocoa map achieved 82.89% producer's and 62.22% user's accuracy, detecting 3.69 million hectares (Mha) and 2.15 Mha of cocoa plantations for Côte d'Ivoire and Ghana, respectively. The results demonstrate that a combination of an RF model and multi-feature classification can distinguish cocoa plantations from other land cover/use, effectively reducing feature dimensions and improving classification efficiency. The results also highlight that cocoa farms largely encroach into protected areas (PAs), as 20% of the detected cocoa plantation area is located in PAs and almost 70% of the PAs in the study area house cocoa plantations.

The environmental impacts of palm oil in context.

Delivering the Sustainable Development Goals (SDGs) requires balancing demands on land between agriculture (SDG 2) and biodiversity (SDG 15). The production of vegetable oils and, in particular, palm oil, illustrates these competing demands and trade-offs. Palm oil accounts for ~40% of the current global annual demand for vegetable oil as food, animal feed and fuel (210 Mt), but planted oil palm covers less than 5-5.5% of the total global oil crop area (approximately 425 Mha) due to oil palm's relatively high yields. Recent oil palm expansion in forested regions of Borneo, Sumatra and the Malay Peninsula, where >90% of global palm oil is produced, has led to substantial concern around oil palm's role in deforestation. Oil palm expansion's direct contribution to regional tropical deforestation varies widely, ranging from an estimated 3% in West Africa to 50% in Malaysian Borneo. Oil palm is also implicated in peatland draining and burning in Southeast Asia. Documented negative environmental impacts from such expansion include biodiversity declines, greenhouse gas emissions and air pollution. However, oil palm generally produces more oil per area than other oil crops, is often economically viable in sites unsuitable for most other crops and generates considerable wealth for at least some actors. Global demand for vegetable oils is projected to increase by 46% by 2050. Meeting this demand through additional expansion of oil palm versus other vegetable oil crops will lead to substantial differential effects on biodiversity, food security, climate change, land degradation and livelihoods. Our Review highlights that although substantial gaps remain in our understanding of the relationship between the environmental, socio-cultural and economic impacts of oil palm, and the scope, stringency and effectiveness of initiatives to address these, there has been little research into the impacts and trade-offs of other vegetable oil crops. Greater research attention needs to be given to investigating the impacts of palm oil production compared to alternatives for the trade-offs to be assessed at a global scale.

Addressing the need for improved land cover map products for policy support.

The continued increase of anthropogenic pressure on the Earth's ecosystems is degrading the natural environment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products - and there are many available - are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system architecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users' specific needs would fundamentally change the relationship between users and land cover products - requiring more government support to make these systems a reality.

Small room for compromise between oil palm cultivation and primate conservation in Africa.

Despite growing awareness about its detrimental effects on tropical biodiversity, land conversion to oil palm continues to increase rapidly as a consequence of global demand, profitability, and the income opportunity it offers to producing countries. Although most industrial oil palm plantations are located in Southeast Asia, it is argued that much of their future expansion will occur in Africa. We assessed how this could affect the continent's primates by combining information on oil palm suitability and current land use with primate distribution, diversity, and vulnerability. We also quantified the potential impact of large-scale oil palm cultivation on primates in terms of range loss under different expansion scenarios taking into account future demand, oil palm suitability, human accessibility, carbon stock, and primate vulnerability. We found a high overlap between areas of high oil palm suitability and areas of high conservation priority for primates. Overall, we found only a few small areas where oil palm could be cultivated in Africa with a low impact on primates (3.3 Mha, including all areas suitable for oil palm). These results warn that, consistent with the dramatic effects of palm oil cultivation on biodiversity in Southeast Asia, reconciling a large-scale development of oil palm in Africa with primate conservation will be a great challenge.

Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (α<0.05). Findings show that using multiple window sizes provided the best results. First-ordertexture featuresalso provided computational advantages and results that were not significantly different fromthose usingsecond-order texture features.

Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology.

Although conservation intervention has reversed the decline of some species, our success is outweighed by a much larger number of species moving towards extinction. Extinction risk modelling can identify correlates of risk and species not yet recognized to be threatened. Here, we use machine learning models to identify correlates of extinction risk in African terrestrial mammals using a set of variables belonging to four classes: species distribution state, human pressures, conservation response and species biology. We derived information on distribution state and human pressure from satellite-borne imagery. Variables in all four classes were identified as important predictors of extinction risk, and interactions were observed among variables in different classes (e.g. level of protection, human threats, species distribution ranges). Species biology had a key role in mediating the effect of external variables. The model was 90% accurate in classifying extinction risk status of species, but in a few cases the observed and modelled extinction risk mismatched. Species in this condition might suffer from an incorrect classification of extinction risk (hence require reassessment). An increased availability of satellite imagery combined with improved resolution and classification accuracy of the resulting maps will play a progressively greater role in conservation monitoring.

Protection of plants from ambient ozone by applications of ethylenediurea (EDU): a meta-analytic review.

A meta-analysis was conducted to quantitatively assess the effects of ethylenediurea (EDU) on ozone (O3) injury, growth, physiology and productivity of plants grown in ambient air conditions. Results indicated that EDU significantly reduced O3-caused visible injury by 76%, and increased photosynthetic rate by 8%, above-ground biomass by 7% and crop yield by 15% in comparison with non-EDU treated plants, suggesting that ozone reduces growth and yield under current ambient conditions. EDU significantly ameliorated the biomass and yield of crops and grasses, but had no significant effect on tree growth with an exception of stem diameter. EDU applied as a soil drench at a concentration of 200-400 mg/L has the highest positive effect on crops grown in the field. Long-term research on full-grown tree species is needed. In conclusion, EDU is a powerful tool for assessing effects of ambient [O3] on vegetation.

Cutleaf coneflower (Rudbeckia laciniata L.) response to ozone and ethylenediurea (EDU).

Cutleaf coneflower (Rudbeckia laciniata L.) seedlings were placed into open-top chambers in May, 2004 and fumigated for 12 wks. Nine chambers were fumigated with either carbon-filtered air (CF), non-filtered air (NF) or twice-ambient (2x) ozone (O(3)). Ethylenediurea (EDU) was applied as a foliar spray weekly at 0 (control), 200, 400 or 600 ppm. Foliar injury occurred at ambient (30%) and elevated O(3) (100%). Elevated O(3) resulted in significant decreases in biomass and nutritive quality. Ethylenediurea reduced percent of leaves injured, but decreased root and total biomass. Foliar concentrations of cell-wall constituents were not affected by EDU alone; however, EDUxO(3) interactions were observed for total cell-wall constituents and lignocellulose fraction. Our results demonstrated that O(3) altered the physiology and productivity of cutleaf coneflower, and although reducing visible injury EDU may be phytotoxic at higher concentrations.

Use of ethylenediurea (EDU) to ameliorate ozone effects on purple coneflower (Echinacea purpurea).

Purple coneflower plants (Echinacea purpurea) were placed into open-top chambers (OTCs) for 6 and 12 weeks in 2003 and 2004, respectively, and exposed to charcoal-filtered air (CF) or twice-ambient (2x) ozone (O3) in 2003, and to CF, 2x or non-filtered (NF), ambient air in 2004. Plants were treated with ethylenediurea (EDU) weekly as a foliar spray. Foliar symptoms were observed in >95% of the plants in 2x-treated OTCs in both years. Above-ground biomass was not affected by 2x treatments in 2003, but root and total-plant biomass decreased in 2004. As a result of higher concentrations of select cell wall constituents (% ADF, NDF and lignin) nutritive quality was lower for plants exposed to 2x-O3 in 2003 and 2004 (26% and 17%, respectively). Significant EDU x O3 interactions for concentrations of cell wall constituents in 2003 indicated that EDU ameliorated O3 effects on nutritive quality. Interactions observed in 2004 were inconsistent.