Decoupling of deforestation and soy production in the southern Amazon during the late 2000s

Impact of run-of-river damming on increasing phytoplankton biomass and species shift in a large Amazonian river

Run-of-river (ROR) dams, often perceived as having minimal environmental impact, can induce significant hydrodynamic changes that alter aquatic ecosystems. We investigated the impacts of an ROR dam on the Madeira River, the largest Amazon tributary, focusing on phytoplankton communities, their ecological implications, and related environmental factors. Our study examined changes in biomass and environmental factors (using General Linear Mixed Models - GLMM), species composition (using PERMANOVA) before and after damming, in both the main channel and tributaries (N = 549 samples). We also identified indicator species associated with different damming phases and regions through an indicator value analysis. The results showed that, following dam construction, the phytoplankton community changed in both the main channel and tributaries, with a shift from lotic diatoms to lentic phytoflagellates. This transition was likely facilitated by altered hydrodynamics and possibly influenced by the decomposition of flooded vegetation in the dam's influence zone. The decomposition of this vegetation could explain both the observed increase in oxygen consumption and the subsequent rise in phytoflagellate biomass after damming. However, despite the overall increase in phytoplankton biomass, the values remained within oligotrophic to mesotrophic conditions, consistent with the low nutrient concentrations recorded. However, we caution that the dam-created hydrodynamic conditions are optimal for phytoplankton growth, potentially exacerbating nutrient-related issues in the future. We recommend proactive management strategies to prevent nutrient enrichment from activities such as agriculture and livestock in isolated Amazon areas affected by dams, thereby mitigating potential degradation of water quality linked to increased phytoplankton biomass.

Soil erosion assessment in the Amazon basin in the last 60 years of deforestation

Anthropic activities in the Amazon basin have been compromising the environmental sustainability of this complex biome. The main economic activities depend on the deforestation of the rainforest for pasture cattle ranching and agriculture. This study analyzes soil erosion to understand how deforestation has impacted the Amazon basin in this context, using three land-use temporal maps (1960, 1990, 2019) through the revised universal soil loss equation (RUSLE). Our results point to a significant influence of deforestation due to the expansion of agricultural and livestock activities on soil erosion rates in the Amazon Basin. The average soil erosion rate has increased by more than 600% between 1960 and 2019, ranging from 0.015 Mg ha-1 year-1 to 0.117 Mg ha-1 year-1. During this period, deforestation of the Amazon rainforest was approximately 7% (411,857 km2), clearly the leading cause of this increase in soil erosion, especially between 1990 and 2019. The south and southeast regions are the most impacted by increasing soil erosion, in which deforestation was accelerated for expanding agriculture and livestock activities, mainly in the sub-basins of the Madeira, Solimões, Xingu, and Tapajós that present soil erosion increases of 390%, 350%, 280%, and 240%, respectively. The sub-basins with the highest sediment delivery rate (SDR) are under the influence of the Andes, highlighting Solimões (27%), Madeira (13%), and Negro (6%) due to the increase in the soil erosion rate increase in these sub-basins.

Complex relationships between soybean trade destination and tropical deforestation

Over the last few years, understanding of the effects of increasingly interconnected global flows of agricultural commodities on coupled human and natural systems has significantly improved. However, many important factors in environmental change that are influenced by these commodity flows are still not well understood. Here, we present an empirical spatial modelling approach to assess how changes in forest cover are influenced by trade destination. Using data for soybean-producing municipalities in the state of Mato Grosso, Brazil, between 2004 and 2017, we evaluated the relationships between forest cover change and the annual soybean trade destination. Results show that although most of the soybean produced in Mato Grosso during the study period (60%) was destined for international markets, municipalities with greater and more consistent soybean production not destined for international markets during the study period were more strongly associated with deforestation. In these municipalities, soybean production was also significantly correlated with cattle and pasture expansion. These results have important implications for the sustainable management of natural resources in the face of an increasingly interconnected world, while also helping to identify the most suitable locations for implementing policies to reduce deforestation risks.

Environmental vulnerability evolution in the Brazilian Amazon

Decision making and environmental policies are mainly based on propensity level to impact in the area. The propensity level can be determined through artificial intelligence techniques included in geotechnological universe. Thus, this study aimed to determine the areas of greatest vulnerability to human activities, in Amazon biome, through MODIS images of Land use and land cover (LULC) from the 2001 and 2013. Remote sensing, Euclidean distance, Fuzzy logic, AHP method and analysis of net variations were applied to specialize the classes of vulnerability in the states belonging to the Amazon Biome. From the results, it can be seen that the class that most evolved in a positive net gain during the evaluated period was "very high" and the one that most reduced was "high", showing that there was a transition from "high" to "very high" risk areas. The states with the largest areas under "very high" risk class were Mato Grosso (101,100.10 km2) and Pará (81,010.30 km2). It is concluded that the application of remote sensing techniques allows the determination and assessment of the environmental vulnerability evolution. Mitigation measures urgently need to be implemented in the Amazon biome. The methodology can be extended to any other area of the planet.

Differing taxonomic responses of mosquito vectors to anthropogenic land-use change in Latin America and the Caribbean

Anthropogenic land-use change, such as deforestation and urban development, can affect the emergence and re-emergence of mosquito-borne diseases, e.g., dengue and malaria, by creating more favourable vector habitats. There has been a limited assessment of how mosquito vectors respond to land-use changes, including differential species responses, and the dynamic nature of these responses. Improved understanding could help design effective disease control strategies. We compiled an extensive dataset of 10,244 Aedes and Anopheles mosquito abundance records across multiple land-use types at 632 sites in Latin America and the Caribbean. Using a Bayesian mixed effects modelling framework to account for between-study differences, we compared spatial differences in the abundance and species richness of mosquitoes across multiple land-use types, including agricultural and urban areas. Overall, we found that mosquito responses to anthropogenic land-use change were highly inconsistent, with pronounced responses observed at the genus- and species levels. There were strong declines in Aedes (-26%) and Anopheles (-35%) species richness in urban areas, however certain species such as Aedes aegypti, thrived in response to anthropogenic disturbance. When abundance records were coupled with remotely sensed forest loss data, we detected a strong positive response of dominant and secondary malaria vectors to recent deforestation. This highlights the importance of the temporal dynamics of land-use change in driving disease risk and the value of large synthetic datasets for understanding changing disease risk with environmental change.

Wildfire-related PM2.5 and health economic loss of mortality in Brazil

BACKGROUND

Wildfire imposes a high mortality burden on Brazil. However, there is a limited assessment of the health economic losses attributable to wildfire-related fine particulate matter (PM_2.5).

METHODS

We collected daily time-series data on all-cause, cardiovascular, and respiratory mortality from 510 immediate regions in Brazil during 2000-2016. The chemical transport model GEOS-Chem driven with Global Fire Emissions Database (GFED), in combination with ground monitored data and machine learning was used to estimate wildfire-related PM_2.5 data at a resolution of 0.25° × 0.25°. A time-series design was applied in each immediate region to assess the association between economic losses due to mortality and wildfire-related PM_2.5 and the estimates were pooled at the national level using a random-effect meta-analysis. We used a meta-regression model to explore the modification effect of GDP and its sectors (agriculture, industry, and service) on economic losses.

RESULTS

During 2000-2016, a total of US$81.08 billion economic losses (US$5.07 billion per year) due to mortality were attributable to wildfire-related PM_2.5 in Brazil, accounting for 0.68% of economic losses and equivalent to approximately 0.14% of Brazil's GDP. The attributable fraction (AF) of economic losses due to wildfire-related PM_2.5 was positively associated with the proportion of GDP from agriculture, while negatively associated with the proportion of GDP from service.

CONCLUSION

Substantial economic losses due to mortality were associated with wildfires, which could be influenced by the agriculture and services share of GDP per capita. Our estimates of the economic losses of mortality could be used to determine optimal levels of investment and resources to mitigate the adverse health impacts of wildfires.

Assessment of Multiple Ecosystem Services and Ecological Security Pattern in Shanxi Province, China

The ecological security pattern construction could effectively regulate ecological processes and ensure ecological functions, then rationally allocate natural resources and green infrastructure, and, finally, realize ecological security. In view of serious soil erosion, accelerated land desertification, soil pollution and habitat degradation in Shanxi Province, the spatial distribution of six key ecosystem services, including water conservation (WC), soil conservation (SC), sand fixation (SF), carbon storage (CS), net primary productivity (NPP) and habitat quality (HQ), was analyzed by using multiple models. The comprehensive ability of multiple ecosystem services in different regions was quantified by calculating multiple ecosystem services landscape index (MESLI). Combined with ecosystem services hotspots, the ecological security pattern of Shanxi Province was constructed by using the minimum cumulative resistance model. The results showed that the spatial differences in ecosystem services in Shanxi Province were obvious, which was low in the seven major basins and Fen River valley, and high in the mountains (especially Taihang and Lvliang Mountains) for WC, SC, CS, NPP and HQ, while high SF was only distributed in the northern Shanxi. The MESLI showed that the ability to provide multiple ecosystem services simultaneously was low in Shanxi Province, with the medium and low grade MESLI regions accounting for 58.61%, and only 18.07% for the high grade MESLI regions. The important protected areas and ecological sources of the ecological security pattern were concentrated in the Lvliang and Taihang Mountains, which were consistent with the key areas of ecosystem services. The ecological corridors illustrated network distribution with ecological sources as the center, the low-, medium- and high-level buffers accounted for 26.34%, 17.03% and 16.35%, respectively. The results will provide important implications for economic transformation, high-quality development and ecological sustainable development in resource-based regions worldwide.

The post-conflict expansion of coca farming and illicit cattle ranching in Colombia

Illicit cattle ranching and coca farming have serious negative consequences on the Colombian Amazon's land systems. The underlying causes of these land activities include historical processes of colonization, armed conflict, and narco-trafficking. We aim to examine how illicit cattle ranching and coca farming are driving forest cover change over the last 34 years (1985-2019). To achieve this aim, we combine two pixel-based approaches to differentiate between coca farming and cattle ranching using hypothetical observed patterns of illicit activities and a deep learning algorithm. We found evidence that cattle ranching, not coca, is the main driver of forest loss outside the legal agricultural frontier. There is evidence of a recent, explosive conversion of forests to cattle ranching outside the agricultural frontier and within protected areas since the negotiation phase of the peace agreement. In contrast, coca is remarkably persistent, suggesting that crop substitution programs have been ineffective at stopping the expansion of coca farming deeper into protected areas. Countering common narratives, we found very little evidence that coca farming precedes cattle ranching. The spatiotemporal dynamics of the expansion of illicit land uses reflect the cumulative outcome of agrarian policies, Colombia's War on Drugs, and the 2016 peace accord. Our study enables the differentiation of illicit land activities, which can be transferred to other regions where these activities have been documented but poorly distinguished spatiotemporally. We provide an applied framework that could be used elsewhere to disentangle other illicit land uses, track their causes, and develop management options for forested land systems and people who depend on them.

Assessment of Climate Change and Land Use Effects on Water Lily (Nymphaea L.) Habitat Suitability in South America

Many aquatic species have restricted dispersal capabilities, making them the most vulnerable organisms to climate change and land use change patterns. These factors deplete Nymphaea species’ suitable habitats, threatening their populations and survival. In addition, the species are poorly documented, which may indicate how scarce they are or will become. Members of Nymphaea are ecologically important as well as having cultural and economic value, making them of conservation interest. Therefore, using the maximum entropy (MaxEnt) approach, climatic variables, land use, and presence points were modeled for seven Nymphaea species in South America, using three general circulation models (CCSM4, HADGEM2-AO, and MIROC5) and in two representative concentration pathways (RCPs 4.5 and 8.5) and two scenarios (2050 and 2070). Our results indicated that mean diurnal range (bio2), precipitation of the wettest month (bio13), temperature seasonality (bio15), and land use (dom_lu) were the main influencing factors. For all species, suitable areas were concentrated east of Brazil, and they were variable in northern parts of the continent. Besides, inconsistent expansion and contraction of suitable habitats were noticed among the species. For example, N. amazonum, N. rudgeana, and N. lasiophylla future habitat expansions declined and habitat contraction increased, while for N. ampla and N. jamesoniana, both future habitat expansion and contraction increased, and for N. pulchella and N. rudgeana it varied in the RCPs. Moreover, the largest projected suitable habitats were projected outside protected areas, characterized by high human impacts, despite our analysis indicating no significant change between protected and unprotected areas in suitable habitat change. Finally, understanding how climate change and land use affect species distribution is critical to developing conservation measures for aquatic species.

Achieving the Sustainable Development Goals in the post-pandemic era

The COVID-19 pandemic continues to pose substantial challenges to achieving the Sustainable Development Goals (SDGs). Exploring systematic SDG strategies is urgently needed to aid recovery from the pandemic and reinvigorate global SDG actions. Based on available data and comprehensive analysis of the literature, this paper highlights ongoing challenges facing the SDGs, identifies the effects of COVID-19 on SDG progress, and proposes a systematic framework for promoting the achievement of SDGs in the post-pandemic era. Progress towards attaining the SDGs was already lagging behind even before the onset of the COVID-19 pandemic. Inequitable distribution of food-energy-water resources and environmental crises clearly threaten SDG implementation. Evidently, there are gaps between the vision for SDG realization and actual capacity that constrain national efforts. The turbulent geopolitical environment, spatial inequities, and trade-offs limit the effectiveness of SDG implementation. The global public health crisis and socio-economic downturn under COVID-19 have further impeded progress toward attaining the SDGs. Not only has the pandemic delayed SDG advancement in general, but it has also amplified spatial imbalances in achieving progress, undermined connectivity, and accentuated anti-globalization sentiment under lockdowns and geopolitical conflicts. Nevertheless, positive developments in technology and improvement in environmental conditions have also occurred. In reflecting on the overall situation globally, it is recommended that post-pandemic SDG actions adopt a "Classification-Coordination-Collaboration" framework. Classification facilitates both identification of the current development status and the urgency of SDG achievement aligned with national conditions. Coordination promotes domestic/international and inter-departmental synergy for short-term recovery as well as long-term development. Cooperation is key to strengthening economic exchanges, promoting technological innovation, and building a global culture of sustainable development that is essential if the endeavor of achieving the SDGs is to be successful. Systematic actions are urgently needed to get the SDG process back on track.

Tracking and classifying Amazon fire events in near real time

Exceptional fire activity in 2019 sparked concern about Amazon forest conservation. However, the inability to rapidly separate satellite fire detections by fire type hampered fire suppression and assessment of ecosystem and air quality impacts. Here, we describe the development of a near-real-time approach for tracking contributions from deforestation, forest, agricultural, and savanna fires to burned area and emissions and apply the approach to the 2019 fire season in South America. Across the southern Amazon, 19,700 deforestation fire events accounted for 39% of all satellite active fire detections and the majority of fire carbon emissions (63%; 69 Tg C). Multiday fires accounted for 81% of burned area and 92% of carbon emissions from the Amazon, with many forest fires burning uncontrolled for weeks. Most fire detections from deforestation fires were correctly identified within 2 days (67%), highlighting the potential to improve situational awareness and management outcomes during fire emergencies.

Analysis of Land Use and Land Cover Changes through the Lens of SDGs in Semarang, Indonesia

Land Use and Land Cover Changes (LULCC) are occurring rapidly around the globe, particularly in developing island nations. We use the lens of the United Nations’ Sustainable Development Goals (SDG) to determine potential policies to address LULCC due to increasing population, suburbia, and rubber plantations in Semarang, Indonesia between 2006 and 2015. Using remote sensing, overlay analysis, optimized hot spot analysis, expert validation, and Continuous Change Detection and Classification, we found that there was a spread of urban landscapes towards the southern and western portions of Semarang that had previously been occupied by forests, plantations, agriculture, and aquaculture. We also witnessed a transition in farming from agriculture to rubber plantations, a cash crop. The implications of this study show that these geospatial analyses and big data can be used to characterize the SDGs, the complex interplay of these goals, and potentially alleviate some of the conflicts between disparate SDGs. We recommend certain policies that can assist in preserving the terrestrial ecosystem of Semarang (SDG 15) while creating a sustainable city (SDG 11, SDG 9) and providing sufficient work for individuals (SDG 1) in a growing economy (SDG 8) while simultaneously maintaining a sufficient food supply (SDG 2).

Monitoring Complex Integrated Crop–Livestock Systems at Regional Scale in Brazil: A Big Earth Observation Data Approach

Due to different combinations of agriculture, livestock and forestry managed by rotation, succession and intercropping practices, integrated agriculture production systems such as integrated crop–livestock systems (iCL) constitute a very complex target and a challenge for automatic mapping of cropping practices based on remote sensing data. The overall objective of this study was to develop a classification strategy for the annual mapping of integrated Crop–Livestock systems (iCL) at a regional scale. This strategy was designed and tested in the six agro-climatic regions of Mato Grosso, the largest Brazilian soybean producer state, using MODIS satellite time-series images acquired between 2012 and 2019, ground data with heterogeneous distribution in space and time and a Random Forest classifier. The results showed that: 1. the use of unbalanced training samples with a class composition close to the real one was the right classifier training strategy; 2. the use of a single training database (pooling samples from different years and regions) to classify each region and year individually proved to be robust enough to provide similar classification accuracies in comparison to those based on the use of a database acquired for each region and for each year. The final hierarchical classification overall accuracy was 0.89 for Level 1, the cropping pattern level (single and double crops DC); 0.84 for Level 2, the DC category level (integrated system iCL soy-pasture/brachiaria, soy-cotton and soy-cereal); 0.77 for Level 3, the iCL level (iCL1 soy-pasture and iCL2 soy-pasture mixed with corn). The F-scores for DC, iCL and iCL1 cropping systems presented high accuracy (0.89, 0.85 and 0.84), while iCL2 was more difficult to classify (0.63). This approach will next be applied across the entire Brazilian soybean corridor, leading to an operational tool for monitoring the adoption of sustainable intensification practices recognized by Brazil’s Agriculture Low Carbon Plan (ABC PLAN).

Ecological Quality Response to Multi-Scenario Land-Use Changes in the Heihe River Basin

To investigate the spatial-temporal effects of land-use changes on ecological quality and future trends, an integrated framework combining the Dyna-CLUE model and the remote sensing ecological index (RSEI) was developed. Land-use changes from 2000 to 2035 were simulated and projected under the current trend scenario (CTS), economic development scenario (EDS) and ecological protection scenario (EPS) in the Heihe River Basin, while the RSEI was predicted using the elastic net regression (machine learning method); finally, the predicted results were synthesized and analyzed. The results showed that forest, grassland and water were positively correlated with ecological quality, with the green space coverage under the CTS, EPS and EDS accounting for 34.15%, 70.65% and 34.72% of the total transferred land area, respectively. The increase in the area of build-up land and unutilized land was detrimental to ecological quality, with the area of building land in the EDS being 1.75 times larger than in the year 2000. The EDS contributes to the sustainable development of the upstream area and the EPS is more conducive to the midstream and downstream areas by limiting the expansion of build-up land and by developing unutilized land in a limited way to increase the area of green space after reconciling economic conditions. Projection results promote the rational allocation of various land-use types in the future (semi) arid region, such as artificial forestation, unutilized land development and restriction of urban expansion, and also lay the foundation for the formulation of policies such as water allocation and ecological protection to facilitate the sustainable development of regional society, economy and ecology.

Research on cropping intensity mapping of the Huai River Basin (China) based on multi-source remote sensing data fusion

As a key input variable to many global climates, land surfaces and crop models, cropping intensity (CI) accurately assesses and predicts crops' output, in view of the global decline in food production in recent years due to declining natural resources, urban expansion and declining quality of arable land. Hence, research on CI mapping can have a contribution to solve this problem. Unfortunately, existing remote sensing data for CI mapping research, including Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images, are not adequate for obtaining CI information at higher spatial and temporal resolution. In this regard, we develop an algorithm to extract CI based on per-pixel physiognomy. To be specific, the algorithm is based on the Google Earth Engine (GEE) platform and constructs a high temporal (10 days) spatial (30 m) resolution dataset with the fusion of Landsat 7/8 and Sentinel-2 A/B image data and extracts CI information using a time series of peak discovery method, threshold method and phenological period feature extraction to obtain the 2018 Chinese Huai River Basin (HRB) CI map. Our results suggest that the overall accuracy (OA) of CI extraction in the HRB is 92.72%, with a kappa coefficient of 0.864. The single-season crop, double-season crop and three-season crop account for 41.6%, 57.7% and 0.7% of the total farmland area, respectively. Compared to existing CI identification and extraction methods, this approach achieves higher accuracy in the identification and extraction of CI information over a larger area.

Impact of Malaysian palm oil on sustainable development goals: co-benefits and trade-offs across mitigation strategies

Palm oil (PO) is an important source of livelihood, but unsustainable practices and widespread consumption may threaten human and planetary health. We reviewed 234 articles and summarized evidence on the impact of PO on health, social and economic aspects, environment, and biodiversity in the Malaysian context, and discuss mitigation strategies based on the sustainable development goals (SDGs). The evidence on health impact of PO is equivocal, with knowledge gaps on whether moderate consumption elevates risk for chronic diseases, but the benefits of phytonutrients (SDG2) and sensory characteristics of PO seem offset by its high proportion of saturated fat (SDG3). While PO contributes to economic growth (SDG9, 12), poverty alleviation (SDG1, 8, 10), enhanced food security (SDG2), alternative energy (SDG9), and long-term employment opportunities (SDG1), human rights issues and inequities attributed to PO production persist (SDG8). Environmental impacts arise through large-scale expansion of monoculture plantations associated with increased greenhouse gas emissions (SDG13), especially from converted carbon-rich peat lands, which can cause forest fires and annual trans-boundary haze; changes in microclimate properties and soil nutrient content (SDG6, 13); increased sedimentation and change of hydrological properties of streams near slopes (SDG6); and increased human wildlife conflicts, increase of invasive species occurrence, and reduced biodiversity (SDG14, 15). Practices such as biological pest control, circular waste management, multi-cropping and certification may mitigate negative impacts on environmental SDGs, without hampering progress of socioeconomic SDGs. While strategies focusing on improving practices within and surrounding plantations offer co-benefits for socioeconomic, environment and biodiversity-related SDGs, several challenges in achieving scalable solutions must be addressed to ensure holistic sustainability of PO in Malaysia for various stakeholders.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11625-021-01052-4.

Modeling Nitrous Oxide Emissions From Large-Scale Intensive Cropping Systems in the Southern Amazon

Nitrogen (N) fertilizer use is rapidly intensifying on tropical croplands and has the potential to increase emissions of the greenhouse gas, nitrous oxide (N2O). Since about 2005 Mato Grosso (MT), Brazil has shifted from single-cropped soybeans to double-cropping soybeans with maize, and now produces 1.5% of the world's maize. This production shift required an increase in N fertilization, but the effects on N2O emissions are poorly known. We calibrated the process-oriented biogeochemical DeNitrification-DeComposition (DNDC) model to simulate N2O emissions and crop production from soybean and soybean-maize cropping systems in MT. After model validation with field measurements and adjustments for hydrological properties of tropical soils, regional simulations suggested N2O emissions from soybean-maize cropland increased almost fourfold during 2001–2010, from 1.1 ± 1.1 to 4.1 ± 3.2 Gg 1014 N-N2O. Model sensitivity tests showed that emissions were spatially and seasonably variable and especially sensitive to soil bulk density and carbon content. Meeting future demand for maize using current soybean area in MT might require either (a) intensifying 3.0 million ha of existing single soybean to soybean-maize or (b) increasing N fertilization to ~180 kg N ha−1 on existing 2.3 million ha of soybean-maize area. The latter strategy would release ~35% more N2O than the first. Our modifications of the DNDC model will improve estimates of N2O emissions from agricultural production in MT and other tropical areas, but narrowing model uncertainty will depend on more detailed field measurements and spatial data on soil and cropping management.

Forest frontiers out of control: The long-term effects of discourses, policies, and markets on conservation and development of the Brazilian Amazon

With the Brazilian military governments of the 1960s, systematic economic development of the Amazon began. Social and environmental concerns have entered Amazonian discourses and policies only since the 1990s. Since then, reports of threats to forests and indigenous people have alternated with reports of socio-economic progress and environmental achievements. These contradictions often arise from limited thematic, sectoral, temporal, or spatial perspectives, and lead to misinterpretation. Our paper offers a comprehensive picture of discourses, policies, and socio-environmental dynamics for the entire region over the last five decades. We distinguish eight historical policy phases, each of which had little effect on near-linear dynamics of demographic growth and land-use expansion, although some policies showed the potential to change the course of development. To prevent local, national, and international actors from continuing to assert harmful interests in the region, a coherent long-term commitment and change in the collective mindset are needed.

Spatial spillover effects from agriculture drive deforestation in Mato Grosso, Brazil

Deforestation of the Amazon rainforest is a threat to global climate, biodiversity, and many other ecosystem services. In order to address this threat, an understanding of the drivers of deforestation processes is required. Spillover effects and factors that differ across locations and over time play important roles in these processes. They are largely disregarded in applied research and thus in the design of evidence-based policies. In this study, we model connectivity between regions and consider heterogeneous effects to gain more accurate quantitative insights into the inherent complexity of deforestation. We investigate the impacts of agriculture in Mato Grosso, Brazil, for the period 2006-2017 considering spatial spillovers and varying impacts over time and space. Spillovers between municipalities that emanate from croplands in the Amazon appear as the major driver of deforestation, with no direct effects from agriculture in recent years. This suggests a moderate success of the Soy Moratorium and Cattle Agreements, but highlights their inability to address indirect effects. We find that the neglect of the spatial dimension and the assumption of homogeneous impacts lead to distorted inference. Researchers need to be aware of the complex and dynamic processes behind deforestation, in order to facilitate effective policy design.

Mapping and Monitoring Zero-Deforestation Commitments

A growing number of companies have announced zero-deforestation commitments (ZDCs) to eliminate commodities produced at the expense of forests from their supply chains. Translating these aspirational goals into forest conservation requires forest mapping and monitoring (M&M) systems that are technically adequate and therefore credible, salient so that they address the needs of decision makers, legitimate in that they are fair and unbiased, and scalable over space and time. We identify 12 attributes of M&M that contribute to these goals and assess how two prominent ZDC programs, the Amazon Soy Moratorium and the High Carbon Stock Approach, integrate these attributes into their M&M systems. These programs prioritize different attributes, highlighting fundamental trade-offs in M&M design. Rather than prescribe a one-size-fits-all solution, we provide policymakers and practitioners with guidance on the design of ZDC M&M systems that fit their specific use case and that may contribute to more effective implementation of ZDCs.

Monitoring of Land Use–Land Cover Change and Potential Causal Factors of Climate Change in Jhelum District, Punjab, Pakistan, through GIS and Multi-Temporal Satellite Data

Land use–land cover (LULC) alteration is primarily associated with land degradation, especially in recent decades, and has resulted in various harmful changes in the landscape. The normalized difference vegetation index (NDVI) has the prospective capacity to classify the vegetative characteristics of many ecological areas and has proven itself useful as a remote sensing (RS) tool in recording vegetative phenological aspects. Likewise, the normalized difference built-up index (NDBI) is used for quoting built-up areas. The current research objectives include identification of LULC, NDVI, and NDBI changes in Jhelum District, Punjab, Pakistan, during the last 30 years (1990–2020). This study targeted five major LULC classes: water channels, built-up area, barren land, forest, and cultivated land. Satellite imagery classification tools were used to identify LULC changes in Jhelum District, northern Punjab, Pakistan. The perception data about the environmental variations as conveyed by the 500 participants (mainly farmers) were also recorded and analyzed. The results depict that the majority of farmers (54%) believe in the appearance of more drastic changes such as less rainfall, drought, and decreased water availability for irrigation during 2020 compared to 30 years prior. Overall accuracy assessment of imagery classification was 83.2% and 88.8% for 1990, 88.1% and 85.7% for 2000, 86.5% and 86.7% for 2010, and 85.6% and 87.3% for 2020. The NDVI for Jhelum District was the highest in 1990 at +0.86 and the lowest in 2020 at +0.32; similarly, NDBI values were the highest in 2020 at +0.72 and the lowest in 1990 at −0.36. LULC change showed a clear association with temperature, NDBI, and NDVI in the study area. At the same time, variations in the land area of barren soil, vegetation, and built-up from 1990 to 2020 were quite prominent, possibly resulting in temperature increases, reduction in water for irrigation, and changing rainfall patterns. Farmers were found to be quite responsive to such climatic variations, diverting to framing possible mitigation approaches, but they need government assistance. The findings of this study, especially the causes and impacts of rapid LULC variations in the study area, need immediate attention from related government departments and policy makers.

Pollination in the Rainforest: Scarce Visitors and Low Effective Pollinators Limit the Fruiting Success of Tropical Orchids

A single plant might be visited by many flower visitors but not all might act as pollinators. Legitimate pollinators might also differ considerably in their efficiency, limiting pollination success. Unsuitable climatic conditions such as rain also affect pollinator activity. However, in the evergreen rainforest there is no prolonged dry season and flowering occurs usually under rain. Here, we explore the dependence on pollinators and the efficiency of flower visitors for the fruiting success of 10 Andean rainforest orchids. All species were self-compatible but strictly pollinator-dependent. Overall, we found low levels of fruit set in control flowers while experimental geitonogamous and cross-pollinations increased fruit set, revealing extensive pollination limitation in all populations. Seed viability dropped considerably after self and geitonogamous pollinations suggesting the possibility of early-acting inbreeding depression. Even though we monitored flower visitors on an extensive survey, few visitors were seen in these species and even fewer acted as legitimate pollinators. Thus, even though orchid pollination might be extremely diversified, these results show that few visitors are pollinating these species, explaining the low levels of fruit set recorded in the area studied.

Socioeconomic and environmental effects of soybean production in metacoupled systems

Human-environment interactions within and across borders are now more influential than ever, posing unprecedented sustainability challenges. The framework of metacoupling (interactions within and across adjacent and distant coupled human-environment systems) provides a useful tool to evaluate them at diverse temporal and spatial scales. While most metacoupling studies have so far addressed the impacts of distant interactions (telecouplings), few have addressed the complementary and interdependent effects of the interactions within coupled systems (intracouplings) and between adjacent systems (pericouplings). Using the production and trade of a major commodity (soybean) as a demonstration, this paper empirically evaluates the complex effects on deforestation and economic growth across a globally important soybean producing region (Mato Grosso in Brazil). Although this region is influenced by a strong telecoupling process (i.e., soybean trade with national and international markets), intracouplings pose significant effects on deforestation and economic growth within focal municipalities. Furthermore, it generates pericoupling effects (e.g., deforestation) on adjacent municipalities, which precede economic benefits on adjacent systems, and may occur during and after the soybean production takes place. These results show that while economic benefits of the production of agricultural commodities for global markets tend to be localized, their environmental costs tend to be spatially widespread. As deforestation also occurred in adjacent areas beyond focal areas with economic development, this study has significant implications for sustainability in an increasingly metacoupled world.

Growth Rate, Dry Matter Accumulation, and Partitioning in Soybean (Glycine max L.) in Response to Defoliation under High-Rainfall Conditions

The frequency of heavy rains is increasing with climate change in regions that already have high annual rainfall (i.e., Sichuan, China). Crop response under such high-rainfall conditions is to increase dry matter investment in vegetative parts rather than reproductive parts. In the case of soybean, leaf redundancy prevails, which reduces the light transmittance and seed yield. However, moderate defoliation of soybean canopy could reduce leaf redundancy and improve soybean yield, especially under high-rainfall conditions. Therefore, the effects of three defoliation treatments (T1, 15%; T2, 30%; and T3, 45% defoliation from the top of the soybean canopy; defoliation treatments were applied at the pod initiation stage of soybean) on the growth and yield parameters of soybean were evaluated through field experiments in the summer of 2017, 2018, and 2019. All results were compared with nondefoliated soybean plants (CK) under high-rainfall conditions. Compared with CK, treatment T1 significantly (p < 0. 05) improved the light transmittance and photosynthetic rate of soybean. Consequently, the leaf greenness was enhanced by 22%, which delayed the leaf senescence by 13% at physiological maturity. Besides, compared to CK, soybean plants achieved the highest values of crop growth rate in T1, which increased the total dry matter accumulation (by 6%) and its translocation to vegetative parts (by 4%) and reproductive parts (by 8%) at physiological maturity. This improved soybean growth and dry matter partitioning to reproductive parts in T1 enhanced the pod number (by 23%, from 823.8 m-2 in CK to 1012.7 m-2 in T1) and seed number (by 11%, from 1181.4 m-2 in CK to 1311.7 m-2 in T1), whereas the heavy defoliation treatments considerably decreased all measured growth and yield parameters. On average, treatment T1 increased soybean seed yield by 9% (from 2120.2 kg ha-1 in CK to 2318.2 kg ha-1 in T1), while T2 and T3 decreased soybean seed yield by 19% and 33%, respectively, compared to CK. Overall, these findings indicate that the optimum defoliation, i.e., T1 (15% defoliation), can decrease leaf redundancy and increase seed yield by reducing the adverse effects of mutual shading and increasing the dry matter translocation to reproductive parts than vegetative parts in soybean, especially under high-rainfall conditions. Future studies are needed to understand the internal signaling and the molecular mechanism controlling and regulating dry matter production and partitioning in soybean, especially from the pod initiation stage to the physiological maturity stage.

Land Use Change Influences Ecosystem Function in Headwater Streams of the Lowland Amazon Basin

Intensive agriculture alters headwater streams, but our understanding of its effects is limited in tropical regions where rates of agricultural expansion and intensification are currently greatest. Riparian forest protections are an important conservation tool, but whether they provide adequate protection of stream function in these areas of rapid tropical agricultural development has not been well studied. To address these gaps, we conducted a study in the lowland Brazilian Amazon, an area undergoing rapid cropland expansion, to assess the effects of land use change on organic matter dynamics (OM), ecosystem metabolism, and nutrient concentrations and uptake (nitrate and phosphate) in 11 first order streams draining forested (n = 4) or cropland (n = 7) watersheds with intact riparian forests. We found that streams had similar terrestrial litter inputs, but OM biomass was lower in cropland streams. Gross primary productivity was low and not different between land uses, but ecosystem respiration and net ecosystem production showed greater seasonality in cropland streams. Although we found no difference in stream concentrations of dissolved nutrients, phosphate uptake exceeded nitrate uptake in all streams and was higher in cropland than forested streams. This indicates that streams will be more retentive of phosphorus than nitrogen and that if fertilizer nitrogen reaches streams, it will be exported in stream networks. Overall, we found relatively subtle differences in stream function, indicating that riparian buffers have thus far provided protection against major functional shifts seen in other systems. However, the changes we did observe were linked to watershed scale shifts in hydrology, water temperature, and light availability resulting from watershed deforestation. This has implications for the conservation of tens of thousands of stream kilometers across the expanding Amazon cropland region.

Massive soybean expansion in South America since 2000 and implications for conservation

A prominent goal of policies mitigating climate change and biodiversity loss is to achieve zero-deforestation in the global supply chain of key commodities, such as palm oil and soybean. However, the extent and dynamics of deforestation driven by commodity expansion are largely unknown. Here we mapped annual soybean expansion in South America between 2000 and 2019 by combining satellite observations and sample field data. From 2000-2019, the area cultivated with soybean more than doubled from 26.4 Mha to 55.1 Mha. Most soybean expansion occurred on pastures originally converted from natural vegetation for cattle production. The most rapid expansion occurred in the Brazilian Amazon, where soybean area increased more than 10-fold, from 0.4 Mha to 4.6 Mha. Across the continent, 9% of forest loss was converted to soybean by 2016. Soy-driven deforestation was concentrated at the active frontiers, nearly half located in the Brazilian Cerrado. Efforts to limit future deforestation must consider how soybean expansion may drive deforestation indirectly by displacing pasture or other land uses. Holistic approaches that track land use across all commodities coupled with vegetation monitoring are required to maintain critical ecosystem services.

Worldwide Research on Land Use and Land Cover in the Amazon Region

Land cover is an important descriptor of the earth’s terrestrial surface. It is also crucial to determine the biophysical processes in global environmental change. Land-use change showcases the management of the land while revealing what motivated the alteration of the land cover. The type of land use can represent local economic and social benefits, framed towards regional sustainable development. The Amazon stands out for being the largest tropical forest globally, with the most extraordinary biodiversity, and plays an essential role in climate regulation. The present work proposes to carry out a bibliometric analysis of 1590 articles indexed in the Scopus database. It uses both Microsoft Excel and VOSviewer software for the evaluation of author keywords, authors, and countries. The method encompasses (i) search criteria, (ii) search and document compilation, (iii) software selection and data extraction, and (iv) data analysis. The results classify the main research fields into nine main topics with increasing relevance: ‘Amazon’, ‘deforestation’, ‘remote sensing’, ‘land use and land cover change’, and ‘land use’. In conclusion, the cocitation authors’ network reveals the development of such areas and the interest they present due to their worldwide importance.

Global land use changes are four times greater than previously estimated

Quantifying the dynamics of land use change is critical in tackling global societal challenges such as food security, climate change, and biodiversity loss. Here we analyse the dynamics of global land use change at an unprecedented spatial resolution by combining multiple open data streams (remote sensing, reconstructions and statistics) to create the HIstoric Land Dynamics Assessment + (HILDA +). We estimate that land use change has affected almost a third (32%) of the global land area in just six decades (1960-2019) and, thus, is around four times greater in extent than previously estimated from long-term land change assessments. We also identify geographically diverging land use change processes, with afforestation and cropland abandonment in the Global North and deforestation and agricultural expansion in the South. Here, we show that observed phases of accelerating (~1960-2005) and decelerating (2006-2019) land use change can be explained by the effects of global trade on agricultural production.

Mediterranean Landscape Re-Greening at the Expense of South American Agricultural Expansion

The stabling of livestock farming implies changes in both local ecosystems (regeneration of forest stands via reduced grazing) and those located thousands of kilometers away (deforestation to produce grain for feeding livestock). Despite their importance, these externalities are poorly known. Here we evaluated how the intensification and confinement of livestock in Spain has affected forest surface changes there and in South America, the largest provider of soybeans for animal feed to the European Union. For this purpose, we have used Spanish soybean import data from Brazil, Paraguay and Argentina and a land condition map of Spain. The area of secondary forest in Spain that has regenerated as a result of livestock stabling has been ~7000 kha for the decade 2000–2010. In the same period, 1220 kha of high value South American ecosystems (e.g., Chaco dry Forest, Amazonian rainforest or Cerrado) have been deforested. While these figures may offer a favorable interpretation of the current industrial livestock production, it is not possible to speak of compensation when comparing the destruction of well-structured ecosystems, such as primary South American forests, with the creation of secondary forest landscapes in Spain, which are also prone to wildfires. Our results highlight how evaluating land use change policies at a national or regional level is an incomplete exercise in our highly telecoupled and globalized world.

Brazil's Amazon Soy Moratorium reduced deforestation

Between 2004 and 2012, multiple policies contributed to one of the great conservation successes of the twenty-first century-an 84% decrease in the rate of Brazilian Amazon deforestation. Among the most prominent of these policies is the Amazon Soy Moratorium (ASM), an agreement by grain traders not to purchase soy grown on recently deforested land. The ASM inspired widespread adoption of similar zero-deforestation commitments, but its impact is poorly understood due to its overlap with other conservation policies. Here, we apply an econometric triple-differences model to remotely sensed deforestation data to isolate the ASM's impact within Brazil's Arc of Deforestation. We show that the ASM reduced deforestation in soy-suitable locations in the Amazon by 0.66 ± 0.32 percentage points relative to a counterfactual control, preventing 18,000 ± 9,000 km² of deforestation over its first decade (2006-2016). Although these results highlight potential benefits of private conservation policies, the ASM's success was dependent on complementarities with public property registries and deforestation monitoring.

Uncovering the spatial variability of recent deforestation drivers in the Brazilian Cerrado

In recent years, the Cerrado deforestation has increased considerably, reaching rates higher than in the Amazonian realm. Although the effects of deforestation are well known, the understanding of its drives at regional levels is incipient. Most studies consider that a driver influences deforestation likewise in all regions. However, deforestation has a strong spatial structure that can lead drivers to vary their influence on deforestation in different regions. Here, we evaluated the spatial variability in the relationship between the recent Cerrado deforestation and socioeconomic, environmental, and structural drivers at a regional scale. We used a geographically weighted regression (GWR) to assess the spatial variability of predictor variables. We identified regions that respond similarly to the drivers by grouping municipalities, considering their GWR coefficients through hierarchical clustering. The analyses that consider the spatial variability of predictors are more appropriated to assess the causes of recent deforestation. Remnant natural vegetation influenced the recent deforestation in all defined regions. Greater access to rural credit concession was the main driving force of deforestation in the northeast region defined here. Distance to roads increased deforestation in the northeast and north regions, while it inhibited deforestation in the central-east and southeast regions. Rainfall inhibited deforestation in the northeast, north, and southwest regions. Steep slope prevented deforestation mainly in the northeast, north, and southwest regions. Our results highlight that, to effectively reduce Cerrado deforestation, public policies should integrate strategies focusing not only at national and biome levels but also at the regional spatial level.

Assessing the effects of agricultural intensification on natural habitats and biodiversity in Southern Amazonia

The ongoing trend toward agricultural intensification in Southern Amazonia makes it essential to explore the future impacts of this development on the extent of natural habitats and biodiversity. This type of analysis requires information on future pathways of land-use and land-cover change (LULCC) under different socio-economic conditions and policy settings. For this purpose, the spatially explicit land-use change model LandSHIFT was applied to calculate a set of high-resolution land-use change scenarios for the Brazilian states Para and Mato Grosso. The period of the analysis were the years 2010-2030. The resulting land-use maps were combined with maps depicting vertebrate species diversity in order to examine the impact of natural habitat loss on species ranges as well as the overall LULCC-induced effect on vertebrate diversity as expressed by the Biodiversity Intactness Index (BII). The results of this study indicate a general decrease in biodiversity intactness in all investigated scenarios. However, agricultural intensification combined with diversified environmental protection policies show least impact of LULCC on vertebrate species richness and conservation of natural habitats compared to scenarios with low agricultural intensification or scenarios with less effective conservation policies.

Assessing Land Use and Land Cover Changes in the Direct Influence Zone of the Braço Norte Hydropower Complex, Brazilian Amazonia

Over the decades, hydropower complexes have been built in several hydrographic basins of Brazil including the Amazon region. Therefore, it is important to understand the effects of these constructions on the environment and local communities. This work presents a land use and land cover change temporal analysis considering a 33-year period (1985–2018) in the direct influence zone of the Braço Norte Hydropower Complex, Brazilian Amazonia, using the Collection 4.1 level 3 of the freely available MapBiomas dataset. Additionally, we have assessed the Brazilian Amazon large-scale deforestation process acting as a land use and land cover change driver in the study area. Our findings show that the most impacted land cover was forest formation (from 414 km2 to 287 km2, a reduction of 69%), which primarily shifted into pasturelands (increase of 664%, from 40 km2 to 299 km2). The construction of the hydropower complex also triggered indirect impacts such as the presence of urban areas in 2018 and the consequent increased local demand for crops. Together with the ongoing large-scale Amazonian deforestation process, the construction of the complex has intensified changes in the study area as 56.42% of the pixels were changed between 1985 and 2018. This indicates the importance of accurate economic and environmental impact studies for assessing social and environmental consequences of future construction in this unique region. Our results reveal the need for adopting special policies to minimize the impact of these constructions, for example, the creation of Protected Areas and the definition of locally-adjusted parameters for the ecological-economic zoning considering environmental and social circumstances derived from the local actors that depend on the natural environment to subsist such as indigenous peoples, riverine population, and artisanal fishermen.

Urbanization Impacts on Natural Habitat and Ecosystem Services in the Guangdong-Hong Kong-Macao “Megacity”

The population aggregation and built-up area expansion caused by urbanization can have significant impacts on the supply and distribution of crucial ecosystem services. The correlation between urbanization and ecosystem services has been well-studied, but additional research is needed to better understand the spatiotemporal interactions between ecosystem services and urbanization processes in highly urbanized areas as well as surrounding rural areas. In this paper, the relationships of urbanization with natural habitat and three key regulating ecosystem services—water retention, soil conservation, and carbon sequestration, were quantified and mapped for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a rapidly developing urban agglomeration of over 70 million people, for the period of 2000–2018. Our results showed that urbanization caused a general decline in ecosystem services, and urbanization and ecosystem services exhibited a negative spatial correlation. However, this relationship varied along urban-rural gradients and weak decoupling was the overall trend during the course of the study period, indicating a greater need for the protection and improvement of ecosystem services. Our results provide instructive insights for new urbanization planning to maintain regional ecosystem services and sustainable development in the GBA and other large, rapidly urbanized agglomerations.

Policy Responses to Direct and Underlying Drivers of Deforestation: Examining Rubber and Coffee in the Central Highlands of Vietnam

Viet Nam’s Central Highlands are a priority region for its National REDD+ Action Plan (NRAP) to reduce emissions from deforestation and forest degradation but are under strong pressures from rubber and coffee production and expansion into forests, and future climate stress. This research explores to what extent REDD+ and sectoral policy interventions have addressed both the direct and underlying drivers of deforestation and forest degradation in this region, with particular focus on the actors and scales that policy interventions must reach to affect driver pressure. National-level policy responses to driver pressures are assessed, with the results indicating poor correlations between the direct drivers and related underlying drivers. The research proposes a framework to guide the policy design and evaluation of response options to enable identification of the causal connections between direct and underlying drivers, and consider future pressures, which actors to target (or not miss) and which scales are best suited for interventions (from international to national, sub-national and local). This is highly relevant for countries pursuing forest and land use sector solutions through Nationally Determined Contributions to the Paris Agreement and REDD+.

Climate Change and Public Policies in the Brazilian Amazon State of Mato Grosso: Perceptions and Challenges

This study examines how key stakeholders in agriculture in a number of municipalities in the Brazilian Amazon state of Mato Grosso are incorporating and adapting to public policies on climate change. Fieldwork and semi-structured interviews conducted in 2014 and 2018 with key stakeholders in the region were analyzed to assess the effectiveness of public policies incorporating climate change factors. Data obtained from documents from national institutions complemented these interviews. The results show that although local government claims that its mission is economic, social and sustainable development, and although public institutions and stakeholders repeat internationally recognized protocols and agreements in their communications, in actual fact, these are not reflected by any change in institutional behavior.

Diazotrophs Show Signs of Restoration in Amazon Rain Forest Soils with Ecosystem Rehabilitation

Biological nitrogen fixation can be an important source of nitrogen in tropical forests that serve as a major CO₂ sink. Extensive deforestation of the Amazon is known to influence microbial communities and the biogeochemical cycles they mediate. However, it is unknown how diazotrophs (nitrogen-fixing microorganisms) respond to deforestation and subsequent ecosystem conversion to agriculture, as well as whether they can recover in secondary forests that are established after agriculture is abandoned. To address these knowledge gaps, we combined a spatially explicit sampling approach with high-throughput sequencing of nifH genes. The main objectives were to assess the functional distance decay relationship of the diazotrophic bacterial community in a tropical forest ecosystem and to quantify the roles of various factors that drive the observed changes in the diazotrophic community structure. We observed an increase in local diazotrophic diversity (α-diversity) with a decrease in community turnover (β-diversity), associated with a shift in diazotrophic community structure as a result of the forest-to-pasture conversion. Both diazotrophic community turnover and structure showed signs of recovery in secondary forests. Changes in the diazotrophic community were primarily driven by the change in land use rather than differences in geochemical characteristics or geographic distances. The diazotroph communities in secondary forests resembled those in primary forests, suggesting that at least partial recovery of diazotrophs is possible following agricultural abandonment.IMPORTANCE The Amazon region is a major tropical forest region that is being deforested at an alarming rate to create space for cattle ranching and agriculture. Diazotrophs (nitrogen-fixing microorganisms) play an important role in supplying soil N for plant growth in tropical forests. It is unknown how diazotrophs respond to deforestation and whether they can recover in secondary forests that establish after agriculture is abandoned. Using high-throughput sequencing of nifH genes, we characterized the response of diazotrophs' β-diversity and identified major drivers of changes in diazotrophs from forest-to-pasture and pasture-to-secondary-forest conversions. Studying the impact of land use change on diazotrophs is important for a better understanding of the impact of deforestation on tropical forest ecosystem functioning, and our results on the potential recovery of diazotrophs in secondary forests imply the possible restoration of ecosystem functions in secondary forests.

Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform

Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985–2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr−1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome.

Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

Droughts Amplify Differences Between the Energy Balance Components of Amazon Forests and Croplands

Droughts can exert a strong influence on the regional energy balance of the Amazon and Cerrado, as can the replacement of native vegetation by croplands. What remains unclear is how these two forcing factors interact and whether land cover changes fundamentally alter the sensitivity of the energy balance components to drought events. To fill this gap, we used remote sensing data to evaluate the impacts of drought on evapotranspiration (ET), land surface temperature (LST), and albedo on cultivated areas, savannas, and forests. Our results (for seasonal drought) indicate that increases in monthly dryness across Mato Grosso state (southern Amazonia and northern Cerrado) drive greater increases in LST and albedo in croplands than in forests. Furthermore, during the 2007 and 2010 droughts, croplands became hotter (0.1–0.8 °C) than savannas (0.3–0.6 °C) and forests (0.2–0.3 °C). However, forest ET was consistently higher than ET in all other land uses. This finding likely indicates that forests can access deeper soil water during droughts. Overall, our findings suggest that forest remnants can play a fundamental role in the mitigation of the negative impacts of extreme drought events, contributing to a higher ET and lower LST.

Ecological aspects of potential arbovirus vectors (Diptera: Culicidae) in an urban landscape of Southern Amazon, Brazil

Amazon has been under intense human pressure, especially in the so-called "Arc of Deforestation" in the Eastern and Southern regions. Changes in biodiversity due to landscape disturbance in municipalities at the Arc of Deforestation are likely to impact mosquito species leading to the potential for emergence of arboviruses. Our aim was to describe the composition and structure of culicids in Sinop, a municipality located in the Southern Amazon at the State of Mato Grosso, as well as their presence in urban habitats, analyzing climate variables and potential risk of arbovirus transmission. Mosquitoes were collected in four habitats located in urban environments for 12 months. We recovered 5,210 adult mosquitoes from mosquito traps. Among the 33 species identified, 19 are associated with arbovirus transmission. Higher abundance, richness, diversity, and equitability of mosquito species were observed in urban forest parks and in new neighborhoods located in the outskirts of the city, nearby forest fragments and agricultural areas. As the environment consolidates as urban, both abundance and richness decrease. Highly urbanized neighborhoods had higher dominance of Culex quinquefasciatus, a mosquito associated with different arboviruses, including West Nile and Saint Louis encephalitis virus. The medically important species Cx. declarator, Cx. (Melanoconion) ssp., Aedeomyia squamipennis and Aedes scapularis were found in the four habitats. It is possible that these mosquitoes are adapting to different habitats and as a consequence, they are reaching urbanized areas. Mosquito abundance was higher in the days of high temperature and low precipitation in urban forest parks and in highly urbanized neighborhoods. These results suggest that Sinop region is a key area for surveillance of arbovirus vectors.

Impacts of Public and Private Sector Policies on Soybean and Pasture Expansion in Mato Grosso—Brazil from 2001 to 2017

Demand for agricultural exports in Brazil has stimulated the expansion of crop production and cattle raising, which has caused environmental impacts. In response, Brazil developed public policies such as the new Forest Code (FC) and supply chain arrangements such the Soy and the Cattle Moratoriums. This paper analyzes the effectiveness of these policies, considering the trajectories of agricultural expansion in the state of Mato Grosso in three years: 2005 (pre-moratorium and before the new FC), 2010 (post-moratorium and before the new FC) and 2017 (post-moratorium and post-new FC). Our analysis uses a detailed land use change data for both the Amazon and Cerrado biomes in Mato Grosso. In all the years considered, soybean expansion occurred in consolidated production areas and by conversion of pastures. Pasture expansion is influenced by existence of pastures nearby, by areas of secondary vegetation and deforestation. Our data and models show the effectiveness of public policies and private arrangements to reduce direct conversion from forests to crop production. However, our results also provide evidence that soybean expansion has caused indirect impacts by replacing pasture areas and causing pasture expansion elsewhere. Evidence from our work indicates that Brazil needs broader-ranging land use policies than what was done in the 2010s to be able to reach the land use goals stated in its Nationally Determined Contribution (NDC) to the Paris Agreement.

What Drives Intensification of Land Use at Agricultural Frontiers in the Brazilian Amazon? Evidence from a Decision Game

Land-use change results from the decisions of diverse actors in response to economic and political contexts. Identification of underlying decision-making processes is key to understanding land-use patterns, anticipating trends, and designing effective environmental governance mechanisms. Here, we use a scenario-based decision game to examine hypothetical land-use decisions among four groups of rural producers in the municipalities of Sinop, Guarantã do Norte and Novo Progresso in the Brazilian Amazon. We simulate changes in agricultural prices, production cost, and frequency of environmental monitoring (in situ inspections) to understand how land-use decisions are made and change with shifts in economic and governance incentives. Hypothetical land-use decisions vary across scenarios, but not across study sites; environmental law enforcement influence land decisions, but not to the extent of dominating market factors and not for all types of producers. Decision games cannot substitute approaches to explain behavioral responses from observational data. However, they can provide immediate feedback on behavioral hypotheses before comprehensive observational data becomes available and support the development of models for land-use policy planning at local and regional scales.

Optical and SAR Remote Sensing Synergism for Mapping Vegetation Types in the Endangered Cerrado/Amazon Ecotone of Nova Mutum—Mato Grosso

Mapping vegetation types through remote sensing images has proved to be effective, especially in large biomes, such as the Brazilian Cerrado, which plays an important role in the context of management and conservation at the agricultural frontier of the Amazon. We tested several combinations of optical and radar images to identify the four dominant vegetation types that are prevalent in the Cerrado area (i.e., cerrado denso, cerradão, gallery forest, and secondary forest). We extracted features from both sources of data such as intensity, grey level co-occurrence matrix, coherence, and polarimetric decompositions using Sentinel 2A, Sentinel 1A, ALOS-PALSAR 2 dual/full polarimetric, and TanDEM-X images during the dry and rainy season of 2017. In order to normalize the analysis of these features, we used principal component analysis and subsequently applied the Random Forest algorithm to evaluate the classification of vegetation types. During the dry season, the overall accuracy ranged from 48 to 83%, and during the dry and rainy seasons it ranged from 41 up to 82%. The classification using Sentinel 2A images during the dry season resulted in the highest overall accuracy and kappa values, followed by the classification that used images from all sensors during the dry and rainy season. Optical images during the dry season were sufficient to map the different types of vegetation in our study area.