Balancing food production with climate change mitigation and biodiversity conservation in the Brazilian Amazon.

Climate change mitigation and biodiversity conservation are two major environmental actions that need to be effectively performed this century, alongside ensuring food supply for a growing global human population. These three issues are highly interlinked through land management systems. Thus, major global food production regions located in biodiversity hotpots and with potential for carbon sequestration face trade-offs between these valuable land-based ecosystem services. The state of Mato Grosso in Brazil is one such region, where private lands that have been illegally used for agriculture could be restored to natural vegetation - with potential benefits for climate change mitigation and biodiversity conservation, although with potentially negative effects on food production. To address this challenge, in this study we used a multicriteria nexus modeling approach that considers carbon stocks, priority areas for biodiversity conservation, and the opportunity for food production, to develop scenarios of land allocation that aim to balance the benefits and drawbacks of ecosystem restoration. Results show that forcing landowners to restore their individual lands compromises the potential for a "green land market" throughout the Amazon biome in which private landowners with lower food production capacities (e.g., less connected to markets and infrastructure) would benefit from restoration programs that compensate them for the inclusion of environmental restoration among their economic activities, instead of taking large economic risks to produce more food. We additionally highlight that strategic ecosystem restoration can achieve higher gains in biodiversity and carbon with lower costs of restoration actions and with minimal impacts on agriculture. Analyses like ours demonstrate how scenarios of land allocation that simultaneously address climate mitigation and biodiversity conservation through ecosystem restoration, while also minimizing possible impacts on food production, can be sought to move the world towards a sustainable future.

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.

Transport cost to port though Brazilian federal roads network: Dataset for years 2000, 2005, 2010 and 2017.

Transport costs can play a significant role in agricultural exports and businesses profitability. It can also influence farmers' decisions regarding cropland expansion, intensification or land abandonment. Thus, transport is useful to take into account when modeling and evaluating land use and cover change related to agriculture production. The dataset described in this article represents the Infrastructure Capital in the work presented by Millington et al. (2021) [1], in which the CRAFTY-Brazil model is used to evaluate the impacts of changing global demand for agricultural products on land use and cover change. This modeling required a transport cost dataset that spanned the model calibration period, was consistent through time and covered the entire study area. The most recent federal road network (for year 2017) obtained in vector format (shapefile) was joined to road section surface status tables for past years (2000, 2005 and 2010) in order to reconstruct the historic road network. Export ports handling agricultural commodities, and their years of operation, were identified. Both datasets were used to derive the relative transport cost to the nearest port for Brazil, for years 2000, 2005, 2010 and 2017.

Land use change emission scenarios: anticipating a forest transition process in the Brazilian Amazon.

Following an intense occupation process that was initiated in the 1960s, deforestation rates in the Brazilian Amazon have decreased significantly since 2004, stabilizing around 6000 km(2) yr(-1) in the last 5 years. A convergence of conditions contributed to this, including the creation of protected areas, the use of effective monitoring systems, and credit restriction mechanisms. Nevertheless, other threats remain, including the rapidly expanding global markets for agricultural commodities, large-scale transportation and energy infrastructure projects, and weak institutions. We propose three updated qualitative and quantitative land-use scenarios for the Brazilian Amazon, including a normative 'Sustainability' scenario in which we envision major socio-economic, institutional, and environmental achievements in the region. We developed an innovative spatially explicit modelling approach capable of representing alternative pathways of the clear-cut deforestation, secondary vegetation dynamics, and the old-growth forest degradation. We use the computational models to estimate net deforestation-driven carbon emissions for the different scenarios. The region would become a sink of carbon after 2020 in a scenario of residual deforestation (~1000 km(2) yr(-1)) and a change in the current dynamics of the secondary vegetation - in a forest transition scenario. However, our results also show that the continuation of the current situation of relatively low deforestation rates and short life cycle of the secondary vegetation would maintain the region as a source of CO2 - even if a large portion of the deforested area is covered by secondary vegetation. In relation to the old-growth forest degradation process, we estimated average gross emission corresponding to 47% of the clear-cut deforestation from 2007 to 2013 (using the DEGRAD system data), although the aggregate effects of the postdisturbance regeneration can partially offset these emissions. Both processes (secondary vegetation and forest degradation) need to be better understood as they potentially will play a decisive role in the future regional carbon balance.

A social and ecological assessment of tropical land uses at multiple scales: the Sustainable Amazon Network.

Science has a critical role to play in guiding more sustainable development trajectories. Here, we present the Sustainable Amazon Network (Rede Amazônia Sustentável, RAS): a multidisciplinary research initiative involving more than 30 partner organizations working to assess both social and ecological dimensions of land-use sustainability in eastern Brazilian Amazonia. The research approach adopted by RAS offers three advantages for addressing land-use sustainability problems: (i) the collection of synchronized and co-located ecological and socioeconomic data across broad gradients of past and present human use; (ii) a nested sampling design to aid comparison of ecological and socioeconomic conditions associated with different land uses across local, landscape and regional scales; and (iii) a strong engagement with a wide variety of actors and non-research institutions. Here, we elaborate on these key features, and identify the ways in which RAS can help in highlighting those problems in most urgent need of attention, and in guiding improvements in land-use sustainability in Amazonia and elsewhere in the tropics. We also discuss some of the practical lessons, limitations and realities faced during the development of the RAS initiative so far.

Land use/cover classification in the Brazilian Amazon using satellite images.

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Farming expansion and land degradation in Western Bahia, Brazil / Expansão agrícola e degradação da terra no Oeste da Bahia, Brasil

Land degradation by soil erosion has called attention in tropical developing countries, particularly when natural vegetation is converted to farmland. Thus, the occupation of Brazilian savannas in Western Bahia is a matter of growing environmental concern. There are approximately 10 million hectares affected by this relatively recent land-use dynamics, but little is known about the temporal and spatial distribution of the process. To better understand such transformations, this paper addresses three related topics: land use/land cover (LULC) in 1985 and 2000; LULC dynamics between 1985 and 2000; and risk of land degradation by soil erosion as a function of farming expansion. The study area is located in Northeastern Brazil, between the coordinates 11º S and 46º 30' W and 14º S and 43º 30' W. All classes of natural vegetation cover decreased their areas during the period of study. Savanna (cerrado) lost 21.0 percent of its original area. Modern farming and irrigated areas increased 154.4 and 526.0 percent, respectively. Farming expansion reached 1,675,233 ha. Moderate risk of land degradation by soil erosion increased from 28.0 to 36.8 percent of the landscape mosaic between 1985 and 2000. The spatial and temporal dynamics observed reproduces development and land degradation examples of other savanna lands in Brazil. The integrity of native vegetation cover and the dissemination of soil and water conservation practices should be considered. This research contributes with an understanding of landscape transformations as a baseline for strategic environmental and land-use planning within the region.

A degradação da terra por erosão do solo tem chamado atenção em países em desenvolvimento, particularmente quando a vegetação nativa é convertida em área agrícola. Portanto, a ocupação do cerrado no Oeste da Bahia é um tema de crescente preocupação ambiental. Existem aproximadamente 10 milhões de hectares afetados por esta recente dinâmica de uso da terra, mas pouco se sabe sobre a distribuição temporal e espacial do processo. Para melhor entender estas transformações, este trabalho aborda três tópicos relacionados: uso e cobertura da terra em 1985 e 2000; dinâmica do uso e cobertura entre 1985 e 2000; e risco de degradação da terra por erosão de solo como função da expansão de áreas agrícolas. A área de estudo localiza-se no Nordeste brasileiro, entre as coordenadas 11º S e 46º 30' O e 14º S e 43º 30' O. Todas as classes de vegetação nativa diminuíram suas áreas durante o período de estudo. O cerrado perdeu 21,0 por cento de sua área original. A agricultura moderna e as áreas irrigadas aumentaram 154,4 e 526,0 por cento, respectivamente. A expansão de áreas agrícolas alcançou 1.675.233 ha. O risco moderado de degradação da terra por erosão de solo aumentou de 28,0 para 36,8 por cento do mosaico de paisagem entre 1985 e 2000. A dinâmica espacial e temporal observada reproduz exemplos de desenvolvimento e degradação da terra de outras áreas de cerrado no Brasil. A integridade da cobertura vegetal nativa e a disseminação de práticas de conservação de solo e água deveriam ser consideradas. Esta pesquisa contribui para um entendimento das transformações da paisagem como uma base para o planejamento ambiental estratégico e do uso das terras na região.

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon.

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.

Integration of Landsat TM and SPOT HRG Images for Vegetation Change Detection in the Brazilian Amazon.

Traditional change detection approaches have been proven to be difficult in detecting vegetation changes in the moist tropical regions with multitemporal images. This paper explores the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data for vegetation change detection in the Brazilian Amazon. A principal component analysis was used to integrate TM and HRG panchromatic data. Vegetation change/non-change was detected with the image differencing approach based on the TM and HRG fused image and the corresponding TM image. A rule-based approach was used to classify the TM and HRG multispectral images into thematic maps with three coarse land-cover classes: forest, non-forest vegetation, and non-vegetation lands. A hybrid approach combining image differencing and post-classification comparison was used to detect vegetation change trajectories. This research indicates promising vegetation change techniques, especially for vegetation gain and loss, even if very limited reference data are available.

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

Muitas medidas de textura têm sido desenvolvidas e utilizadas para melhorar a acurácia de classificações de cobertura das terras, mas raramente têm-se avaliado a importância dessas medidas em estimativas de biomassa. Este trabalho utilizou dados Landsat TM para explorar as relações entre texturas de imagens TM e biomassa em Rondônia, Amazônia. Foram analisadas oito medidas de textura baseadas em matrizes de co-ocorrência de tons de cinza (i.e., média, variância, homogeneidade, contraste, dissimilaridade, entropia, segundo momento e correlação), associadas com sete diferentes tamanhos de janela (5x5, 7x7, 9x9, 11x11, 15x15, 19x19 e 25x25) e cinco bandas TM (TM 2, 3, 4, 5 e 7). Índices de correlação de Pearson foram utilizados para analisar as relações entre textura e biomassa. Esta pesquisa indica que a maioria das medidas de textura são pouco correlacionadas com biomassa de vegetação secundária, mas algumas medidas de textura têm correlação significativa com a biomassa de formações florestais maduras. Ao contrário, assinaturas espectrais de bandas TM são significativamente correlacionadas com a biomassa de vegetação secundária, mas fracamente correlacionadas com a biomassa de florestas maduras. Os resultados indicam que medidas de textura são importantes em estimativas de biomassa de floresta madura, mas relativamente menos importantes para estimativas de biomassa de vegetação secundária.

Dimensões humanas do uso e cobertura das terras na Amazônia: uma contribuição do LBA / Human dimensions of land use and land cover in the Amazon: a contribution from LBA

O entendimento das mudanças em paisagens amazônicas depende de documentação das alterações na cobertura da terra. Este artigo parte de resultados do Experimento de Larga Escala de Biosfera-Atmosfera na Amazônia (LBA) relativos ao tema para focalizar um estudo transversal que analisa as dimensões sociais e biofísicas dessas transformações. As áreas de estudo representam um gradiente de fertilidade de solos e inclui distintos mosaicos de paisagens, desde o estuário Amazônico e a Região Bragantina até o nordeste de Rondônia. Areas de assentamento rural são enfatizadas, devido a sua relevância social e a seus impactos sobre a cobertura das terras em escala local e regional. Para exemplificar o potencial destes estudos, apresentamos resultados comparativos para Machadinho d'Oeste e Vale do Anari, RO. A análise multitemporal utilizou imagens do satélite Landsat e levantamentos de campo. Proprietários, seringueiros, madeireiros e outros atores locais foram entrevistados sobre seus sistemas de produção e a história de uso das terras. O cálculo de métricas espaciais embasou nossas conclusões. Os resultados indicam que o desenho do assentamento e aspectos institucionais têm um papel importante no processo de alteração da paisagem. A combinação de lotes privados com reservas comuns, manejadas por populações locais, pode produzir efeitos positivos na manutenção de maiores manchas de floresta. A metodologia utilizada oferece potenciais de integração, análise e monitoramento do uso e cobertura das terras na Amazônia, visando fornecer subsídios a políticas que valorizem as dimensões sociais e ambientais do desenvolvimento da região.