Modeling of land use and land cover change dynamics for future projection of the Amazon number curve.

The hydrological parameter Curve Number (CN) was projected in the future in a 30 m spatial resolution grid for the Amazon. Through the DINAMICA EGO platform, Land Use and Land Cover (LULC) were calibrated, simulated, validated, and projected for 2049 in a five-year time frame from 2009. The reclassified LULCs of 2009, 2014, and 2019 of the MapBiomas 5.0 project were used as input to DINAMICA EGO. Calibration was prepared using the 2009 and 2014 maps and the 2014 simulated map; the validation was carried out using the 2014 map, 2019, and 2019 simulated. In the calibration, the multiple window similarity values were all above 50% for the models of each basin, except for the Tapajós which was 40% in spatial resolution of 255 m. Validation values ranged between 36% and 76% at a spatial resolution of 255 m. Concerning the future projection of CN, the average CN of the Amazon region is equal to 77. The highest values of CN were found in the southern regions of the basins of the Xingu, Tapajós, Madeira, and throughout the basins of the Araguaia and Tocantins. In this Amazon region, in 2049, the areas of high CN will increase due to forest conversion to pasture/agriculture, implying larger runoff and flooding, including the urban areas, which will also expand. These floods will be intensified concerning those that already occur in the Amazon.

Spatial near future modeling of land use and land cover changes in the temperate forests of Mexico.

The loss of temperate forests of Mexico has continued in recent decades despite wide recognition of their importance to maintaining biodiversity. This study analyzes land use/land cover change scenarios, using satellite images from the Landsat sensor. Images corresponded to the years 1990, 2005 and 2017. The scenarios were applied for the temperate forests with the aim of getting a better understanding of the patterns in land use/land cover changes. The Support Vector Machine (SVM) multispectral classification technique served to determine the land use/land cover types, which were validated through the Kappa Index. For the simulation of land use/land cover dynamics, a model developed in Dinamica-EGO was used, which uses stochastic models of Markov Chains, Cellular Automata and Weight of Evidences. For the study, a stationary, an optimistic and a pessimistic scenario were proposed. The projections based on the three scenarios were simulated for the year 2050. Five types of land use/land cover were identified and evaluated. They were primary forest, secondary forest, human settlements, areas without vegetation and water bodies. Results from the land use/land cover change analysis show a substantial gain for the secondary forest. The surface area of the primary forest was reduced from 55.8% in 1990 to 37.7% in 2017. Moreover, the three projected scenarios estimate further losses of the surface are for the primary forest, especially under the stationary and pessimistic scenarios. This highlights the importance and probably urgent implementation of conservation and protection measures to preserve these ecosystems and their services. Based on the accuracy obtained and on the models generated, results from these methodologies can serve as a decision tool to contribute to the sustainable management of the natural resources of a region.

Changes in vegetation cover of the Pantanal wetland detected by Vegetation Index: a strategy for conservation / Mudanças na cobertura da vegetação do Pantanal dectadas por Índice de Vegetação: uma estratégia de conservação

The Brazilian Pantanal wetland undergoes landscape alterations that can cause impacts on hydrological processes, changing the flood pulse. The objective of this work is to analyse the vegetation cover of the Pantanal in the period of 2000, 2008 and 2015, and to make a projection for 2030. Therefore, NDVI from the sensor MODIS was analysed and the transition matrix was calculated by the DINAMICA EGO. The methods adopted were open sources. The results were worrisome, indicating alterations of the vegetation cover of the Pantanal, with an increase of short vegetation (grasslands or pastures) in the evaluated period. The projection pointed out that in 2030 the Brazilian Pantanal wetland area will be covered by 78% of short vegetation and only 14% of dense (arboreal-shrubby) vegetation. The approach can be a useful tool for conservation of the Brazilian Pantanal wetland.

O Pantanal brasileiro sofre alterações em sua paisagem que podem provocar impactos sobre os processos hidrológicos, afetando os pulsos de inundação. O objetivo do trabalho é analisar a cobertura vegetal do Pantanal no período de 2000, 2008 e 2015 e realizar a projeção quantitativa para 2030. Portanto, foram analisados dados NDVI do sensor MODIS e a análise da matriz de transição foi calculada pelo DINAMICA EGO. Os métodos utilizados foram todos em softwares livres. Os resultados foram preocupantes, indicando alteração da cobertura vegetal do Pantanal, com o aumento da vegetação rasteira (campos ou pastagens) no período avaliado. A projeção apontou que em 2030 a área do Pantanal será coberta por 78% de vegetação rasteira e apenas 14% de vegetação densa (arbóreo-arbustiva). A abordagem apresentada pode ser uma ferramenta útil para a conservação do Pantanal.

Offsetting the impacts of mining to achieve no net loss of native vegetation.

Offsets are a novel conservation tool, yet using them to achieve no net loss of biodiversity is challenging. This is especially true when using conservation offsets (i.e., protected areas) because achieving no net loss requires avoiding equivalent loss. Our objective was to determine if offsetting the impacts of mining achieves no net loss of native vegetation in Brazil's largest iron mining region. We used a land-use change model to simulate deforestation by mining to 2020; developed a model to allocate conservation offsets to the landscape under 3 scenarios (baseline, no new offsets; current practice, like-for-like [by vegetation type] conservation offsetting near the impact site; and threat scenario, like-for-like conservation offsetting of highly threatened vegetation); and simulated nonmining deforestation to 2020 for each scenario to quantify avoided deforestation achieved with offsets. Mines cleared 3570 ha of native vegetation by 2020. Under a 1:4 offset ratio, mining companies would be required to conserve >14,200 ha of native vegetation, doubling the current extent of protected areas in the region. Allocating offsets under current practice avoided deforestation equivalent to 3% of that caused by mining, whereas allocating under the threat scenario avoided 9%. Current practice failed to achieve no net loss because offsets did not conserve threatened vegetation. Explicit allocation of offsets to threatened vegetation also failed because the most threatened vegetation was widely dispersed across the landscape, making conservation logistically difficult. To achieve no net loss with conservation offsets requires information on regional deforestation trajectories and the distribution of threatened vegetation. However, in some regions achieving no net loss through conservation may be impossible. In these cases, other offsetting activities, such as revegetation, will be required.