Beyond the park and city dichotomy: Land use and land cover change in the northern coast of São Paulo (Brazil)

The natural and cultural landscapes of the Northern Coast of São Paulo State (Brazil) are threatened by increasing urban growth, as a result of inefficient land use management and fast population growth. Our work analysed land use/cover changes (LULCC) between 1985 and 2015 at 5 year intervals, to describe and understand the main processes and drivers of landscape change. LULCC were mapped using Landsat images and geographic object-based image analysis (GEOBIA), based on the Random Forests supervised algorithm. Over 30 years, we show a dichotomic trend for the two main land change trajectories: forest persistence and fast urban growth. We found only 8% of forest disturbance within the State Parks, while dense urban settlements grew 163% outside the park, mainly replacing rural uses. We estimate that all available land for human settlement may be occupied by 2030 as a result of this fast urban growth. Our study exemplifies a likely pattern of land use change for coastal regions, with fast urban growth driven by economic interests in transforming these regions into urban and touristic hubs, clashing with environmental policies for forest conservation and afforestation. The history of LULCC in the Northern Coast of São Paulo State has resulted in several land use conflicts in the present, especially when considering fast urban growth versus a very large proportion of areas where no human settlement is permitted. This complex combination of drivers has led to rural depopulation and decrease in small-scale agricultural uses, reducing the diversity and functionality of the studied landscape

Reconstrução histórica de mudanças na cobertura florestal em várzeas do Baixo Amazonas utilizando o algoritmo LandTrendr

The Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzea forest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain

A floristic survey of angiosperm species occurring at three landscapes of the Central Amazon várzea, Brazil

The Amazonian floodplains harbor highly diverse wetland forests, with angiosperms adapted to survive extreme floods and droughts. About 14% of the Amazon Basin is covered by floodplains, which are fundamental to river productivity, biogeochemical cycling and trophic flow, and have been subject to human occupation since Pre-Colombian times. The botanical knowledge about these forests is still incomplete, and current forest degradation rates are much higher than the rate of new botanical surveys. Herein we report the results of three years of botanical surveys in floodplain forests of the Central Amazon. This checklist contains 432 tree species comprising 193 genera and 57 families. The most represented families are Fabaceae, Myrtaceae, Lauraceae, Sapotaceae, Annonaceae, and Moraceae representing 53% of the identified species. This checklist also documents the occurrence of approximately 236 species that have been rarely recorded as occurring in white-water floodplain forests

Concessions in protected areas: An analysis of USA, Chilean models and the proposal for the state of Sao Paulo (Bra)

The models of concessions in Protected Areas (PAs) differ worldwide and are directly related to the history of land use, culture and environmental policies adopted by each country. The laws that regulate concessions in US and Chilean PAs were developed decades ago and are still under constant evaluation by the public authorities of both countries. The State Law No. 16.260 / 2016, which authorizes concessions in 25 Conservation Units (UC) in the state of São Paulo, is recent and, therefore, it is not possible to identify a consolidated model for concessions for use and services in UCs . The determinations related to the rights and duties of the concessionaires, the inspection of the concessioned activity, the specialization of those subject to responsibility for managing the contracts and the contract termination are absent from the São Paulo Law. The performance of government management is necessary so that concessions for uses and services do not negatively impact the protection of natural and cultural heritage present in UCs and in their surroundings. The main objective of this study was to analyze different concession models adopted in PAs, considering the main laws that govern it and analyzing the proposal for the Serra do Mar State Park-Núcleo Santa Virgínia. To this end, the legislation that authorizes as concessions in US and Chilean PAs and probable models adopted in Yellowstone National Park (USA) and Los Flamencos National Reserve (CHI) were investigated

Object‑oriented classification applied to the characterization of soil use and land cover in the Araguaia, Brazil

O objetivo deste trabalho foi utilizar a classificação orientada a objetos em imagens TM/ Landsat‑5, para caracterizar classes de uso e cobertura da terra, na região do Médio Araguaia. A cena 223/068, adquirida em 5/9/2010, foi submetida a correção radiométrica, atmosférica e geométrica, como etapas de pré‑processamento. Em seguida, foram geradas duas imagens por meio das matemáticas de bandas espectrais do índice de vegetação por diferença normalizada (NDVI) e do índice de água por diferença normalizada modificado (MNDWI), utilizados na classificação de imagens. Para a segmentação destas, utilizaram-se os parâmetros de escala 250, 200, 150, 100, 50, os algoritmos “assign class” e “nearest neighbor”, e os descritores de média, área e relação de borda. Foi empregada matriz de confusão, para avaliar a acurácia da classificação, por meio do coeficiente de exatidão global e do índice de concordância Kappa. A exatidão global para o mapeamento foi de 83,3%, com coeficiente Kappa de 0,72. A classificação foi feita quanto às fitofisionomias do Cerrado, ao uso antrópico e urbano da terra, a corpos d’água e a bancos de areia. As matemáticas de bandas espectrais utilizadas apresentam resultados promissores no delineamento das classes de cobertura da terra no Araguaia.The objective of this work was to use object‑oriented classification in TM/Landsat‑5 images to characterize land use and land cover classes in the Araguaia region. The scene 223/068, acquired on 9/5/2010, was subjected to the following pre‑processing stages: radiometric, atmospheric, and geometric corrections. Two images were generated by the mathematical spectral bands normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI), which were used in the classification process. For image segmentation, the scale parameters 250, 200, 150, 100, 50, the algorithms assign class and nearest neighbor, and the attributes of average, area, and border ratio were used. A confusion matrix was used to assess the accuracy of the classification, using the overall accuracy coefficient and the Kappa index of agreement. Overall accuracy for mapping was 83.3%, with Kappa coefficient of 0.72. The classification was done as to Cerrado physiognomies, anthropic and urban use of the land, water bodies, and sand banks. The mathematical spectral bands used are promising for delineating classes of the land cover in Araguaia

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland

Urban expansion and forest reserves: Drivers of change and persistence on the coast of São Paulo State (Brazil)

Landscapes changes are a result of a wide range of interactions between actors and driving forces (DFs). In this study, we quantify the contribution of different types of DFs to processes of land change in the Northern Coast of São Paulo State (NCSP), Brazil, an important region for tourism and the energy sector. We analysed the relationship between DFs and the processes of land change from 1985 to 2000 and from 2000 to 2015 with partial least squares path modelling. The political and technological DFs were the most important groups of drivers for explaining the observed processes, especially the most dominant ones: policies on land use and environment (political DF), distances to the main transportation infrastructure (technological DF), and the presence of steep slopes in Serra do Mar (natural DF) influenced forest persistence and were also determinants for urban settlement distribution. The State Parks and the zones for nature conservation (political DF) were important for the maintenance of forest cover and overall the importance of political DF increased after 2000. In general, the DFs in NCSP were similar to those observed in other coastal and tourist regions, but surprisingly, despite a rapid population increase, demography did not explain urban and peri-urban growth. Urban growth was happening foremost in the zones for urban development and was accompanied by increases in water provision services and waste collection, whereas peri-urban sprawl was concentrated in conservation and agricultural zones, without investments in basic services. We conclude that an increasing demand for housing must be considered in future policies in NCSP, instead of solely focussing on economic interests in tourism and the energy sectors

Accuracy and limitations for spectroscopic prediction of leaf traits in seasonally dry tropical environments

Generalized assessments of the accuracy of spectroscopic estimates of ecologically important leaf traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) are still lacking for most ecosystems, and particularly for non-forested and/or seasonally dry tropical vegetation. Here, we tested the ability of using leaf reflectance spectra to estimate LMA and LDMC and classify plant growth forms within the cerrado and campo rupestre seasonally dry non-forest vegetation types of Southeastern Brazil, filling an existing gap in published assessments of leaf optical properties and plant traits in such environments. We measured leaf reflectance spectra from 1648 individual plants comprising grasses, herbs, shrubs, and trees, developed partial least squares regression (PLSR) models linking LMA and LDMC to leaf spectra (400–2500 nm), and identified the spectral regions with the greatest discriminatory power among growth forms using Bhattacharyya distances. We accurately predicted leaf functional traits and identified different growth forms. LMA was overall more accurately predicted (RMSE = 8.58%) than LDMC (RMSE = 9.75%). Our model including all sampled plants was not biased towards any particular growth form, but growth-form specific models yielded higher accuracies and showed that leaf traits from woody plants can be more accurately estimated than for grasses and forbs, independently of the trait measured. We observed a large range of LMA values (31.80–620.81 g/m2) rarely observed in tropical or temperate forests, and demonstrated that values above 300 g/m2 could not be accurately estimated. Our results suggest that spectroscopy may have an intrinsic saturation point, and/or that PLSR, the current approach of choice for estimating traits from plant spectra, is not able to model the entire range of LMA values. This finding has very important implications to our ability to use field, airborne, and orbital spectroscopic methods to derive generalizable functional information. We thus highlight the need for increasing spectroscopic sampling and research efforts in drier non-forested environments, where environmental pressures lead to leaf adaptations and allocation strategies that are very different from forested ecosystems. Our findings also confirm that leaf reflectance spectra can provide important information regarding differences in leaf metabolism, structure, and chemical composition. Such information enabled us to accurately discriminate plant growth forms in these environments regardless of lack of variation in leaf economic traits, encouraging further adoption of remote sensing methods by ecologists and allowing a more comprehensive assessment of plant functional diversity

Fires in Amazonian Blackwater Floodplain Forests: Causes, Human Dimension, and Implications for Conservation

The Amazon basin is being increasingly affected by anthropogenic fires, however, most studies focus on the impact of fires on terrestrial upland forests and do not consider the vast, annually inundated floodplains along the large rivers. Among these, the nutrient-poor, blackwater floodplain forests (igapós) have been shown to be particularly susceptible to fires. In this study we analyzed a 35-year time series (1982/1983–2016/2017) of Landsat Thematic Mapper from the Jaú National Park (Central Amazonia) and its surroundings. Our overall objective was to identify and delineate fire scars in the igapó floodplains and relate the resulting time series of annual burned area to the presence of human populations and interannual variability of regional hydroclimatic factors. We estimated hydroclimatic parameters for the study region using ground-based instrumental data (maximum monthly temperature–T$_{max}$, precipitation–P, maximum cumulative water deficit–MCWD, baseflow index–BFI, minimum water level–WL$_{min90}$ of the major rivers) and large-scale climate anomalies (Oceanic Niño Index–ONI), considering the potential dry season of the non-flooded period of the igapó floodplains from September to February. Using a wetland mask, we identified 518,135 ha of igapó floodplains in the study region, out of which 17,524 ha (3.4%) burned within the study period, distributed across 254 fire scars. About 79% of the fires occurred close to human settlements (<10 km distance), suggesting that human activities are the main source of ignition. Over 92.4% of the burned area is associated with El Niño events. Non-linear regression models indicate highly significant relationships (p < 0.001) with hydroclimatic parameters, positive with T$_{max}$ (R$^{2}$adj. = 0.83) and the ONI (R$^{2}$adj. = 0.74) and negative with P (R$^{2}$adj. = 0.88), MCWD (R$^{2}$adj. = 0.90), WL$_{min90}$ (R$^{2}$adj. = 0.61) and BFI (R$^{2}$adj. = 0.80). Hydroclimatic conditions were of outstanding magnitude in particular during the El Niño event in 2015/2016, which was responsible for 42.8% of the total burned floodplain area. We discuss these results under a historical background of El Niño occurrences and a political, demographic, and socioeconomic panorama of the study region considering the past 400 years, suggesting that disturbance of igapós by fires is not a recent phenomenon. Concluding remarks focus on current demands to increase the conservation to prevent and mitigate the impacts of fire in this vulnerable ecosystem