58 research outputs found
Improved quantification of forest cover change and implications for the carbon cycle
Changes in forest cover significantly affect the global carbon cycle, the hydrological cycle and biodiversity richness. This dissertation explores the potential of satellite-derived land cover datasets in quantifying changes in global forest cover and carbon stock. The research involved the following three components: 1) improving forest cover characterization, 2) developing advanced methods for detecting forest cover change (FCC) and 3) estimating the amount and trend of forest carbon change.
The first component sought to improve global forest cover characterization through data fusion. Multiple global land cover maps have been generated, which collectively represent our current best knowledge of global land cover, but substantial discrepancies were found in their depiction of forest. I demonstrated that the extent and density of forest cover could be much better characterized by integrating existing datasets. However, these independent map products cannot be directly compared to quantify FCC, because post-classification change detection requires significant consistency in land cover definition, satellite data source and classification procedure. The yearly vegetation continuous field (VCF) product derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) provides a prototype that fulfills such requirement. The second component was intended to explore the features of this time series dataset in change analysis. A new algorithm called VCF-based Change Analysis was developed that can explicitly characterize the timing and intensity of FCC. The efficiency and robustness of this algorithm stem from two realistic assumptions—the spatial rarity and the temporal continuity of land cover change/modification. The developed method was applied to continental scales for mapping forest disturbance hotspots.
The third component of the research combined MODIS-based deforestation indicators, a Landsat sample and a biomass dataset to estimate annual carbon emissions from deforestation with a regional focus on the Amazon basin. I found that deforestation emissions varied considerably not only across regions but also from year to year. Moreover, deforestation has been progressively encroaching into higher biomass lands in the Amazon interior. These observed deforestation and emission dynamics are expected to provide scientific support to policies on reducing emissions from deforestation and forest degradation (REDD+). The generated panel data are also of great value for evaluating forest protection policies
Vegetation Fraction Images Derived from PROBA-V Data for Rapid Assessment of Annual Croplands in Brazil.
Abstract: This paper presents a new method for rapid assessment of the extent of annual croplands in Brazil. The proposed method applies a linear spectral mixing model (LSMM) to PROBA-V time series images to derive vegetation, soil, and shade fraction images for regional analysis. We used S10-TOC (10 days synthesis, 1 km spatial resolution, and top-of-canopy) products for Brazil and S5-TOC (five days synthesis, 100 m spatial resolution, and top-of-canopy) products for Mato Grosso State (Brazilian Legal Amazon). Using the time series of the vegetation fraction images of the whole year (2015 in this case), only one mosaic composed with maximum values of vegetation fraction was generated, allowing detecting and mapping semi-automatically the areas occupied by annual crops during the year. The results (100 m spatial resolution map) for the Mato Grosso State were compared with existing global datasets (Finer Resolution Observation and Monitoring?Global Land Cover (FROM-GLC) and Global Food Security?Support Analyses Data (GFSAD30)). Visually those maps present a good agreement, but the area estimated are not comparable since the agricultural class definition are different for those maps. In addition, we found 11.8 million ha of agricultural areas in the entire Brazilian territory. The area estimation for the Mato Grosso State was 3.4 million ha for 1 km dataset and 5.3 million ha for 100 m dataset. This difference is due to the spatial resolution of the PROBA-V datasets used. A coefficient of determination of 0.82 was found between PROBA-V 100 m and Landsat-8 OLI area estimations for the Mato Grosso State. Therefore, the proposed method is suitable for detecting and mapping annual croplands distribution operationally using PROBA-V datasets for regional analysis
Estimates of fire emissions from an active deforestation region in the southern Amazon based on satellite data and biogeochemical modelling
Tropical deforestation contributes to the build-up of atmospheric carbon dioxide in the atmosphere. Within the deforestation process, fire is frequently used to eliminate biomass in preparation for agricultural use. Quantifying these deforestation-induced fire emissions represents a challenge, and current estimates are only available at coarse spatial resolution with large uncertainty. Here we developed a biogeochemical model using remote sensing observations of plant productivity, fire activity, and deforestation rates to estimate emissions for the Brazilian state of Mato Grosso during 2001–2005. Our model of DEforestation CArbon Fluxes (DECAF) runs at 250-m spatial resolution with a monthly time step to capture spatial and temporal heterogeneity in fire dynamics in our study area within the ''arc of deforestation'', the southern and eastern fringe of the Amazon tropical forest where agricultural expansion is most concentrated. Fire emissions estimates from our modelling framework were on average 90 Tg C year<sup>&minus;1</sup>, mostly stemming from fires associated with deforestation (74%) with smaller contributions from fires from conversions of Cerrado or pastures to cropland (19%) and pasture fires (7%). In terms of carbon dynamics, about 80% of the aboveground living biomass and litter was combusted when forests were converted to pasture, and 89% when converted to cropland because of the highly mechanized nature of the deforestation process in Mato Grosso. The trajectory of land use change from forest to other land uses often takes more than one year, and part of the biomass that was not burned in the dry season following deforestation burned in consecutive years. This led to a partial decoupling of annual deforestation rates and fire emissions, and lowered interannual variability in fire emissions. Interannual variability in the region was somewhat dampened as well because annual emissions from fires following deforestation and from maintenance fires did not covary, although the effect was small due to the minor contribution of maintenance fires. Our results demonstrate how the DECAF model can be used to model deforestation fire emissions at relatively high spatial and temporal resolutions. Detailed model output is suitable for policy applications concerned with annual emissions estimates distributed among post-clearing land uses and science applications in combination with atmospheric emissions modelling to provide constrained global deforestation fire emissions estimates. DECAF currently estimates emissions from fire; future efforts can incorporate other aspects of net carbon emissions from deforestation including soil respiration and regrowth
Mapping regional land cover and land use change using MODIS time series
Coarse resolution satellite observations of the Earth provide critical data in support of land cover and land use monitoring at regional to global scales. This dissertation focuses on methodology and dataset development that exploit multi-temporal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve current information related to regional forest cover change and urban extent.
In the first element of this dissertation, I develop a novel distance metric-based change detection method to map annual forest cover change at 500m spatial resolution. Evaluations based on a global network of test sites and two regional case studies in Brazil and the United States demonstrate the efficiency and effectiveness of this methodology, where estimated changes in forest cover are comparable to reference data derived from higher spatial resolution data sources.
In the second element of this dissertation, I develop methods to estimate fractional urban cover for temperate and tropical regions of China at 250m spatial resolution by fusing MODIS data with nighttime lights using the Random Forest regression algorithm. Assessment of results for 9 cities in Eastern, Central, and Southern China show good agreement between the estimated urban percentages from MODIS and reference urban percentages derived from higher resolution Landsat data.
In the final element of this dissertation, I assess the capability of a new nighttime lights dataset from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) for urban mapping applications. This dataset provides higher spatial resolution and improved radiometric quality in nighttime lights observations relative to previous datasets. Analyses for a study area in the Yangtze River Delta in China show that this new source of data significantly improves representation of urban areas, and that fractional urban estimation based on DNB can be further improved by fusion with MODIS data.
Overall, the research in this dissertation contributes new methods and understanding for remote sensing-based change detection methodologies. The results suggest that land cover change products from coarse spatial resolution sensors such as MODIS and VIIRS can benefit from regional optimization, and that urban extent mapping from nighttime lights should exploit complementary information from conventional visible and near infrared observations
Carbon Emissions from Deforestation in the Brazilian Amazon Region
A simulation model based on satellite observations of monthly vegetation greenness from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of Brazilian Amazon and Cerrado regions over the period 2000-2002. The NASA-CASA (Carnegie Ames Stanford Approach) model estimates of annual forest production were used for the first time as the basis to generate a prediction for the standing pool of carbon in above-ground biomass (AGB; gC/sq m) for forested areas of the Brazilian Amazon region. Plot-level measurements of the residence time of carbon in wood in Amazon forest from Malhi et al. (2006) were interpolated by inverse distance weighting algorithms and used with CASA to generate a new regional map of AGB. Data from the Brazilian PRODES (Estimativa do Desflorestamento da Amazonia) project were used to map deforested areas. Results show that net primary production (NPP) sinks for carbon varied between 4.25 Pg C/yr (1 Pg=10(exp 15)g) and 4.34 Pg C for the region and were highest across the eastern and northern Amazon areas, whereas deforestation sources of CO2 flux from decomposition of residual woody debris were higher and less seasonal in the central Amazon than in the eastern and southern areas. Increased woody debris from past deforestation events was predicted to alter the net ecosystem carbon balance of the Amazon region to generate annual CO2 source fluxes at least two times higher than previously predicted by CASA modeling studies. Variations in climate, land cover, and forest burning were predicted to release carbon at rates of 0.5 to 1 Pg C/yr from the Brazilian Amazon. When direct deforestation emissions of CO2 from forest burning of between 0.2 and 0.6 Pg C/yr in the Legal Amazon are overlooked in regional budgets, the year-to-year variations in this net biome flux may appear to be large, whereas our model results implies net biome fluxes had actually been relatively consistent from year to year during the period 2000-2002. This is the first study to use MODIS data to model all carbon pools (wood, leaf, root) dynamically in simulations of Amazon forest deforestation from clearing and burning of all kinds
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
Discriminação de Classes de Cobertura Vegetal em uma Região de Transição Amazônia/Cerrado no Estado de Mato Grosso por meio de Imagens do Satélite ALOS-2/PALSAR-2.
Resumo: A região de transição entre Amazônia e Cerrado, especialmente no estado de Mato Grosso, é bastante sensÃvel em termos ambientais por causa da elevada biodiversidade e alta produção de grãos e carne bovina. O objetivo deste estudo foi discriminar classes representativas de uso e cobertura de terras encontradas na região de Sinop/Mato Grosso com base nas imagens de radar do satélite ALOS-2/PALSAR-2. As seguintes classes temáticas foram consideradas: floresta primária; floresta secundária; cultura agrÃcola; e pastagem cultivada. As imagens foram obtidas nos meses de fevereiro (estação chuvosa) e setembro (estação seca) de 2016, com resolução espacial de 6,25 metros, polarizações HH e HV e banda L (comprimento de onda de 23 cm), as quais foram processadas pelos algoritmos de classificação supervisionada Random Forest (RF) e Support Vector Machine (SVM). Amostras de treinamento e de validação (65 amostras) foram obtidas em campo e complementadas com base nos mapas de uso e cobertura de terras produzidos pelos projetos MapBiomas e TerraClass Amazônia (135 amostras). Foi possÃvel discriminar dois grupos de classes temáticas: floresta primária e floresta secundária; e cultura agrÃcola e pastagem cultivada. Apesar da acurácia global do RF ter sido superior ao do SVM, os dois classificadores mostraram desempenhos estatisticamente similares
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)
Changes in Amazon Forest Structure from Land-Use Fires: Integrating Satellite Remote Sensing and Ecosystem Modeling
Fire is the dominant method of deforestation and agricultural maintenance in Amazonia, and these land-use fires frequently escape their intended boundaries and burn into adjacent forests. Initial understory fires may increase forest flammability, thereby creating a positive fire feedback and the potential for long-term changes in Amazon forest structure. The four studies in this dissertation describe the development and integration of satellite remote sensing and ecosystem modeling approaches to characterize land-use fires and their consequences in southern Amazon forests. The dissertation contributes three new methods: use of the local frequency of satellite-based active fire detections to distinguish between deforestation and maintenance fires, use of satellite data time series to identify canopy damage from understory fires, and development of a height-structured fire sub-model in Ecosystem Demography, an advanced ecosystem model, to evaluate the impacts of a positive fire feedback on forest structure and composition. Conclusions from the dissertation demonstrate that the expansion of mechanized agricultural production in southern Amazonia increased the frequency and duration of fire use compared to less intensive methods of deforestation for pasture. Based on this increase in the frequency of land-use fires, fire emissions from current deforestation may be higher than estimated for previous decades. Canopy damage from understory fires was widespread in both dry and wet years, suggesting that drought conditions may not be necessary to burn extensive areas of southern Amazon forests. Understory fires were five times more common in previously-burned than unburned forest, providing satellite-based evidence for a positive fire feedback in southern Amazonia. The impact of this positive fire feedback on forest structure and composition was assessed using the Ecosystem Demography model. Scenarios of continued understory fires under current climate conditions show the potential to trap forests in a fire-prone structure dominated by early-successional trees, similar to secondary forests, reducing net carbon storage by 20-46% within 100 years. In summary, satellite and model-based results from the dissertation demonstrate that fire-damaged forests are an extensive and long-term component of the frontier landscape in southern Amazonia and suggest that a positive fire feedback could maintain long-term changes in forest structure and composition in the region
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