Eduardo Eiji Maeda

Dynamic modeling of forest conversion: Simulation of past and future scenarios of rural activities expansion in the fringes of the Xingu National Park, Brazilian Amazon

Abstract The present work is committed to simulate the expansion of agricultural and cattle raising activities within a watershed located in the fringes of the Xingu National Park, Brazilian Amazon. A spatially explicit dynamic model of land cover and land use change was used to provide both past and future scenarios of forest conversion into such rural activities, aiming to identify the role of driving forces of change in the study area. The employed modeling platform – Dinamica EGO – consists in a cellular automata environment that embodies neighborhood-based transition algorithms and spatial feedback approaches in a stochastic multi-step simulation framework. Biophysical variables and legal restrictions drove this simulation model, and statistical validation tests were then conducted for the generated past simulations (from 2000 to 2005), by means of multiple resolution fitting methods. Based on optimal calibration of past simulations, future scenarios were conceived, so as to figure out trends and spatial patterns of forest conversion in the study area for the year 2015. In all simulated scenarios, pasturelands remained nearly stable throughout the analyzed period, while a large expansion in croplands took place. The most optimistic scenario indicates that more than 50% of the natural forest will be replaced by either cropland or pastureland by 2015. This modeling experiment revealed the suitability of the adopted model to simulate processes of forest conversion. It also indicates its possible further applicability in generating simulations of deforestation for areas with expanding rural activities in the Amazon and in tropical forests worldwide.

Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil

The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.

Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks.

The presented work describes a methodology that employs artificial neural networks (ANN) and multi-temporal imagery from the MODIS/Terra-Aqua sensors to detect areas of high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that due to characteristic land use and land cover change dynamics in the Amazon forest, forest areas likely to be burned can be separated from other land targets. A study case was carried out in three municipalities located in northern Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS imagery acquired during five different periods preceding the 2005 fire season. Selected samples were extracted from areas where forest fires were detected in 2005 and from other non-burned forest and agricultural areas. These samples were used to train, validate and test the ANN. The results achieved a mean squared error of 0.07. In addition, the model was simulated for an entire municipality and its results were compared with hotspots detected by the MODIS sensor during the year. A histogram analysis showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed from June to December 2005. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area. Hence, it offers an excellent alternative for supporting forest fire prevention policies, and in assisting the assessment of burned areas, reducing the uncertainty involved in currently used methods.

Can MODIS EVI monitor ecosystem productivity in the Amazon rainforest

The enhanced vegetation index (EVI) obtained from satellite imagery has often been used as a proxy of vegetation functioning and productivity in the Amazon rainforest. However, recent studies indicate that EVI patterns are strongly affected by satellite data artifacts. Hence, it is unclear if EVI is sensitive to subtle seasonal variations in evergreen Amazon forest productivity. This study analyzes 12 years of Moderate Resolution Imaging Spectroradiometer (MODIS) EVI in order to evaluate its response to factors driving productivity in the Amazon. We show that, after removing cloud and aerosol contamination, and correcting bidirectional reflectance distribution function effects, radiation and rainfall extremes show no influence on EVI anomalies. However, EVI seasonal patterns are still evident after accounting for Sun-sensor geometry effects. This remaining pattern cannot be explained by solar radiation or rainfall, but it is significantly correlated to gross primary production (GPP). Nevertheless, we argue that the causality between GPP and EVI should be interpreted with caution.

Disruption of hydroecological equilibrium in southwest Amazon mediated by drought

The impacts of droughts on the Amazon ecosystem have been broadly discussed in recent years, but a comprehensive understanding of the consequences is still missing. In this study, we show evidence of a fragile hydrological equilibrium in the western Amazon. While drainage systems located near the equator and the western Amazon do not show water deficit in years with average climate conditions, this equilibrium can be broken during drought events. More importantly, we show that this effect is persistent, taking years until the normal hydrological patterns are reestablished. We show clear links between persistent changes in forest canopy structure and changes in hydrological patterns, revealing physical evidence of hydrological mechanisms that may lead to permanent changes in parts of the Amazon ecosystem. If prospects of increasing drought frequency are confirmed, a change in the current hydroecological patterns in the western Amazon could take place in less than a decade.

Agricultural Expansion and Its Consequences in the Taita Hills, Kenya

Abstract The indigenous cloud forests in the Taita Hills have suffered substantial degradation for several centuries due to agricultural expansion. Additionally, climate change imposes an imminent threat for local economy and environmental sustainability. In such circumstances, elaborating tools to conciliate socioeconomic growth and natural resources conservation is an enormous challenge. This chapter describes applications of remote sensing and geographic information systems for assessing land-cover changes in the Taita Hills and its surrounding lowlands. Furthermore, it provides an overall assessment on the consequences of land-cover changes to water resources, biodiversity and livelihoods. The analyses presented in this study were undertaken at multiple spatial scales, using field data, airborne digital images and satellite imagery. Furthermore, a modelling framework was designed to delineate agricultural expansion projections and evaluate the future impacts of agriculture on soil erosion and irrigation water demand.

Climate drivers of the Amazon forest greening

Our limited understanding of the climate controls on tropical forest seasonality is one of the biggest sources of uncertainty in modeling climate change impacts on terrestrial ecosystems. Combining leaf production, litterfall and climate observations from satellite and ground data in the Amazon forest, we show that seasonal variation in leaf production is largely triggered by climate signals, specifically, insolation increase (70.4% of the total area) and precipitation increase (29.6%). Increase of insolation drives leaf growth in the absence of water limitation. For these non-water-limited forests, the simultaneous leaf flush occurs in a sufficient proportion of the trees to be observed from space. While tropical cycles are generally defined in terms of dry or wet season, we show that for a large part of Amazonia the increase in insolation triggers the visible progress of leaf growth, just like during spring in temperate forests. The dependence of leaf growth initiation on climate seasonality may result in a higher sensitivity of these ecosystems to changes in climate than previously thought.

Land Cover Characterization in West Sudanian Savannas Using Seasonal Features from Annual Landsat Time Series

With the increasing temporal resolution of medium spatial resolution data, seasonal features are becoming more readily available for land cover characterization. However, in the tropical regions, images can be severely contaminated by clouds during the rainy season and fires during the dry season, with possible effects to seasonal features. In this study, we evaluated the performance of seasonal features based on an annual Landsat time series (LTS) of 35 images for land cover characterization in West Sudanian savanna woodlands. First, the burnt areas were detected and removed. Second, the reflectance seasonality was modelled using a harmonic model, and model parameters were used as inputs for land cover classification and tree crown cover prediction using the random forest algorithm. Furthermore, to study the sensitivity of the approach to the burnt areas, we repeated the analyses without the first step. Our results showed that seasonal features improved classification accuracy significantly from 68.7% and 66.1% to 76.2%, and decreased root mean square error (RMSE) of tree crown cover predictions from 11.7% and 11.4% to 10.4%, in comparison to the dry and rainy season single date images, respectively. The burnt areas biased the seasonal parameters in near-infrared and shortwave infrared bands, and decreased the accuracy of classification and tree crown cover prediction, suggesting that burnt areas should be removed before fitting the harmonic model. We conclude that seasonal features from annual LTS improved land cover characterization performance, and the harmonic model, provided a simple method for computing annual seasonal features with burnt area removal.

Consistency of Vegetation Index Seasonality Across the Amazon Rainforest

Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.

Downscaling MODIS LST in the East African mountains using elevation gradient and land-cover information

Land-surface temperature (LST) is strongly affected by altitude and surface albedo. In mountain regions where steep slopes and heterogeneous land cover are predominant, LST can vary significantly within short distances. Although remote sensing currently provides opportunities for monitoring LST in inaccessible regions, the coarse resolution of some sensors may result in large uncertainties at sub-pixel scales. This study aimed to develop a simple methodology for downscaling 1 km Moderate Resolution Spectroradiometer (MODIS) LST pixels, by accounting for sub-pixel LST variation associated with altitude and land-cover spatial changes. The approach was tested in Mount Kilimanjaro, Tanzania, where changes in altitude and vegetation can take place over short distances. Daytime and night-time MODIS LST estimates were considered separately. A digital elevation model (DEM) and normalized difference vegetation index (NDVI), both at 250 m spatial resolution, were used to assess altitude and land-cover changes, respectively. Simple linear regressions and multivariate regressions were used to quantify the relationship between LST and the independent variables, altitude and NDVI. The results show that, in Kilimanjaro, altitude variation within the area covered by a 1 km MODIS LST pixel can be up to ±300 m. These altitude changes can cause sub-pixel variation of up to ±2.13°C for night-time and ±2.88°C for daytime LST. NDVI variation within 1 km pixels ranged between –0.2 and 0.2. For night-time measurements, altitude explained up to 97% of LST variation, while daytime LST was strongly affected by land cover. Using multivariate regressions, the combination of altitude and NDVI explained up to 94% of daytime LST variation in Kilimanjaro. Finally, the downscaling approach proposed in this study allowed an improved representation of the influence of landscape features on local-scale LST patterns.