Aplicación de la teledetección satelital en el seguimiento de la defoliación por insectos forestales

La defoliación por insectos es la causa de daño más común que afecta a la salud forestal de coníferas y frondosas. Varios estudios han mostrado que el actual calentamiento global puede conducir a un incremento en la frecuencia, severidad y extensión de daños producidos por plagas forestales, con consecuencias productivas y ecológicas predecibles. La teledetección de defoliaciones por insectos se basa en la integración de los datos de campo con las capacidades espectrales y espaciales de los sensores remotos, satelitales y aéreos. Las técnicas desarrolladas recientemente en este ámbito tienen un importante potencial como apoyo para los gestores forestales en el manejo sostenible de la salud forestal. En esta tesis se revisan las investigaciones a nivel mundial durante el periodo 2007-2012 para conocer el estado, tendencia y potencialidad de la teledetección en la detección, mapeo y seguimiento de la defoliación forestal causada por insectosInstituto Universitario de Investigación en Gestión Forestal Sostenibl

Master of Science

thesisPersistent drought conditions and associated vegetation mortality in the central Sierra Nevada of California were analyzed from 2013-2015 using a combination of field-derived polygons and AVIRIS hyperspectral data. Linear Discriminant Analysis (LDA) was used to classify hyperspectral data into five land cover classes based on dominant flora. LDA accuracies were compared across years in order to determine whether classification accuracy was correlated with increasing drought severity. It was determined that 2013 had the greatest accuracy and 2015 had the lowest. However, this trend was influenced by Bidirectional Reflectance Distribution Function (BRDF) effects in the densely forested landscape. Fractional cover data of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil were obtained from the US Forest Service to analyze which land cover classes and which elevation intervals experienced the greatest fractional cover change, which are both indicators of vegetation senescence and mortality. GV loss deemed the most appropriate indicator of vegetation senescence and mortality as NPV and soil appeared to be confused by the Multiple Endmember Spectral Mixture Analysis (MESMA) method used to obtain the fractional cover images. Mixed oak woodland (MO) and mixed low conifer (LC) forests experienced the greatest and second-greatest decreases in GV, respectively. Lower elevation areas (695-1369 m) generally experienced greater GV loss than higher elevation areas (2167-2779), which coincided with both MO and LC forest classes. The MO forest class, which occurs most in lower elevation areas, was comprised of dominantly drought resistant flora and experienced the greatest GV loss during the study period (16%). Conversely, the HC forest, which occurs dominantly in higher elevation areas, was comprised of dominantly non-drought-tolerant flora but experienced less GV loss (5%). This suggests that the differences in elevation and location of vegetation within the landscape played a larger role than the dominant floras' degrees of drought tolerance. Variations in seasonal senescence may have influenced the measured loss of GV for the MO and LC classes, which contained deciduous vegetation. However, overall GV loss in all classes, even those without trees, indicates that the landscape likely experienced vegetation mortality, especially at low elevations in the MO and LC classes

Estimación de biomasa aérea de eucalipto (Eucalyptus grandis) y pino (Pinus spp) en plantaciones forestales comerciales, usando imágenes satelitales Sentinel

La estimación de la biomasa aérea forestal es necesaria para diversas aplicaciones técnicas y científicas, lo que permite mejorar el manejo de los bosques y plantaciones . Dado que las mediciones locales son costosas, existe un gran interés en obtener estimaciones confiables sobre grandes áreas a partir de datos de sensores remotos. Actualmente, dichas estimaciones se obtienen con una variedad de fuentes de datos, métodos estadísticos y estándares de predicción. Los datos de percepción remota en combinación con algoritmos de aprendizaje automático basados en arboles de decisión han generado resultados favorables en la estimación de valores de biomasa aérea (AGB, siglas en inglés). En este estudio, la biomasa aérea se estimó para dos especies de árboles principales, Eucalyptus grandis (E. grandis) y Pinus spp (P. spp), de plantaciones forestales comerciales en el departamento del Cauca, Colombia. La biomasa aérea se estimó combinando los datos SAR (Radar de apertura sintética) de banda C del satélite Sentinel-1A, las imágenes de textura generadas a partir de los datos de Sentinel-1A, los índices de vegetación producidos con los datos de Sentinel-2A y datos de inventarios forestales. Se usaron regresiones paramétrica lineales y regresiones no paramétricas con Random Forest para establecer una relación entre los valores medidos en campo y los parámetros de percepción remota. El uso de un modelo de Random Forest en combinación de índices de vegetación con la retrodispersión de Radar de Apertura Sintética (SAR, siglas en inglés ) como variables predictoras mostró el mejor resultado para el bosque de E. grandis, con un coeficiente de valor de determinación de 0,273 y un valor RMSE de 346,62 t.ha-1. En P. spp, el mejor resultado se pudo encontrar en la misma combinación (R2 = 0,617 y EMC = 9.025 t.ha-1). Este estudio muestra que los datos satelitales Sentinel tienen la capacidad de estimar AGB en plantaciones forestales comerciales y que el algoritmo de aprendizaje automático Random Forest puede ser muy útil para hacerlo.Abstract: The estimation of the forest aboveground biomass (AGB) is necessary for diverse technical and scientific applications, which allows to improve the management of forests and plantations. Since local measurements are expensive, there is necessary to get reliable estimates over large areas from remote sensing data. Currently, these estimations are obtained with a variety of data sources, statistical methods and prediction standards. Remote sensing data in combination with machine learning algorithms based on decision trees have generated favorable results in the estimation of aboveground biomass values. In this study, aboveground biomass was estimated for two main tree species, Eucalyptus grandis (E. grandis) and Pinus spp (P. spp), from commercial forest plantations in Cauca, Colombia. AGB was estimated by combining C-band SAR data from Sentinel-1A satellite, texture images generated from Sentinel-1A data, vegetation indices produced with Sentinel-2A data, and forest inventory data. Linear parametric regressions and nonparametric regressions as Random Forest were used to establish a relationship between the values measured in field and the parameters of remote sensing. The use of a Random Forest model in combination of vegetation indices with SAR data as predictor variables showed the best result for the E. grandis forest, with a coefficient of determination value of 0.273 and an RMSE value of 346, 62 t.ha-1. In P. spp, the best result could be found in the same combination (R2 = 0.617 and RMSE = 9.025 t.ha-1). This study shows that Sentinel satellite data have the ability to estimate AGB in commercial forest plantations and that the Random Forest machine learning algorithm can be very useful to do so.Maestrí

The Changing Matrix: Reforestation and Connectivity in a Tropical Habitat Corridor

In the last two decades, export-oriented crops and timber and fruit plantations have joined small-scale cultivation and pasture as important causes of tropical deforestation. Widespread conversion of tropical forest to agriculture threatens to isolate protected areas, which has led to efforts to maintain functional connectivity in landscapes between protected areas. Relatively few "landscape conservation" efforts have been assessed for their effect on deforestation, but advances in remote sensing now permit detailed monitoring of tropical land uses over time, including mapping of tree crops and plantations. This dissertation evaluates the long-term impact of forest conservation and reforestation policies on tropical forests in a habitat corridor. The following chapters test the capability of remote sensing to monitor tropical conservation efforts and assess whether landscape conservation policies can maintain forest cover and connectivity in the face of rapid agricultural expansion. Costa Rica has one of the most comprehensive landscape conservation policies in the tropics: a 1996 Forest Law banned deforestation and expanded payments for environmental services (PES) to protect forests and plant trees, prioritizing designated habitat corridors between protected areas. The long-term effect of the program on land-use transitions is not well known. To take advantage of this regional policy experiment, I used a time-series of five moderate-resolution Landsat images to track land-use change from 1986 to 2011in the oldest habitat corridor, the San Juan-La Selva Biological Corridor (SJLSBC). Forest conservation policies were associated with a 40% decline in deforestation after 1996 despite a doubling in the area of cropland in the last decade. The proportion of cropland derived from mature forest dropped from 16.4% to 1.9% after 1996, while one fifth of pasture expansion continued to be derived from mature forest. These results suggest that forest conservation policies can successfully lower deforestation, and that they can be more effective with large export producers than small-scale cattle producers. Tree plantations are an important component of Costa Rican PES, but knowledge of their distribution and contribution to connectivity in the corridor region is poor. After reviewing the remote sensing literature, I employed a novel integration of hyperspectral images and a Landsat time-series to create the first regional map of tropical tree plantation species. Including multitemporal data significantly improved overall hyperspectral map accuracy to 91%; the six tree plantation species were classified with 83% mean producer's accuracy. Non-native species made up 89% of tree plantations, and they were cleared more rapidly than native tree plantations and secondary forests. I combined existing land cover maps, field behavioral experiments, and a graph connectivity model to estimate whether landscape conservation policies increased connectivity for understory insectivorous birds, a representative forest-dependent group. The field playback experiments indicated both native and exotic tree plantations with a dense shrubby understory were acceptable dispersal habitat for all species, and that birds traveled readily near secondary forest edges but rarely into forested pasture. Graph model parameters were informed by these results. For all of these bird species, functional connectivity declined by 14-21% with only a 4.9% decline in forest area over time, implying that conservation policies have not caused a net increase in functional connectivity in the SJLSBC region. Despite making up 2% of the region, tree plantations had little effect on regional connectivity because of their placement in the landscape; we demonstrate that spatially-targeted reforestation of 0.1% of the region could increase connectivity by 1.8%. Collectively, the results presented in these chapters underline the potential and limitations of landscape conservation policies and corridor plans in the tropics; combining regulations and PES can lower deforestation over the medium-term, but increased enforcement, improved monitoring with remote sensing, and targeted conservation effort is needed to combat illegal deforestation and restore functional connectivity. Given numerous new tropical corridor and PES programs and the qualified successes of landscape conservation policies in Costa Rica and other tropical countries, our approach to the analysis can be applied to monitor and evaluate connectivity across the tropics

Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat 5-TM and EO-1 Hyperion data

Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill.) plantation in southern Australia. This technique combines an iterative mixture analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral mixture analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-spectral (R2no age correction = 0.55 vs R2age correction = 0.73 for Landsat) and hyperspectral (R2no age correction = 0.12 vs R2age correction = 0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for analysis of multi-spectral datasets such as MODIS and SPOT-VEGETATION