Abstract:Mangrove forests play an important role in providing ecological and socioeconomic services for human society. Coastal development, which converts mangrove forests to other land uses, has often ignored the services that mangrove may provide, leading to irreversible environmental degradation. Monitoring the spatiotemporal distribution of mangrove forests is thus critical for natural resources management of mangrove ecosystems. This study investigates spatiotemporal changes in Honduran mangrove forests using Landsat imagery during the periods 1985-1996, 1996-2002, and 2002-2013. The future trend of mangrove forest changes was projected by a Markov chain model to support decision-making for coastal management. The remote sensing data were OPEN ACCESS Remote Sens. 2013, 5 6409 processed through three main steps: (1) data pre-processing to correct geometric errors between the Landsat imageries and to perform reflectance normalization; (2) image classification with the unsupervised Otsu's method and change detection; and (3) mangrove change projection using a Markov chain model. Validation of the unsupervised Otsu's method was made by comparing the classification results with the ground reference data in 2002, which yielded satisfactory agreement with an overall accuracy of 91.1% and Kappa coefficient of 0.82. When examining mangrove changes from 1985 to 2013, approximately 11.9% of the mangrove forests were transformed to other land uses, especially shrimp farming, while little effort (3.9%) was applied for mangrove rehabilitation during this 28-year period. Changes in the extent of mangrove forests were further projected until 2020, indicating that the area of mangrove forests could be continuously reduced by 1,200 ha from 2013 (approximately 36,700 ha) to 2020 (approximately 35,500 ha). Institutional interventions should be taken for sustainable management of mangrove ecosystems in this coastal region.
Forests in Honduras are endangered as a result of the relentless occurrence of wildfires during the dry season, and their frequency and area burned have been gradually increasing, a pattern attributable to the numerous ignition sources. For this reason, there is a substantial need to identify the major drivers of wildfires and map the regions where they are most likely to occur. In this study, we integrated the wildfire occurrences throughout the 2010-2015 period with a series of variables using the random forest algorithm. We included variables related to human activities such as the continuous distances to infrastructure and settlements. Other variables included are satellite observations that reflect the seasonal vegetation change, climatic conditions over the country, and topographical variables. The analysis of the explanatory variables revealed that the dry fuel conditions and low precipitation combined with the proximity to non-paved and paved roads were the major drivers of wildfires in the region. The estimated area with high and very high wildfire susceptibility was 15% of the country, located mainly in the central and eastern regions. The proposed national-scale wildfire susceptibility map can lead to enhanced preventive measures to minimize risk and the impacts caused by wildfires.
Drought is the most pressing problem facing farmers in Central America, and information on drought is thus crucial for agronomic planners to minimize impacts on crop production and food supply. This study assessed the cultivated areas affected by droughts using the Moderate Resolution Imaging Spectroradiometer (MODIS) data during 2001-2014, processed using a simple vegetation health index (VHI). The results, verified with the Advanced Microwave Scanning Radiometer 2 (AMSR2) precipitation data and TVDI (temperature vegetation dryness index), indicated that the correlation coefficients (r) between the VHI and AMSR2 precipitation data for 2013 and 2014 were 0.81 and 0.78, respectively, and the values between VHI and TVDI were -0.68 and -0.61, respectively. The largest area of severe drought was especially observed for the 2014 primera season (April-August) over the last 14 years. The drought mapping results were aggregated with the cultivated areas for crop monitoring purposes.
Soil moisture is a critical element in the hydrological cycle, which is intimately tied to agriculture production, ecosystem integrity, and hydrological cycle. Point measurements of soil moisture samples are laborious, costly, and inefficient. Remote sensing technologies are capable of conducting soil moisture mapping at the regional scale. The advanced microwave scanning radiometer on earth observing system (AMSR-E) provides global surface soil moisture (SSM) products with the spatial resolution of 25 km which is not sufficient enough to meet the demand for various local-scale applications. This study refines AMSR-E SSM data with normalized multiband drought index (NMDI) derived from the moderate resolution imaging spectroradiometer (MODIS) data to provide fused SSM product with finer spatial resolution that can be up to 1 km. Practical implementation of this data fusion method was carried out in Central America Isthmus region to generate the SSM maps with the spatial resolution of 1 km during the dry seasons in 2010 and 2011 for various agricultural applications. The calibration and validation of the SSM maps based on the fused images of AMSR-E and MODIS yielded satisfactory agreement with in situ ground truth data pattern wise.Index Terms-Advanced microwave scanning radiometer on earth observing system (AMSR-E), leaf area index (LAI), moderate resolution imaging spectroradiometer (MODIS), normalized multiband drought index (NMDI), surface soil moisture (SSM).
The inherent effects of global sea surface temperature (SST) anomalies on hydrological cycle and vegetation cover complicate the structure of tropical climate at the regional scale. Assessing hydrological processes related to climate forcing is important in Central America because it is surrounded by both the Pacific and Atlantic oceans and two continental landmasses. In this study, the use of high‐resolution remote sensing imagery in wavelet analysis helps identify nonstationary characteristics of hydrological and ecological responses. The wavelet‐based empirical orthogonal function (WEOF) further reflects the nonlinear relationship between the Atlantic and Pacific SST and the greenness of a pristine forested site in Panama, La Amistad International Park. Integrated WEOF and descriptive statistics for data analysis reveal a higher temporal variability in terrestrial precipitation relative to in situ land surface temperature and its probable effects on the presence of dry periods. Such teleconnection signals of SST were identified as a driving force of decline in tropical forest greenness during dry periods. The results of our remote sensing‐based wavelet analysis showed intra‐annual high‐frequency and biennial to triennial low‐frequency signals between enhanced vegetation index/precipitation datasets and SST indices in both Atlantic and Pacific oceans. A spatiotemporal priority search further confirmed the importance of the effects of the El Niño–Southern Oscillation (ENSO) over terrestrial responses in the selected study site. Coincidence of the effect of ENSO teleconnection patterns on precipitation and vegetation suggests possible impacts of El Niño‐associated droughts in Central America, accompanied by reduced rainfall, especially during the first months of rainy season (June, July, and August), and decline in vegetation cover during the dry season (March and April). Copyright © 2014 John Wiley & Sons, Ltd.
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