Mapping Deforestation and Afro-Caribbean Drum-Making Traditions

My research project tracks the effects of deforestation on African diaspora drum-making traditions in the Caribbean. I focus on the islands of Cuba, Puerto Rico, and Hispaniola (Haiti/Dominican Republic). Forest resource scarcity on these islands creates new challenges for the continued practice of centuries-old traditions from the African continent. Using the ESRI Story Maps platform, I will combine geospatial data on drummakers’ source materials with prose, oral history excerpts, photos, and audio-visual recordings. This multimodal approach contributes to recent scholarly work on the cultural impacts of climate change. The project is a continuation of my ongoing research on folkloric music in the Dominican Republic, and it forms the basis of my MLS digital humanities capstone project

INVESTIGATION OF DEFORESTATION USING MULTI-SENSOR SATELLITE TIME SERIES DATA IN NORTH KOREA

Department of Urban and Environmental Engineering(Environmental Science and Engineering)North Korea is very vulnerable to natural disasters such as floods and landslides due to institutional, technological, and other various reasons. Recently, the damage has been more severe and vulnerability is also increased because of continued deforestation. However, due to political constraints, such disasters and forest degradation have not been properly monitored. Therefore, using remote sensing based satellite imagery for forest related research of North Korea is regarded as currently the only and most effective method. Especially, machine learning has been widely used in various classification studies as a useful technique for classification and analysis using satellite images. The aim of this study was to improve the accuracy of forest cover classification in the North Korea, which cannot be accessed by using random forest model. Indeed, another goal of this study was to analyze the change pattern of denuded forest land in various ways. The study area is Musan-gun, which is known to have abundant forests in North Korea, with mountainous areas accounting for more than 90%. However, the area has experienced serious environmental problems due to the recent rapid deforestation. For example, experts say that the damage caused by floods in September 2016 has become more serious because denuded forest land has increased sharply in there and such pattern appeared even in the high altitude areas. And this led the mountain could not function properly in the flood event. This study was carried out by selecting two study periods, the base year and the test year. To understand the pattern of change in the denuded forest land, the time difference between the two periods was set at about 10 years. For the base year, Landsat 5 imageries were applied, and Landsat 8 and RapidEye imageries were applied in the test year. Then the random forest machine learning was carried out using randomly extracted sample points from the study area and various input variables derived from the used satellite imageries. Finally, the land cover classification map for each period was generated through this random forest model. In addition, the distribution of forest changing area to cropland, grassland, and bare-soil were estimated to the denuded forest land. According to the study results, this method showed high accuracy in forest classification, also the method has been effective in analyzing the change detection of denuded forest land in North Korea for about 10 years.ope

Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

Using sentinel-1 time series for monitoring deforestation in regions with high precipitation rate - Study case: Chocó-Colombia

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesDespite nowadays there are many optical sensors out there, meteorological conditions in some places on the Earth makes very difficult to have access to images without clouds. Some of those places have unique ecosystems and landscape with natural forest that should be taken care of. SAR images has proven its capabilities for monitoring deforestation since the first sensors were deployed. Sentinel-1 allows to have free access to SAR data with high temporal resolution. Therefore, this study explores the use of SAR data for monitoring deforestation in places where the precipitation rate is too high. A time-series approach is used as framework to detect forest disturbances; the work tests if performing a combination of the Sentinel-1 bands through a modified version from the RFDI gets better results than the original bands; two methods for detecting changes along the time focus on deforestation are compared. The results show that VH band is the best input with similar overall accuracy with the two methods, around 80%, the mRFDI showed acceptable results but it does not prove any improvement on the deforestation events detected. It was concluded that with a workflow optimization, it can be used to overcome the optical images problem to monitor deforestation events

Data Preprocessing in Multi-Temporal Remote Sensing Data for Deforestation Analysis

In recent years, the contemporary data mining community has developed a plethora of algorithms and methods used for different tasks in knowledge discovery within large databases. Furthermore, algorithms become more complex and hybrid as algorithms combining several approaches are suggested, the task of implementing such algorithms from scratch becomes increasingly time consuming. Spatial data sets often contain large amounts of data arranged in multiple layers. These data may contain errors and may not be collected at a common set of coordinates. Therefore, various data pre-processing steps are often necessary to prepare data for further usage. It is important to understand the quality and characteristics of the chosen data. Careful selection, preprocessing, and transformation of the data are needed to ensure meaningful analysis and results

Evaluating multiple causes of persistent low microwave backscatter from Amazon forests after the 2005 drought

Amazonia has experienced large-scale regional droughts that affect forest productivity and biomass stocks. Space-borne remote sensing provides basin-wide data on impacts of meteorological anomalies, an important complement to relatively limited ground observations across the Amazon’s vast and remote humid tropical forests. Morning overpass QuikScat Ku-band microwave backscatter from the forest canopy was anomalously low during the 2005 drought, relative to the full instrument record of 1999–2009, and low morning backscatter persisted for 2006–2009, after which the instrument failed. The persistent low backscatter has been suggested to be indicative of increased forest vulnerability to future drought. To better ascribe the cause of the low post-drought backscatter, we analyzed multiyear, gridded remote sensing data sets of precipitation, land surface temperature, forest cover and forest cover loss, and microwave backscatter over the 2005 drought region in the southwestern Amazon Basin (4°-12°S, 66°-76°W) and in adjacent 8°x10° regions to the north and east. We found moderate to weak correlations with the spatial distribution of persistent low backscatter for variables related to three groups of forest impacts: the 2005 drought itself, loss of forest cover, and warmer and drier dry seasons in the post-drought vs. the pre-drought years. However, these variables explained only about one quarter of the variability in depressed backscatter across the southwestern drought region. Our findings indicate that drought impact is a complex phenomenon and that better understanding can only come from more extensive ground data and/or analysis of frequent, spatially-comprehensive, high-resolution data or imagery before and after droughts

Integration of vegetation inventory data and thematic mapper image for Amazonian successional and mature forest classification.

Successional and mature forest classification is often difficult in moist tropical regions. This paper explores vegetation stand structures of successional and mature forests and their spectral characteristics. Canonical discriminant analysis (CDA) was used to identify important stand parameters for secondary succession and mature forest classification. Correlation coefficient was used to analyze different stand parameter relationships and associated TM spectral signatures. Transformed divergence was used to analyze the separability of succession stages and mature forest based on the resultant images from CDA and principal component analysis (PCA), respectively. This study indicates that five vegetation categories, i.e., initial succession, intermediate succession, advanced succession, small biomass mature forest, and large biomass mature forest, can be istinguished based on vegetation stand features using field measurements, but some of them are difficult to be classified using TM data. Tree diameter at breast height, tree height, aboveground biomass, and ratio of tree biomass to total aboveground biomass are the best stand parameters distinguishing vegetation classes. Bands TM 4 and TM 5 are best for distinguishing vegetation classes. The transformation using CDA improved separability of vegetation classes, but not using PCA. Two successional stages and one mature forest class are suitable in this study area

Change detection of deforestation in the Brazilian Amazon using landsat data and convolutional neural networks

Mapping deforestation is an essential step in the process of managing tropical rainforests. It lets us understand and monitor both legal and illegal deforestation and its implications, which include the effect deforestation may have on climate change through greenhouse gas emissions. Given that there is ample room for improvements when it comes to mapping deforestation using satellite imagery, in this study, we aimed to test and evaluate the use of algorithms belonging to the growing field of deep learning (DL), particularly convolutional neural networks (CNNs), to this end. Although studies have been using DL algorithms for a variety of remote sensing tasks for the past few years, they are still relatively unexplored for deforestation mapping. We attempted to map the deforestation between images approximately one year apart, specifically between 2017 and 2018 and between 2018 and 2019. Three CNN architectures that are available in the literature—SharpMask, U-Net, and ResUnet—were used to classify the change between years and were then compared to two classic machine learning (ML) algorithms—random forest (RF) and multilayer perceptron (MLP)—as points of reference. After validation, we found that the DL models were better in most performance metrics including the Kappa index, F1 score, and mean intersection over union (mIoU) measure, while the ResUnet model achieved the best overall results with a value of 0.94 in all three measures in both time sequences. Visually, the DL models also provided classifications with better defined deforestation patches and did not need any sort of post-processing to remove noise, unlike the ML models, which needed some noise removal to improve results