Burnt area mapping in insular Southeast Asia using medium resolution satellite imagery

Burnt area mapping in humid tropical insular Southeast Asia using medium resolution (250-500m) satellite imagery is characterized by persisting cloud cover, wide range of land cover types, vast amount of wetland areas and highly varying fire regimes. The objective of this study was to deepen understanding of three major aspects affecting the implementation and limits of medium resolution burnt area mapping in insular Southeast Asia: 1) fire-induced spectral changes, 2) most suitable multitemporal compositing methods and 3) burn scars patterns and size distribution. The results revealed a high variation in fire-induced spectral changes depending on the pre-fire greenness of burnt area. It was concluded that this variation needs to be taken into account in change detection based burnt area mapping algorithms in order to maximize the potential of medium resolution satellite data. Minimum near infrared (MODIS band 2, 0.86μm) compositing method was found to be the most suitable for burnt area mapping purposes using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In general, medium resolution burnt area mapping was found to be usable in the wetlands of insular Southeast Asia, whereas in other areas the usability was seriously jeopardized by the small size of burn scars. The suitability of medium resolution data for burnt area mapping in wetlands is important since recently Southeast Asian wetlands have become a major point of interest in many fields of science due to yearly occurring wild fires that not only degrade these unique ecosystems but also create regional haze problem and release globally significant amounts of carbon into the atmosphere due to burning peat. Finally, super-resolution MODIS images were tested but the test failed to improve the detection of small scars. Therefore, super-resolution technique was not considered to be applicable to regional level burnt area mapping in insular Southeast Asia.Laaja valikoima erilaisia maankäyttöluokkia, pilvisyys ja kosteikkoalueiden suuri määrä luovat erityispiirteet paloalueiden kartoitukselle Kaakkois-Aasian saariston kostean troppisissa olosuhteissa keskiresoluutioisilla (250m-500m) satelliittikuva-aineistoilla. Tämän tutkimuksen tavoitteena oli syventää ymmärrystä keskiresoluutioisen paloaluekartoituksen toteutukseen ja rajoituksiin Kaakkois-Aasian saaristossa vaikuttavista tekijöistä. Tutkimuksen tulokset paljastivat suurta vaihtelua tulipalojen aiheuttamissa heijastussäteilyn muutoksissa riippuen palaneen alueen vehreydestä ennen tulipaloa. Johtopäätöksenä todettiin että keskiresoluutioisten satelliittikuvien koko potentiaalin hyödyntämiseksi paloalueiden kartoituksessa tämä vaihtelu tulisi ottaa huomioon paloalueiden havainnointialgoritmeissa jotka perustuvat heijastussäteilyn muutosten seurantaan. Tähän ajatukseen perustuvaa paloalueiden kartoitusta myös kokeiltiin aineistoilla jotka oli tutkimuksissa todettu parhaiten tarkoitukseen sopiviksi. Paloalueiden muoto- ja kokojakauman analyysiin sekä käytännön testeihin perustuen keskiresoluutioinen paloalueiden kartoitus todettiin käyttökelpoiseksi Kaakkois-Aasian saariston kosteikkoalueilla. Muilla alueilla sen sijaan paloalueiden pieni koko uhkasi vakavasti sen käyttökelpoisuutta. Keskiresoluutioisten satelliittikuva-aineistojen käyttökelpoisuus paloalueiden kartoitukseen kosteikkoalueilla on kuitenkin merkittävää sillä viime aikoina Kaakkois-Aasian kosteikkoalueet ovat monilla tieteenaloilla nousseet kiinnostuksen kohteeksi vuosittain esiintyvien tulipalojen takia. Vuosittaiset tulipalot eivät ainoastaan heikennä näitä ainutlaatuisia ekosysteemejä vaan lähinnä palavan turpeen johdosta myös aiheuttavat pahoja alueellisia savusumuongelmia ja vapauttavat maailmanlaajuisesti merkittäviä määriä hiilidioksidia ilmakehään. Tämän tutkimuksen tulokset osaltaan luovat pohjaa yhä tarkempien alueellisten paloalueiden kartoitusmenetelmien kehittämiselle. Näillä menetelmillä kerättävä tieto paloalueiden laajuudesta ja sijainneista antaa muiden alojen tutkijoille yhä paremmat mahdollisuudet arvioida Kaakkois-Aasian saariston kosteikkoalueiden tulipalojen paikallisia, alueellisia ja maailmanlaajuisia vaikutuksia

Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010.

Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 10(6 )km(2). The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests

Ecological impacts of deforestation and forest degradation in the peat swamp forests of northwestern Borneo

Tropical peatlands have some of the highest carbon densities of any ecosystem and are under enormous development pressure. This dissertation aimed to provide better estimates of the scales and trends of ecological impacts from tropical peatland deforestation and degradation across more than 7,000 hectares of both intact and disturbed peatlands in northwestern Borneo. We combined direct field sampling and airborne Light Detection And Ranging (LiDAR) data to empirically quantify forest structures and aboveground live biomass across a largely intact tropical peat dome. The observed biomass density of 217.7 ± 28.3 Mg C hectare-1 was very high, exceeding many other tropical rainforests. The canopy trees were ~65m in height, comprising 81% of the aboveground biomass. Stem density was observed to increase across the 4m elevational gradient from the dome margin to interior with decreasing stem height, crown area and crown roughness. We also developed and implemented a multi-temporal, Landsat resolution change detection algorithm for identify disturbance events and assessing forest trends in aseasonal tropical peatlands. The final map product achieved more than 92% user’s and producer’s accuracy, revealing that after more than 25 years of management and disturbances, only 40% of the area was intact forest. Using a chronosequence approach, with a space for time substitution, we then examined the temporal dynamics of peatlands and their recovery from disturbance. We observed widespread arrested succession in previously logged peatlands consistent with hydrological limits on regeneration and degraded peat quality following canopy removal. We showed that clear-cutting, selective logging and drainage could lead to different modes of regeneration and found that statistics of the Enhanced Vegetation Index and LiDAR height metrics could serve as indicators of harvesting intensity, impacts, and regeneration stage. Long-term, continuous monitoring of the hydrology and ecology of peatland can provide key insights regarding best management practices, restoration, and conservation priorities for this unique and rapidly disappearing ecosystem

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

A global moderate resolution dataset of gross primary production of vegetation for 2000–2016

Ye

Using New and Long-Term Multi-Scale Remotely Sensed Data to Detect Recurrent Fires and Quantify Their Relationship to Land Cover/Use in Indonesian Peatlands

Indonesia has committed to reducing its greenhouse gases emissions by 29% (potentially up to 41% with international assistance) by 2030. Achieving those targets requires many efforts but, in particular, controlling the fire problem in Indonesia’s peatlands is paramount, since it is unlikely to diminish on its own in the coming decades. This study was conducted in Sumatra and Kalimantan peatlands in Indonesia. Four MODIS-derived products (MCD45A1 collection 5.1, MCD64A1 (collection 5.1 and 6), FireCCI51) were initially assessed to explore long-term fire frequency and land use/cover change relationships. The results indicated the product(s) could only detect half of the fires accurately. A further study was conducted using additional moderate spatial resolution data to compare two years of different severity (2014 and 2015) (Landsat, Sentinel 2, Sentinel 1, VIIRS 375 m). The results showed that MODIS BA products poorly discriminated small fires and failed to detect many burned areas due to persistent interference from clouds and smoke that often worsens as fire seasons progress. Although there are unique fire detection capabilities associated with each sensor (MODIS, VIIRS, Landsat, Sentinel 2, Sentinel 1), no single sensor was ideal for accurate detection of peatland fires under all conditions. Multisensor approaches could advance biomass-burning detection in peatlands, improving the accuracy and comprehensive coverage of burned area maps, thereby enabling better estimation of associated fire emissions. Despite missing many burned areas, MODIS BA (MCD64A1 C6) provides the best available data for evaluating longer term (2001-2018) associations between the frequency of fire occurrence and land use/cover change across large areas. Results showed that Sumatra and Kalimantan have both experienced frequent fires since 2001. Although extensive burning was present across the entire landscape, burning in peatlands was ~5- times more frequent and strongly associated with changes of forest to other land use/cover classes. If fire frequencies since 2001 remain unchanged, remnant peat swamp forests of Sumatra and Kalimantan will likely disappear over the next few decades. The findings reported in this dissertation provide critical insights for Indonesian stakeholders that can help them to minimize impacts of environmental change, manage ecological restoration efforts, and improve fire monitoring systems within Indonesia

Characterizing forest disturbance dynamics in the humid tropics using optical and LIDAR remotely sensed data sets

Human-induced tropical deforestation and forest degradation are widely recognized as major environmental threats, negatively affecting tropical forest ecosystem services, such as biodiversity and climate regulation. To mitigate the effects of forest disturbance, particularly carbon emissions, national forest monitoring systems are being established throughout the tropics. Multiple good practice guidelines aimed at developing accurate, compatible and cost-effective monitoring systems have been issued by IPCC, UNFCCC, GFOI and other organizations. However, there is a lack of consensus in characterization of the baseline state of the forests and carbon stocks. This dissertation is focused on the improvement of the current methods of remotely-sensed forest area and carbon loss estimation. A sample-based estimation method employing Landsat-based forest type and change maps and GLAS Lidar-modeled carbon data was first prototyped for the Democratic Republic of the Congo (DRC), and then applied for the entire pan-tropical region. The DRC study found that Landsat-scale (30m) map-based forest loss assessments unadjusted for errors may lead to significant underestimation of forest aboveground carbon (AGC) loss in the environments with small-scale land cover change dynamics. This conclusion was supported by the pan-tropical study, which revealed that Landsat-based mapping omitted almost half (44%) of forest loss in Africa compared to the sample-based estimate (sample-based estimate exceeded map-based by 78%). Landsat performed well in Latin America and Southeast Asia (sample-based estimate exceeded map-based by 15% and 6% respectively), where forest dynamics are dominated by large-scale industrial forest clearings. The pan-tropical validation sample also allowed disaggregating forest cover and AGC loss by occurrence in natural- (primary and mature secondary forests, and natural woodlands) or human-managed (tree plantations, agroforestry systems, areas of subsistence agriculture with rapid tree cover rotation) forests. Pan-tropically, 58% of AGC loss came from natural forests, with proportion of natural AGC loss being the highest in Brazil (72%) and the lowest in the humid tropical Africa outside of the DRC (22%). The pan-tropical study employed a novel forest stratification for carbon estimation based on forest structural characteristics (canopy cover and height) and intactness, which aided in reducing standard errors of the sample-based estimate (SE of 4% for the pan-tropical gross forest loss area estimate). Such a stratification also allowed for the quantification of forest degradation by delineating intact and non-intact forest areas with different carbon content. This indirect approach to quantify forest degradation was advanced in the last research chapter by automating the process of intact (hinterland) forest mapping. Hinterland forests are defined as forest patches absent of and removed from disturbance in near-term history. Their utility in using spatial context to map structurally different (degraded and non-degraded) forests points a way forward for improved stratification of forest carbon stocks. Conclusions from the dissertation summarize strengths and challenges of sample-based area estimation in monitoring forest carbon stocks and the possible use of such estimates in the revision of spatially explicit maps by adjusting them to match the unbiased sample-based estimates. Hinterland forest maps, in addition to providing a valuable stratum for sample-based carbon monitoring, may serve as a baseline for the near real-time monitoring of remaining ecologically intact tropical forests

Distribution, range connectivity, and trends of bear populations in Southeast Asia

University of Minnesota Ph.D. dissertation. June 2017. Major: Conservation Biology. Advisor: Francesca Cuthbert. 1 computer file (PDF); ix, 123 pages.Sun bears and Asiatic black bears co-occur in Southeast Asia with wide areas of overlapping range. Both species are in decline, and are vulnerable to extinction due mainly to habitat loss and illegal hunting. Efforts to conserve bears in Southeast Asia are hampered by a lack of basic knowledge of distribution, population trends and habitat configuration. To advance the scientific understanding of sun bears and Asiatic back bears in this region I investigated fine and broad scale patterns of distribution. In Lao PDR, I gathered data on bear occurrence using bear sign transects walked in multiple forest blocks throughout the country. To model the country-wide relative abundance of bears and habitat quality, I related bear sign to environmental factors associated with bear occurrence. Within global sun bear range, I gathered camera trap records of sun bear detections from seven sun bear range countries. To generate quantitative measures of sun bear population trends, I related sun bear detection rates to tree cover and estimated related changes in country and global-level sun bear populations based on tree cover loss. To evaluate the global extent of sun bear range connectivity, I used the modelled relationship between sun bears and tree cover to create a habitat suitability index, and I identified areas of fractured range that have created unnatural subpopulations that are at risk from isolation. In Lao PDR, bears selected for areas of high elevation, rugged terrain, and areas of high tree density far from roads. My model-based estimates of sun bear global population trends predicted that over a 30-year period, sun bear populations in mainland southeast Asia have potentially declined by close to 20%, and insular sun bear populations have declined by ~50%. I identified seven potential sun bear subpopulations; two that are fully isolated with no potential for inter-subpopulation movement, and in the other five, inter and intra-subpopulation habitat fragmentation occurs in a continuum of severity. My findings advance the understanding of patterns in bear distribution and trends in southeast Asia, identify research priorities, and lay a framework for future monitoring efforts at country and region-level scales. I conclude with recommendations on how to better manage camera trap data for secondary research and sharing