Fire Regime in a Peatland Restoration Area: Lesson from Central Kalimantan

Peat fires have caused carbon emissions and damage to local and regional communities in Indonesia. An effective fire prevention system is required for mitigating climate change and enabling sustainable development of peatlands. This study examined the fire regime in a peatland restoration area in Central Kalimantan in order to assist the establishment of a fire prevention system. The fire regime was analysed using spatial-temporal analysis, land cover change mapping, and logistic regression analysis. Spatial-temporal analysis was done using monthly Niño 3.4 sea surface temperature anomalies, daily rainfall, and MODIS Active Fire (MCD14DL) hotspots from 2006 to 2015. Land cover change was mapped using Landsat imagery from2014, 2015 and 2016. Logistic regression analysis was conducted to identify significant factors that increase fire risk. The temporal analysis showed that the strongest El Niño occurred in 2015, when the region experienced a 140-days drought period. The highest number of hotspots was also observed in this year, with hotspots concentrated in the latter half of drought period. Moreover, spatial analysis using Kernel Density Estimation (KDE) showed fire recur in degraded areas. The logistic regression analysis used topographic and proximity factors, land cover classes, and soil types as independent variables. It showed that fire in 2014 and 2015 was associated with several land cover classes and was related to historical fire occurrence areas based on KDE results. Several area of peatland forests burned in 2015 and occurred at the forest edge areas located near cultivated or degraded land (e.g. shrubland) and oil palm plantations. Based on the results, the fire regime in the study area is characterized by fires that occurring/recurring in relation to climatic conditions, especially drought periods, and are typically located in cultivated or degraded land cover classes. These parameters should be considered in developing a fire prevention system in the restoration area.Rezim Kebakaran Hutan dan Lahan di Area Restorasi Lahan Gambut: Studi dari Kalimantan TengahIntisariKebakaran di lahan gambut menyebabkan emisi karbon dan kerusakan sistem kehidupan masyarakat lokal dan regional. Sistem pencegahan kebakaran yang efektif diperlukan untuk mitigasi perubahan iklim serta mendorong pembangunan lahan dan hutan yang lestari di kawasan gambut. Studi ini meneliti tentang rezim kebakaran hutan dan lahan di suatu kawasan restorasi gambut di Kalimantan Tengah. Rezim kebakaran hutan dan lahan dianalisis menggunakan analisis spasial-temporal, perubahan tutupan lahan, dan regresi logistik. Analisis spasial-temporal menggunakan parameter nilai rata-rata sea surface temperature (SST) bulanan, curah hujan harian, dan hotspot dari MODIS Active Fire (MCD14DL) tahun 2006-2016. Perubahan tutupan lahan dipetakan dengan analisis citra Landsat tahun 2014, 2015 dan 2016. Regresi logistik digunakan untuk menganalisis faktor yang berpengaruh pada peningkatan resiko kebakaran. Analisis temporal terhadap nilai SST tahun 2006-2016 menunjukkan bahwa El- Niño terparah terjadi di tahun 2015 yang memiliki hari tanpa hujan selama 140 hari berturut-turut dan ditemukan titik hotspot terbanyak. Kernel Density Estimation (KDE) digunakan dalam analisis spasial dan hasilnya menunjukkan bahwa kebakaran terjadi dan dapat berulang di area terdegradasi. Regresi logistik  menggunakan parameter yang terdiri faktor topografis, kedekatan dengan sungai/kanal, tipe penutupan lahan, serta jenis tanah. Hasil analisis menunjukkan bahwa kebarakan tahun 2014 dan 2015 berhubungan dengan beberapa tipe tutupan lahan di area yang secara historis pernah terbakar berdasarkan analisis KDE, sehingga area tersebut terindikasi telah terdegradasi sebelumnya. Beberapa area hutan di lahan gambut juga mengalami kebakaran pada tahun 2015 khususnya di area tepi hutannya. Berdasarkan hasil, rezim kebakaran di area studi dapat dijelaskan bahwa kebakaran terjadi dan dapat berulang karena pengaruh iklim

Biomass Estimation Using ALOS PALSAR for Identification of Lowland Forest Transition Ecosystem in Jambi Province

The accurate information derived from high accuracy of remote sensing imagery analyses coupled with field observation data are required to develop a sound forest management. The study is mainly emphasized on assessment of the capabilities of remote sensing imageries to identify ecosystem types within the transitional  ecosystem. Since, the predominant transition ecosystems found within the study area were secondary forest, rubber jungle, rubber, oil palm plantation, and also other land cover such as mixed plantation and shrubs,  therefore,  the models developed were focused for those ecosystem types.  Prior to any further analysis, this study was initiated  to develop the biomass estimation model using 50 m resolution of ALOS PALSAR image in transition ecosystem, Jambi Province. Biomass models were developed by analyzing the relationship between  backscatter magnitude and field biomass. Backscatter magnitude from 1 polarization images, namely HH,  HV, and one additional band of  ratio of HH/HV  were analyzed simultaneously with  field biomass. The best models established are AGB = 42,069 exp (0.510 HV) and AGB = 1,610 exp (-0.02 HV²) with R² of 52.3% and 50,8%, respectively. The models are then used to map out the biomass distribution within the transition ecosystem and to identify the factors affecting the magnitude of biomass content for all transition ecosystem types

Spatial structure and dynamics of the plant communities in a pro-grading river delta : Wax Lake Delta, Atchafalaya Bay, Louisiana

River deltas are dynamic depositional environments that are controlled to varying degrees by coastal and fluvial forces. Plant communities in deltas respond to many of the same allogenic forces that shape delta geomorphology. This study examines the factors that influence plant community development, productivity, and species distributions in the Wax Lake delta, a young, actively pro-grading river delta in coastal Louisiana, USA. A species distribution map created using high-resolution 8-band WorldView-2 imagery was found to have an overall accuracy of 75 percent. Classification tree analysis suggested that most of the observed variation in plant species distributions within the delta can be explained by variables related to flooding, riverine and tidal flushing, soil development, ecological succession, and exposure. This full model explained 65 percent of the spatial variability, compared to 54 percent explained by elevation alone, indicating that elevation is the most important driver of species distributions in this deltaic system. Analysis of a time series of NDVI data derived from 94 Landsat images from 1973 to 2011 suggests that both total and mean plant community productivity within the delta has increased over time and that seasonal fluctuations occur that are related to water temperature and discharge. While significant short-term decreases in NDVI were found following five major storm events, in each case, total and mean NDVI recovered to within the 95 percent prediction interval of the long-term trend by the following growing season. Following the historic 2011 Mississippi River flood, the area of the delta increased by nearly 5 km2. Greater increases in delta area occurred at higher water levels, suggesting substantial vertical accretion across much of the subaerial delta. The plant community responded to this vertical accretion by shifting to higher elevation species across nearly 9 km2 of the delta. Overall, these results indicate that the plant community in the Wax Lake delta is largely driven by allogenic factors related to delta geomorphology and is increasing in productivity as the delta continues to accrete over time. The marshes in the delta show great resilience to storm disturbance, and a strong response to allogenic succession driven by extreme flood events

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Quantitative Analysis of Driving Factors of Grassland Degradation: A Case Study in Xilin River Basin, Inner Mongolia

Current literature suggests that grassland degradation occurs in areas with poor soil conditions or noticeable environmental changes and is often a result of overgrazing or human disturbances. However, these views are questioned in our analyses. Based on the analysis of satellite vegetation maps from 1984, 1998, and 2004 for the Xilin River Basin, Inner Mongolia, China, and binary logistic regression (BLR) analysis, we observe the following: (1) grassland degradation is positively correlated with the growth density of climax communities; (2) our findings do not support a common notion that a decrease of biological productivity is a direct indicator of grassland degradation; (3) a causal relationship between grazing intensity and grassland degradation was not found; (4) degradation severity increased steadily towards roads but showed different trends near human settlements. This study found complex relationships between vegetation degradation and various microhabitat conditions, for example, elevation, slope, aspect, and proximity to water

ESTIMATES OF FOREST CHARACTERISTICS DERIVED FROM REMOTELY SENSED IMAGERY AND FIELD SAMPLES: APPLICABLE SCALES, APPROPRIATE STUDY DESIGN, AND RELEVANCE TO FOREST MANAGEMENT

Information and knowledge about a given forested landscape drives forest management decisions. Within forest management though, information that adequately describes various characteristics of the forested environment in the spatial detail desired to make fully informed management decisions is often limited. Key metrics such as species composition, tree basal area, and tree density are typically too expensive to collect using ground-based inventory methods alone across broad extents for forest level planning (thousands of ha) at fine spatial detail that permit use at tactical spatial scales (tens of ha). However, quantifying these metrics accurately, in spatial detail, across broad landscapes is important to inform the management process. While relating remotely sensed data to classical ground-based survey data through modeling has shown promise for describing landscapes at the spatial detail need to inform planning and tactical scale projects, questions remain related to integrating both sources of data, sample design, and linking plots to remotely sensed data. This dissertation addresses critical aspects of these questions by: quantifying and mitigating the impact of co-registration errors; comparing various sample designs and estimation techniques using simulated ground-based information, remotely sensed data, and a variety of modeling techniques; developing enhanced image normalization routines; and creating an ensemble approach to estimating various forest characteristics that describe species composition, basal area, and tree density. This dissertation address knowledge gaps in the fields of forestry, remote sensing, data science, and decision science that can be used to efficiently and effectively inform the natural resource management decision-making process at fine spatial resolutions across broad extents