Browsing by Author "Hansen, Matthew C."
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Item A Sample-Based Forest Monitoring Strategy Using Landsat, AVHRR and MODIS Data to Estimate Gross Forest Cover Loss in Malaysia between 1990 and 2005(MDPI, 2013-04-15) Giree, Namita; Stehman, Stephen V.; Potapov, Peter; Hansen, Matthew C.Insular Southeast Asia is a hotspot of humid tropical forest cover loss. A sample-based monitoring approach quantifying forest cover loss from Landsat imagery was implemented to estimate gross forest cover loss for two eras, 1990–2000 and 2000–2005. For each time interval, a probability sample of 18.5 km × 18.5 km blocks was selected, and pairs of Landsat images acquired per sample block were interpreted to quantify forest cover area and gross forest cover loss. Stratified random sampling was implemented for 2000–2005 with MODIS-derived forest cover loss used to define the strata. A probability proportional to x (πpx) design was implemented for 1990–2000 with AVHRR-derived forest cover loss used as the x variable to increase the likelihood of including forest loss area in the sample. The estimated annual gross forest cover loss for Malaysia was 0.43 Mha/yr (SE = 0.04) during 1990–2000 and 0.64 Mha/yr (SE = 0.055) during 2000–2005. Our use of the πpx sampling design represents a first practical trial of this design for sampling satellite imagery. Although the design performed adequately in this study, a thorough comparative investigation of the πpx design relative to other sampling strategies is needed before general design recommendations can be put forth.Item Annual Carbon Emissions from Deforestation in the Amazon Basin between 2000 and 2010(PLOS (Public Library of Science), 2015-05-07) Song, Xiao-Peng; Huang, Chengquan; Saatchi, Sassan S.; Hansen, Matthew C.; Townshend, John R.Reducing emissions from deforestation and forest degradation (REDD+) is considered one of the most cost-effective strategies for mitigating climate change. However, historical deforestation and emission rates―critical inputs for setting reference emission levels for REDD+―are poorly understood. Here we use multi-source, time-series satellite data to quantify carbon emissions from deforestation in the Amazon basin on a year-to-year basis between 2000 and 2010.We first derive annual deforestation indicators by using the Moderate Resolution Imaging Spectroradiometer Vegetation Continuous Fields (MODIS VCF) product. MODIS indicators are calibrated by using a large sample of Landsat data to generate accurate deforestation rates, which are subsequently combined with a spatially explicit biomass dataset to calculate committed annual carbon emissions. Across the study area, the average deforestation and associated carbon emissions were estimated to be 1.59 ± 0.25M ha•yr−1 and 0.18 ± 0.07 Pg C•yr−1 respectively, with substantially different trends and inter-annual variability in different regions. Deforestation in the Brazilian Amazon increased between 2001 and 2004 and declined substantially afterwards, whereas deforestation in the Bolivian Amazon, the Colombian Amazon, and the Peruvian Amazon increased over the study period. The average carbon density of lost forests after 2005 was 130 Mg C•ha−1, ~11%lower than the average carbon density of remaining forests in year 2010 (144 Mg C•ha−1). Moreover, the average carbon density of cleared forests increased at a rate of 7 Mg C•ha−1•yr−1 from 2005 to 2010, suggesting that deforestation has been progressively encroaching into high-biomass lands in the Amazon basin. Spatially explicit, annual deforestation and emission estimates like the ones derived in this study are useful for setting baselines for REDD+ and other emission mitigation programs, and for evaluating the performance of such efforts.Item Evaluating Landsat and RapidEye Data for Winter Wheat Mapping and Area Estimation in Punjab, Pakistan(MDPI, 2018-03-21) Khan, Ahmad; Hansen, Matthew C.; Potapov, Peter V.; Adusei, Bernard; Pickens, Amy; Krylov, Alexander; Stehman, Stephen V.While publicly available, cost-free coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems, commercial high spatial resolution satellite data are often preferred alternative for fine scale land tenure agricultural systems such as found in Pakistan. In this article, we integrated commercial 5 m spatial resolution RapidEye and free 30 m Landsat imagery in characterizing winter wheat in Punjab province, Pakistan. Specifically, we used 5 m spatial resolution RapidEye imagery from peak of the winter wheat growing season to derive training data for the characterization of time-series Landsat data. After co-registration, each RapidEye image was classified into wheat/no wheat labels at the 5 m resolution and then aggregated as percent cover to 30 m Landsat grid cells. We produced four maps, two using RapidEye derived continuous training data (of percent wheat cover) as input to a regression tree model, and two using RapidEye derived categorical training data as input to a classification tree model. From the RapidEye-derived 30 m continuous training data, we derived Map 1 as percent wheat per pixel, and Map 2 as binary wheat/no wheat classification derived using a 50% threshold applied to Map 1. To create the categorical wheat/no wheat training data, we first converted the continuous training data to a wheat/no wheat classification, and then used these categorical RapidEye training data to produce a categorical wheat map from the Landsat data. Two methods for categorizing the training data were used. The first method used a 50% wheat/no wheat threshold to produce Map 3, and the second method used only pure wheat (≥75% cover) and no wheat (≤25% cover) training pixels to produce Map 4. The approach of Map 4 is analogous to a standard method in which whole, pure, high-confidence training pixels are delineated. We validated the wheat maps with field data collected using a stratified, two-stage cluster design. Accuracy of the maps produced from the percent cover training data (Map 1 and Map 2) was not substantially better than the accuracy of the maps produced from the categorical training data as all methods yielded similar overall accuracies (±standard error): 88% (±4%) for Map 1, 90% (±4%) for Map 2, 90% (±4%) for Map 3, and 87% (±4%) for Map 4. Because the percent cover training data did not produce significantly higher accuracies, sub-pixel training data are not required for winter wheat mapping in Punjab. Given sufficient expertise in supervised classification model calibration, freely available Landsat data are sufficient for crop mapping in the fine-scale land tenure system of Punjab. For winter wheat mapping in Punjab and other like landscapes, training data for supervised classification may be collected directly from Landsat images without the need for high resolution reference imagery.Item Land-cover and land-use change trajectory hopping facilitates estate-crop expansion into protected forests in Indonesia(Wiley, 2023-05-10) Xin, Yu; Sun, Laixiang; Hansen, Matthew C.Protected areas (PAs) have been regarded as a critical strategy to protect natural forest (NF) and biodiversity. Estate-crop expansion is an important driver of deforestation in Indonesia. Yet, little is known regarding the temporal dynamics of PA effectiveness in preventing estate-crop expansion into NF. We employ Cox proportional hazard models and their extensions to characterize the dynamics of estate-crop expansion into NF in Indonesia during 1996–2015. The results show that PA effectiveness in Sumatra decreased over time and became insignificant in 2012–2015. A multistate modeling analysis shows that hopping in land-cover and land-use change (LCLUC) trajectories with shrub and/or bare ground as intermediates has decreased PA effectiveness and facilitated the expansion. Preventing LCLUC trajectory hopping becomes crucial to biodiversity conservation because it tends to occur at lowland forest, diminishing natural habitat area and increasing NF isolation.Item Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping(MDPI, 2020-01-29) Potapov, Peter; Hansen, Matthew C.; Kommareddy, Indrani; Kommareddy, Anil; Turubanova, Svetlana; Pickens, Amy; Adusei, Bernard; Tyukavina, Alexandra; Ying, QingThe multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.Item Landsat ETM+ and SRTM Data Provide Near Real-Time Monitoring of Chimpanzee (Pan troglodytes) Habitats in Africa(MDPI, 2016-05-20) Jantz, Samuel M.; Pintea, Lilian; Nackoney, Janet; Hansen, Matthew C.All four chimpanzee sub-species populations are declining due to multiple factors including human-caused habitat loss. Effective conservation efforts are therefore needed to ensure their long-term survival. Habitat suitability models serve as useful tools for conservation planning by depicting relative environmental suitability in geographic space over time. Previous studies mapping chimpanzee habitat suitability have been limited to small regions or coarse spatial and temporal resolutions. Here, we used Random Forests regression to downscale a coarse resolution habitat suitability calibration dataset to estimate habitat suitability over the entire chimpanzee range at 30-m resolution. Our model predicted habitat suitability well with an r2 of 0.82 (±0.002) based on 50-fold cross validation where 75% of the data was used for model calibration and 25% for model testing; however, there was considerable variation in the predictive capability among the four sub-species modeled individually. We tested the influence of several variables derived from Landsat Enhanced Thematic Mapper Plus (ETM+) that included metrics of forest canopy and structure for four three-year time periods between 2000 and 2012. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. Because the models were sensitive to such temporally based predictors, our results are the first to highlight the value of integrating continuously updated variables derived from satellite remote sensing into temporally dynamic habitat suitability models to support near real-time monitoring of habitat status and decision support systems.Item Potential Transient Response of Terrestrial Vegetation and Carbon in Northern North America from Climate Change(MDPI, 2019-09-18) Flanagan, Steven A.; Hurtt, George C.; Fisk, Justin P.; Sahajpal, Ritvik; Zhao, Maosheng; Dubayah, Ralph; Hansen, Matthew C.; Sullivan, Joe H.; Collatz, G. JamesTerrestrial ecosystems and their vegetation are linked to climate. With the potential of accelerated climate change from anthropogenic forcing, there is a need to further evaluate the transient response of ecosystems, their vegetation, and their influence on the carbon balance, to this change. The equilibrium response of ecosystems to climate change has been estimated in previous studies in global domains. However, research on the transient response of terrestrial vegetation to climate change is often limited to domains at the sub-continent scale. Estimation of the transient response of vegetation requires the use of mechanistic models to predict the consequences of competition, dispersal, landscape heterogeneity, disturbance, and other factors, where it becomes computationally prohibitive at scales larger than sub-continental. Here, we used a pseudo-spatial ecosystem model with a vegetation migration sub-model that reduced computational intensity and predicted the transient response of vegetation and carbon to climate change in northern North America. The ecosystem model was first run with a current climatology at half-degree resolution for 1000 years to establish current vegetation and carbon distribution. From that distribution, climate was changed to a future climatology and the ecosystem model run for an additional 2000 simulation years. A model experimental design with different combinations of vegetation dispersal rates, dispersal modes, and disturbance rates produced 18 potential change scenarios. Results indicated that potential redistribution of terrestrial vegetation from climate change was strongly impacted by dispersal rates, moderately affected by disturbance rates, and marginally impacted by dispersal mode. For carbon, the sensitivities were opposite. A potential transient net carbon sink greater than that predicted by the equilibrium response was estimated on time scales of decades–centuries, but diminished over longer time scales. Continued research should further explore the interactions between competition, dispersal, and disturbance, particularly in regards to vegetation redistribution.Item Potential Vegetation and Carbon Redistribution in Northern North America from Climate Change(MDPI, 2016-01-06) Flanagan, Steven A.; Hurtt, George C.; Fisk, Justin P.; Sahajpal, Ritvik; Hansen, Matthew C.; Dolan, Katelyn A.; Sullivan, Joe H.; Zhao, MaoshengThere are strong relationships between climate and ecosystems. With the prospect of anthropogenic forcing accelerating climate change, there is a need to understand how terrestrial vegetation responds to this change as it influences the carbon balance. Previous studies have primarily addressed this question using empirically based models relating the observed pattern of vegetation and climate, together with scenarios of potential future climate change, to predict how vegetation may redistribute. Unlike previous studies, here we use an advanced mechanistic, individually based, ecosystem model to predict the terrestrial vegetation response from future climate change. The use of such a model opens up opportunities to test with remote sensing data, and the possibility of simulating the transient response to climate change over large domains. The model was first run with a current climatology at half-degree resolution and compared to remote sensing data on dominant plant functional types for northern North America for validation. Future climate data were then used as inputs to predict the equilibrium response of vegetation in terms of dominant plant functional type and carbon redistribution. At the domain scale, total forest cover changed by ~2% and total carbon storage increased by ~8% in response to climate change. These domain level changes were the result of much larger gross changes within the domain. Evergreen forest cover decreased 48% and deciduous forest cover increased 77%. The dominant plant functional type changed on 58% of the sites, while total carbon in deciduous vegetation increased 107% and evergreen vegetation decreased 31%. The percent of terrestrial carbon from deciduous and evergreen plant functional types changed from 27%/73% under current climate conditions, to 54%/46% under future climate conditions. These large predicted changes in vegetation and carbon in response to future climate change are comparable to previous empirically based estimates, and motivate the need for future development with this mechanistic model to estimate the transient response to future climate changes.Item Sample-Based Estimation of Tree Cover Change in Haiti Using Aerial Photography: Substantial Increase in Tree Cover between 2002 and 2010(MDPI, 2021-09-14) Rodrigues-Eklund, Gabriela; Hansen, Matthew C.; Tyukavina, Alexandra; Stehman, Stephen V.; Hubacek, Klaus; Baiocchi, GiovanniRecent studies have used high resolution imagery to estimate tree cover and changes in natural forest cover in Haiti. However, there is still no rigorous quantification of tree cover change accounting for planted or managed trees, which are very important in Haiti’s farming systems. We estimated net tree cover change, gross loss, and gross gain in Haiti between 2002 and 2010 from a stratified random sample of 400 pixels with a systematic sub-sample of 25 points. Using 30 cm and 1 m resolution images, we classified land cover at each point, with any point touching a woody plant higher than 5 m classified as tree crown. We found a net increase in tree crown cover equivalent to 5.0 ± 2.3% (95% confidence interval) of Haiti’s land area. Gross gains and losses amounted to 9.0 ± 2.1% and 4.0 ± 1.3% of the territory, respectively. These results challenge, for the first time with empirical evidence, the predominant narrative that portrays Haiti as experiencing ongoing forest or tree cover loss. The net gain in tree cover quantified here represents a 35% increase from 2002 to 2010. Further research is needed to determine the drivers of this substantial net gain in tree cover at the national scale.Item Satellite-detected gain in built-up area as a leading economic indicator(IOP Publishing, 2019-10-30) Ying, Qing; Hansen, Matthew C.; Sun, Laixiang; Wang, Lei; Steininger, MarcLeading indicators of future economic activity include measures such as new housing starts, managers purchasing index, money supply, and bond yields. Such macroeconomic and financial indicators hold predictive power in signaling recessionary periods. However, many indicators are constrained by the fact that data are often published with some delay and are subject to constant revision (Bandholz and Funke 2003, Huang et al 2018, Orphanides 2003). In this research, we propose a leading indicator derived from satellite imagery, the expansion of anthropogenic bare ground. Satellite-detected gain in built-up area, a major land cover and land use (LCLU) outcome of anthropogenic bare ground gain (ABGG), provides an inexpensive, consistent, and near-real-time indicator of global and regional macroeconomic change. Our panel data analysis across four major regions of the world from 2001 to 2012 shows that the logarithm of total ABGG, mostly owing to its major LCLU outcome, the expansion of built-up land in either year t, t −1 or t −2, significantly correlated with the year t logarithm of gross domestic product (GDP, de-trended by Hodrick–Prescott filter). Global ABGG between 2001 and 2012 averaged 7875 km2 yr−1, with a peak gain of 11 875 (± 2014 km2 at the 95% confidence interval) in 2006, prior to the 2007–2008 global financial crisis. The curve of global ABGG or its major LCLU outcome of built-up area in year t − 1 accords well with that of the de-trended logarithm of the global GDP in year t. Given the 40 year archive of free satellite data, a growing satellite constellation, advances in machine learning, and scalable methods, this study suggests that analyses of ABGG as a whole or its LCLU outcomes can provide valuable information in near-real time for socioeconomic research, development planning, and economic forecasting.Item Using Multi-Resolution Satellite Data to Quantify Land Dynamics: Applications of PlanetScope Imagery for Cropland and Tree-Cover Loss Area Estimation(MDPI, 2021-06-04) Pickering, Jeffrey; Tyukavina, Alexandra; Khan, Ahmad; Potapov, Peter; Adusei, Bernard; Hansen, Matthew C.; Lima, AndréThe Planet constellation of satellites represents a significant advance in the availability of high cadence, high spatial resolution imagery. When coupled with a targeted sampling strategy, these advances enhance land-cover and land-use monitoring capabilities. Here we present example regional and national-scale area-estimation methods as a demonstration of the integrated and efficient use of mapping and sampling using public medium-resolution (Landsat) and commercial high resolution (PlanetScope) imagery. Our proposed method is agnostic to the geographic region and type of land cover and change, which is demonstrated by applying the method across two very different geographies and thematic classes. Wheat extent is estimated in Punjab, Pakistan, for the 2018/2019 growing season, and tree-cover loss area is estimated over Peru for 2017 and 2018. We used a time series of PlanetScope imagery to classify a sample of 5 × 5 km blocks for each region and produce area estimates of 55,947 km2 (±9.0%) of wheat in Punjab and 5398 km2 (±9.1%) of tree-cover loss in Peru. We also demonstrate the use of regression estimation utilizing population information from Landsat-based maps to reduce standard errors of the sample-based estimates. Resulting regression estimates have SEs of 3.6% and 5.1% for Pakistan and Peru, respectively. The combination of daily global coverage and high spatial resolution of Planet imagery improves our ability to monitor crop phenology and capture ephemeral tree-cover loss and degradation dynamics, while Landsat-based maps provide wall-to-wall information to target the sample and increase precision of the estimates through the use of regression estimation.