Remote sensing analysis of forest disturbances

The present invention provides systems and methods to automatically analyze Landsat satellite data of forests. The present invention can easily be used to monitor any type of forest disturbance such as from selective logging, agriculture, cattle ranching, natural hazards (fire, wind events, storms), etc. The present invention provides a large-scale, high-resolution, automated remote sensing analysis of such disturbances

Accelerated losses of protected forests from gold mining in the Peruvian Amazon

Gold mining in Amazonia involves forest removal, soil excavation, and the use of liquid mercury, which together pose a major threat to biodiversity, water quality, forest carbon stocks, and human health. Within the global biodiversity hotspot of Madre de Dios, Peru, gold mining has continued despite numerous 2012 government decrees and enforcement actions against it. Mining is now also thought to have entered federally protected areas, but the rates of miner encroachment are unknown. Here, we utilize high-resolution remote sensing to assess annual changes in gold mining extent from 1999 to 2016 throughout the Madre de Dios region, including the high-diversity Tambopata National Reserve and buffer zone. Regionally, gold mining-related losses of forest averaged 4437 ha yr−1. A temporary downward inflection in the annual growth rate of mining-related forest loss following 2012 government action was followed by a near doubling of the deforestation rate from mining in 2013–2014. The total estimated area of gold mining throughout the region increased about 40% between 2012 and 2016, including in the Tambopata National Reserve. Our results reveal an urgent need for more socio-environmental effort and law enforcement action to combat illegal gold mining in the Peruvian Amazon

Hyperion Studies Of Crop Stress In Mexico

Satellite-based measurements of crop stress could provide much needed information for cropland management, especially in developing countries where other precision agriculture technologies are too expensive (Pierce and Nowak 1999; Robert 2002). For example, detection of areas that are nitrogen deficient or water stressed could guide fertilizer and water management decisions for all farmers within the swath of the satellite. Several approaches have been proposed to quantify canopy nutrient or water content based on spectral reflectance, most of which involve combinations of reflectance in the form of vegetation indices. While these indices are designed to maximize sensitivity to leaf chemistry, variations in other aspects of plant canopies may significantly impact remotely sensed reflectance. These confounding factors include variations in canopy structural properties (e.g., leaf area index, leaf angle distribution) as well as the extent of canopy cover, which determines the amount of exposed bare soil within a single pixel. In order to assess the utility of spectral indices for monitoring crop stress, it is therefore not only necessary to establish relationships at the leaf level, but also to test the relative importance of variations in other canopy attributes at the spatial scale of the remote sensing measurement. In this context, the relative importance of a given attribute will depend on (1) the sensitivity of the reflectance index to variation in the attribute and (2) the degree to which the attribute varies spatially and temporally

Liana canopy cover mapped throughout a tropical forest with high-fidelity imaging spectroscopy

Increasing size and abundance of lianas relative to trees are pervasive changes in Neotropical forests that may lead to reduced forest carbon stocks. Yet the liana growth form is chronically understudied in large-scale tropical forest censuses, resulting in few data on the scale, cause, and impact of increasing lianas. Satellite and airborne remote sensing provide potential tools to map and monitor lianas at much larger spatial and rapid temporal scales than are possible with plot-based forest censuses. We combined high-resolution airborne imaging spectroscopy and a ground-based tree canopy census to investigate whether tree canopies supporting lianas could be discriminated from tree canopies with no liana coverage. Using support vector machine algorithms, we achieved accuracies of nearly 90% in discriminating the presence–absence of lianas, and low error (15.7% RMSE) when predicting liana percent canopy cover. When applied to the full image of the study site, our model had a 4.1% false-positive error rate as validated against an independent plot-level dataset of liana canopy cover. Using the derived liana cover classification map, we show that 6.1%–10.2% of the 1823 ha study site has high-to-severe (50–100%) liana canopy cover. Given that levels of liana infestation are increasing in Neotropical forests and can result in high tree mortality, the extent of high-to-severe liana canopy cover across the landscape may have broad implications for ecosystem function and forest carbon storage. The ability to accurately map landscape-scale liana infestation is crucial to quantifying their effects on forest function and uncovering the mechanisms underlying their increase

Objective indicators of pasture degradation from spectral mixture analysis of Landsat imagery

Author Posting. © American Geophysical Union, 2008. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 113 (2008): G00B03, doi:10.1029/2007JG000622.Degradation of cattle pastures is a management concern that influences future land use in Amazonia. However, “degradation” is poorly defined and has different meanings for ranchers, ecologists, and policy makers. Here we analyze pasture degradation using objective scalars of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and exposed soil (S) derived from Landsat imagery. A general, probabilistic spectral mixture model decomposed satellite spectral reflectance measurements into subpixel estimates of PV, NPV, and S covers at ranches in western and eastern Amazonia. Most pasture management units at all ranches fell along a single line of decreasing PV with increasing NPV and S, which could be considered a degradation continuum. The ranch with the highest stocking densities and most intensive management had greater NPV and S than a less intensively managed ranch. The number of liming, herbiciding, and disking treatments applied to each pasture management unit was positively correlated with NPV and negatively correlated with PV. Although these objective scalars revealed signs of degradation, intensive management kept exposed soil to <40% cover and maintained economically viable cattle production over several decades. In ranches with few management inputs, the high PV cover in young pastures declined with increasing pasture age, while NPV and S increased, even where grazing intensity was low. Both highly productive pastures and vigorous regrowth of native vegetation cause high PV values. Analysis of spectral properties holds promise for identifying areas where grazing intensity has exceeded management inputs, thus increasing coverage of senescent foliage and exposed soil.This research was supported by grant NNG06GE88A of NASA’s Terrestrial Ecology Program as part of the Large-scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project

Soil–Atmosphere Exchange of Nitrous Oxide, Nitric Oxide, Methane, and Carbon Dioxide in Logged and Undisturbed Forest in the Tapajos National Forest, Brazil

Selective logging is an extensive land use in the Brazilian Amazon region. The soil–atmosphere fluxes of nitrous oxide (N2O), nitric oxide (NO), methane (CH4), and carbon dioxide (CO2) are studied on two soil types (clay Oxisol and sandy loam Ultisol) over two years (2000–01) in both undisturbed forest and forest recently logged using reduced impact forest management in the Tapajos National Forest, near Santarem, Para, Brazil. In undisturbed forest, annual soil–atmosphere fluxes of N2O (mean ± standard error) were 7.9 ± 0.7 and 7.0 ± 0.6 ng N cm−2 h−1 for the Oxisol and 1.7 ± 0.1 and 1.6 ± 0.3 ng N cm−2 h−1 for the Ultisol for 2000 and 2001, respectively. The annual fluxes of NO from undisturbed forest soil in 2001 were 9.0 ± 2.8 ng N cm−2 h−1 for the Oxisol and 8.8 ± 5.0 ng N cm−2 h−1 for the Ultisol. Consumption of CH4 from the atmosphere dominated over production on undisturbed forest soils. Fluxes averaged −0.3 ± 0.2 and −0.1 ± 0.9 mg CH4 m−2 day−1 on the Oxisol and −1.0 ± 0.2 and −0.9 ± 0.3 mg CH4 m−2 day−1 on the Ultisol for years 2000 and 2001. For CO2 in 2001, the annual fluxes averaged 3.6 ± 0.4 μmol m−2 s−1 on the Oxisol and 4.9 ± 1.1 μmol m−2 s−1 on the Ultisol. We measured fluxes over one year each from two recently logged forests on the Oxisol in 2000 and on the Ultisol in 2001. Sampling in logged areas was stratified from greatest to least ground disturbance covering log decks, skid trails, tree-fall gaps, and forest matrix. Areas of strong soil compaction, especially the skid trails and logging decks, were prone to significantly greater emissions of N2O, NO, and especially CH4. In the case of CH4, estimated annual emissions from decks reached extremely high rates of 531 ± 419 and 98 ± 41 mg CH4 m−2 day−1, for Oxisol and Ultisol sites, respectively, comparable to wetland emissions in the region. We calculated excess fluxes from logged areas by subtraction of a background forest matrix or undisturbed forest flux and adjusted these fluxes for the proportional area of ground disturbance. Our calculations suggest that selective logging increases emissions of N2O and NO from 30% to 350% depending upon conditions. While undisturbed forest was a CH4 sink, logged forest tended to emit methane at moderate rates. Soil–atmosphere CO2 fluxes were only slightly affected by logging. The regional effects of logging cannot be simply extrapolated based upon one site. We studied sites where reduced impact harvest management was used while in typical conventional logging ground damage is twice as great. Even so, our results indicate that for N2O, NO, and CH4, logging disturbance may be as important for regional budgets of these gases as other extensive land-use changes in the Amazon such as the conversion of forest to cattle pasture

Accelerated soil carbon loss does not explain warming related increases in soil CO2 efflux

The universally observed exponential increase in soil-surface CO2 efflux (‘soil respiration’; FS) with increasing temperature has led to speculation that global warming will accelerate soil-organic-carbon (SOC) decomposition, reduce SOC storage, and drive a positive feedback to future warming. However, interpreting temperature–FS relationships, and so modelling terrestrial carbon balance in a warmer world, is complicated by the many sources of respired carbon that contribute to FS (ref. 3) and a poor understanding of how temperature influences SOC decomposition rates. Here we quantified FS, litterfall, bulk SOC and SOC fraction size and turnover, and total below-ground carbon flux (TBCF) across a highly constrained 5.2 °C mean annual temperature (MAT) gradient in tropical montane wet forest. From these, we determined that: increases in TBCF and litterfall explain >90% of the increase in FS with MAT; bulk SOC and SOC fraction size and turnover rate do not vary with MAT; and increases in TBCF and litterfall do not influence SOC storage or turnover on century to millennial timescales. This gradient study shows that for tropical montane wet forest, long-term and whole-ecosystem warming accelerates below-ground carbon processes with no apparent impact on SOC storage