New Jersey.

Scale 1:500,000; 1 cm. equals 5 kilometers.

New Jersey Coastal Heritage Trail, Absecon & Cape May regions /

Shows points of interest along southern part of the N.J. Heritage Trail.

General soil map, Burlington County, New Jersey /

"June 1970."

Acidic substrata potential map : [Burlington County, New Jersey] /

"This map estimates the potential of encountering a sulfide bearing geologic substratum beneath the soil."

Acidic substrata potential map : [Camden County, New Jersey] /

"This map estimates the potential of encountering a sulfide bearing geologic substratum beneath the soil."

Soil surface textures, Cumberland County, New Jersey /

"October 1986."

Soil surface textures, Cumberland County, New Jersey /

"October 1986."

Acidic substrata potential map : [Monmouth County, New Jersey] /

"This map estimates the potential of encountering a sulfide bearing geologic substratum beneath the soil."

Acidic substrata potential map : [Middlesex County, New Jersey] /

"This map estimates the potential of encountering a sulfide bearing geologic substratum beneath the soil."

Acidic substrata potential map : [Mercer County, New Jersey] /

"This map estimates the potential of encountering a sulfide bearing geologic substratum beneath the soil."

Soil surface textures, Salem County, New Jersey /

"October 1986."

Soil surface textures, Salem County, New Jersey /

"October 1986."

New Jersey, 1797 Raster Image

This layer is a digitized geo-referenced raster image of a 1797 map of New Jersey drawn by D.F. Sotzmann. These Sotzmann maps (10 maps of New England and Mid-Atlantic states) typically portray both natural and manmade features. They are highly detailed with symbols for churches, roads, court houses, distilleries, iron works, mills, academies, county lines, town lines, and more. Relief is usually indicated by hachures and country boundaries have also been drawn. Place names are shown in both German and English and each map usually includes an index to land grants. Prime meridians used for this series are Greenwich and Washington, D.C.

Forest inventory-based large-scale forest biomass and carbon budget assessment: new enhanced methods and use of remote sensing for verification

In recent years, concern has arisen over the effects of increasing carbon dioxide (CO2) in the earth's atmosphere due to the burning of fossil fuels. One way to mitigate increase in atmospheric CO2 concentration and climate change is carbon sequestration to forest vegeta-tion through photosynthesis. Comparable regional scale estimates for the carbon balance of forests are therefore needed for scientific and political purposes. The aim of the present dissertation was to improve methods for quantifying and verifying inventory-based carbon pool estimates of the boreal forests in the mineral soils. Ongoing forest inventories provide a data based on statistically sounded sampling for estimating the level of carbon stocks and stock changes, but improved modelling tools and comparison of methods are still needed. In this dissertation, the entire inventory-based large-scale forest carbon stock assessment method was presented together with some separate methods for enhancing and comparing it. The enhancement methods presented here include ways to quantify the biomass of understorey vegetation as well as to estimate the litter production of needles and branches. In addition, the optical remote sensing method illustrated in this dis-sertation can be used to compare with independent data. The forest inventory-based large-scale carbon stock assessment method demonstrated here provided reliable carbon estimates when compared with independent data. Future ac-tivity to improve the accuracy of this method could consist of reducing the uncertainties regarding belowground biomass and litter production as well as the soil compartment. The methods developed will serve the needs for UNFCCC reporting and the reporting under the Kyoto Protocol. This method is principally intended for analysts or planners interested in quantifying carbon over extensive forest areas.

Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data

Large-scale estimates of the area of terrestrial surface waters have greatly improved over time, in particular through the development of multi-satellite methodologies, but the generally coarse spatial resolution (tens of kms) of global observations is still inadequate for many ecological applications. The goal of this study is to introduce a new, globally applicable downscaling method and to demonstrate its applicability to derive fine resolution results from coarse global inundation estimates. The downscaling procedure predicts the location of surface water cover with an inundation probability map that was generated by bagged derision trees using globally available topographic and hydrographic information from the SRTM-derived HydroSHEDS database and trained on the wetland extent of the GLC2000 global land cover map. We applied the downscaling technique to the Global Inundation Extent from Multi-Satellites (GIEMS) dataset to produce a new high-resolution inundation map at a pixel size of 15 arc-seconds, termed GIEMS-D15. GIEMS-D15 represents three states of land surface inundation extents: mean annual minimum (total area, 6.5 x 10(6) km(2)), mean annual maximum (12.1 x 10(6) km(2)), and long-term maximum (173 x 10(6) km(2)); the latter depicts the largest surface water area of any global map to date. While the accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates, overall accuracy is good yet spatially variable. A comparison against regional wetland cover maps generated by independent observations shows that the results adequately represent large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than previously available for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems. The technique of applying inundation probabilities also allows for coupling with coarse-scale hydro-climatological model simulations. (C) 2014 Elsevier Inc All rights reserved.