Quantifying Forest Spatial Pattern Trends at Multiple Extents: An Approach to Detect Significant Changes at Different Scales

Abstract

Abstract:We propose a procedure to detect significant changes in forest spatial patterns and relevant scales. Our approach consists of four sequential steps. First, based on a series of multi-temporal forest maps, a set of geographic windows of increasing extents are extracted. Second, for each extent and date, specific stochastic simulations that replicate real-world spatial pattern characteristics are run. Third, by computing pattern metrics on both simulated and real maps, their empirical distributions and confidence intervals are derived. Finally, multi-temporal scalograms are built for each metric. Based on cover maps (1954, 2011) with a resolution of 10 m we analyze forest pattern changes in a central Apennines (Italy) reserve at multiple spatial extents (128, 256 and 512 pixels). We identify three types of multi-temporal scalograms, depending on pattern metric behaviors, describing different dynamics of natural reforestation process. The statistical distribution and variability of pattern metrics at multiple extents offers a new and powerful tool to OPEN ACCESS Remote Sens. 2014, 6 9299 detect forest variations over time. Similar procedures can (i) help to identify significant changes in spatial patterns and provide the bases to relate them to landscape processes; (ii) minimize the bias when comparing pattern metrics at a single extent and (iii) be extended to other landscapes and scales.

Earth Observation for Ecosystems Monitoring in Space and Time: A Special Issue in Remote Sensing

“…Research scientists and other subject matter experts submitted innovative and challenging papers that showed advances in several topics: estimating the spatial distribution of plant species richness by Light Detection and Ranging (LiDAR) and hyperspectral data [1], assessing habitat quality of forest corridor based on NDVI [2], applying remote sensing to study (marine) coral ecosystems [3], identifying ecosystem functional types [4], distinguishing between different forest trunk size classes from remote sensing [5], detecting changes in forest patterns [6], applying light use efficiency models to estimate vegetation productivity [7], classifying grassland successional stages by airborne hyperspectral images [8], proposing monitoring programs of grasslands based on multi-temporal optical and radar satellite images [9], estimating the potential of remote sensing to capture field-based plants phenology [10].…”

“…[1,2,5,6]), also marine ecosystems were considered in the special issue as a core part of remote sensing use in ecology [3]. Very different remote sensing data, including optical and LiDAR data, were used, applying a variety of interesting models (Figure 1), from dynamic system models for phenology assessment [10] to light use efficiency models for inferring gross primary production [7] to modified random clustering to represent forest fragmentation [6]. The 50 researchers coming from nine countries (Figure 2) did extend the current knowledge on remote sensing applied to ecosystem monitoring based on previous literature which was fully brought up.…”

Measuring forest fragmentation using multitemporal remotely sensed data: three decades of change in the dry Chaco

“…In particular, the sample-based analysis of the percent cover of a given landscape class and of its edge density offers unbiased estimators of the entire landscape [Stehman et al, 2003], while the bias of the estimators of mean patch size and patch density is very small or negligible [Hassett et al, 2012]. All those parameters, which are also among the most used indicators for fragmentation analysis [Townsend et al, 2009;Moreno-Sanchez et al, 2012;Frate et al, 2014], can be easily calculated for each sampling unit using off-theshelf software, such as FRAGSTATS [McGarigal and Marks, 1995].…”

Pixel-Wise vs. Object-Based Impervious Surface Analysis from Remote Sensing: Correlations with Land Surface Temperature and Population Density

“…Few studies have explored the quantitative relationships between ISA and landscape cover features at the object level in an automatic or semiautomatic way [30,31]. Subjective delineation of spatial units also makes it difficult to consider multiple dimensions of landscape arrangement [32,33]. Therefore, object-based image segmentation were proposed to scale the landscape information in proper way, in order to exploit the flexibility of object-based spatial units in measuring the relationships between impervious surface change and land cover features [8,34,35].…”