Models expand the utility of point data, which are limited because of data scatter and collection bias. If we use the point data in conjunction with remote-sensing data to construct models, we can predict probabilities of species distribution with much better resolution. Multivariate models can accommodate a wide variety of explanatory variables, including temperature, rainfall, elevation, aspect, slope, relief, geology, potential soil nutrients, and soil wetness. With such models, we can identify potential habitats for a given species. Current models, Wang pointed out, are mostly static approaches.
Quantitative landscape modeling requires that time or temporal changes be considered.
Effective dynamic mechanisms are demanded to integrate fully physical, biological, and human factors into landscape simulation. Only rarely have socioeconomic, demographic, and driving-factor data been linked with remote sensing data in simulation models. Nevertheless, these ever-changing human factors, especially suburban sprawl, are the main drivers behind changing landscapes and threats to natural ecosystems, according to Wang. Bridging the gap between conservation biology and global monitoring. These all point to ways in which we can close the gap between in situ data and remote sensing data.